bluuebunny/crossref_metadata_2025 huggingface dataset:
A prediction model of rubber content in the dried root of Taraxacum kok-saghyz Rodin based on near-infrared spectroscopy
Authors: Chen Runfeng , Yan Qingqing , Tuoheti Tuhanguli , Xu Lin , Gao Qiang , Zhang Yan , Ren Hailong , Zheng Lipeng , Wang Feng , Liu Ya
Published: May 2024

Background

Taraxacum kok-saghyz Rodin (TKS) is a highly potential source of natural rubber (NR) due to its wide range of suitable planting areas, strong adaptability, and suitability for mechanized planting and harvesting. However, current methods for detecting NR content are relatively cumbersome, necessitating the development of a rapid detection model. This study used near-infrared spectroscopy technology to establish a rapid detection model for NR content in TKS root segments and powder samples. The K445 strain at different growth stages within a year and 129 TKS samples hybridized with dandelion were used to obtain their near-infrared spectral data. The rubber content in the root of the samples was detected using the alkaline boiling method. The Monte Carlo sampling method (MCS) was used to filter abnormal data from the root segments of TKS and powder samples, respectively. The SPXY algorithm was used to divide the training set and validation set in a 3:1 ratio. The original spectrum was preprocessed using moving window smoothing (MWS), standard normalized variate (SNV), multiplicative scatter correction (MSC), and first derivative (FD) algorithms. The competitive adaptive reweighted sampling (CARS) algorithm and the corresponding chemical characteristic bands of NR were used to screen the bands. Partial least squares (PLS), random forest (RF), Lightweight gradient augmentation machine (LightGBM), and convolutional neural network (CNN) algorithms were employed to establish a model using the optimal spectral processing method for three different bands: full band, CARS algorithm, and chemical characteristic bands corresponding to NR. The model with the best predictive performance for high rubber content intervals (rubber content > 15%) was identified.

Result

The results indicated that the optimal rubber content prediction models for TKS root segments and powder samples were MWS–FD CASR–RF and MWS–FD chemical characteristic band RF, respectively. Their respective $${\text{R}}_{{\text{P}}}^{2}$$

R

P

2

, RMSEP, and RPDP values were 0.951, 0.979, 1.814, 1.133, 4.498, and 6.845. In the high rubber content range, the model based on the LightGBM algorithm had the best prediction performance, with the RMSEP of the root segments and powder samples being 0.752 and 0.918, respectively.

Conclusions

This research indicates that dried TKS root powder samples are more appropriate for constructing a rubber content prediction model than segmented samples, and the predictive capability of root powder samples is superior to that of root segmented samples. Especially in the elevated rubber content range, the model formulated using the LightGBM algorithm has superior predictive performance, which could offer a theoretical basis for the rapid detection technology of TKS content in the future.
Compression algorithm for colored de Bruijn graphs
Authors: Rahman Amatur , Dufresne Yoann , Medvedev Paul
Published: May 2024

A colored de Bruijn graph (also called a set of k-mer sets), is a set of k-mers with every k-mer assigned a set of colors. Colored de Bruijn graphs are used in a variety of applications, including variant calling, genome assembly, and database search. However, their size has posed a scalability challenge to algorithm developers and users. There have been numerous indexing data structures proposed that allow to store the graph compactly while supporting fast query operations. However, disk compression algorithms, which do not need to support queries on the compressed data and can thus be more space-efficient, have received little attention. The dearth of specialized compression tools has been a detriment to tool developers, tool users, and reproducibility efforts. In this paper, we develop a new tool that compresses colored de Bruijn graphs to disk, building on previous ideas for compression of k-mer sets and indexing colored de Bruijn graphs. We test our tool, called ESS-color, on various datasets, including both sequencing data and whole genomes. ESS-color achieves better compression than all evaluated tools and all datasets, with no other tool able to consistently achieve less than 44% space overhead. The software is available at http://github.com/medvedevgroup/ESSColor.
Hierarchical Identity-Based Authenticated Encryption with Keyword Search over encrypted cloud data
Authors: Shiraly Danial , Eslami Ziba , Pakniat Nasrollah
Published: May 2024

With the rapid development of cloud computing technology, cloud storage services are becoming more and more mature. However, the storage of sensitive data on remote servers poses privacy risks and is presently a source of concern. Searchable Encryption (SE) is an effective method for protecting sensitive data while preserving server-side searchability. Hierarchical Public key Encryption with Keyword Search (HPEKS), a new variant of SE, allows users with higher access permission to search over encrypted data sent to lower-level users. To the best of our knowledge, there exist only four HPEKS schemes in the literature. Two of them are in traditional public-key setting, and the remaining ones are identity-based public key cryptosystems. Unfortunately, all of the four existing HPEKS schemes are vulnerable against inside Keyword Guessing Attacks (KGAs). Moreover, all of the existing HPEKS schemes are based on the computationally expensive bilinear pairing operation which dramatically increases the computational costs. To overcome these issues, in this paper, we introduce the notion of Hierarchical Identity-Based Authenticated Encryption with Keyword Search (HIBAEKS). We formulate a security model for HIBAEKS and propose an efficient pairing-free HIBAEKS scheme. We then prove that the proposed HIBAEKS scheme is secure under the defined security model and is resistant against KGAs. Finally, we compare our proposed scheme with related constructions regarding security requirements, computational and communication costs to indicate the overall superiority of our proposed scheme.
A modified persian version of the self-stigma of depression scale among the Iranian population: a methodological study in 2023
Authors: Jafari Alireza , Moshki Mahdi , Naddafi Fatemehzahra , Lael-Monfared Elaheh , Nejatian Mahbobeh
Published: May 2024

Introduction

This cross -sectional research evaluated the psychometric properties of the Self-Stigma of Depression Scale (SSDS) among Iranian people.

Methods

This methodological study was conducted among 881 people in 2023, Iran. The method of proportional stratified sampling was used to select participants. To evaluate the validity, face, content, construct, convergent, and discriminant were evaluated. The reliability of SSDS was assessed with the McDonald’s omega coefficient, Cronbach α coefficient, and test– retest (Intraclass Correlation Coefficient).

Results

In confirmatory factor analysis, the factor loading of all items of SSDS was more than 0.5, and two items had low factor loading. After deleted these items, goodness of fit indexes (such as GFI = 0.945, RMSEA = 0.067, AGFI = 0.917, CFI = 0.941, RFI = 0.905) confirmed the final model with 14 items and four factors of social inadequacy (3 items), help-seeking inhibition (4 questions), self-blame (3 questions), and shame (4 questions). In the reliability phase, for all items of SSDS, Cronbach α coefficient was 0.850, the McDonald omega coefficient was 0.853, and the intraclass correlation coefficient was 0.903.

Conclusion

The Persian form of SSDS was approved with 14 items and four factors: social inadequacy, help-seeking inhibition, self-blame, and shame. This tool can be used to check the status of self-stigmatization of depression in different groups.
The sense of coherence scale: psychometric properties in a representative sample of the Czech adult population
Authors: Tušl Martin , Šípová Ivana , Máčel Martin , Cetkovská Kristýna , Bauer Georg F.
Published: May 2024

Background

Sense of coherence (SOC) is a personal resource that reflects the extent to which one perceives the world as comprehensible, manageable, and meaningful. Decades of empirical research consistently show that SOC is an important protective resource for health and well-being. Despite the extensive use of the 13-item measure of SOC, there remains uncertainty regarding its factorial structure. Additionally, a valid and reliable Czech version of the scale is lacking. Therefore, the present study aims to examine the psychometric properties of the SOC-13 scale in a representative sample of Czech adults.

Methods

An online survey was completed by 498 Czech adults (18–86 years old) between November 2021 and December 2021. We used confirmatory factor analysis to examine the factorial structure of the scale. Further, we examined the variations in SOC based on age and gender, and we tested the criterion validity of the scale using the short form of the Mental Health Continuum (MHC) scale and the Generalized Anxiety Disorder (GAD) scale as mental health outcomes.

Results

SOC-13 showed an acceptable one- and three-factor fit only with specified residual covariance between items 2 and 3. We tested alternative short versions by systematically removing poorly performing items. The fit significantly improved for all shorter versions with SOC-9 having the best psychometric properties with a clear one-factorialstructure. We found that SOC increases with age and males score higher than females. SOC showed a moderately strong positive correlation with MHC, and a moderately strong negative correlation with GAD. These findings were similar for all tested versions supporting the criterion validity of the SOC scale.

Conclusion

Our findings suggest that shortened versions of the SOC-13 scale have better psychometric properties than the original 13-item version in the Czech adult population. Particularly, SOC-9 emerges as a viable alternative, showing comparable reliability and validity as the 13-item version and a clear one-factorial structure in our sample.
A TVP-VAR assessment of the spillover effects of geopolitical risk shocks on macroeconomic variability: a study of the Ghanaian economy
Authors: Asomaning Kwame Ofori , Hamayoon Shah , Uche Emmanuel
Published: May 2024

Our study verified the implications of the spillover of geopolitical risk (GPR) shocks to the economic crisis in Ghana. Our analysis employed the VAR-based spillover models by Diebold and Yilmaz (Int J Forecast 28:57–66, 2012; J Econ 182:119–134, 2014) and the Time-Varying Parameter Vector Autoregressive (TVP-VAR) connectedness approach by Gabauer and Antonakakis (Munich personal RePEc archive refined measures of dynamic connectedness based on TVP-VAR refined measures of dynamic connectedness based on TVP-VAR*, 2017). We scrutinized the interconnections and transmission mechanisms among key macro-financial variables spanning from 2000 to 2022. Our findings indicate that GPR is a fundamental source of shocks to the foreign exchange reserve (FXI), real exchange rate (REER), consumer price index (CPI), and debt. Other significant contributors include export (EXP) and import (IMP), with EXP standing out as the main shock transmitter. On the receiving end, CPI is most impacted by transmissions from IMP and GPR. Our study demonstrates that EXP and IMP are the top shock contributors, while FXI and CPI are the major recipients of these shocks. Such findings provide policymakers with valuable insights into the ramifications of geopolitical risk on the macroeconomic environment. Hence, policymakers are expected to provide necessary buffers to curb the influence of geopolitical risks on the economy.
Evaluation of sugar content and bioethanol production of Ethiopian local varieties “Nech Tinkish” and “Hawaye” sweet sorghum (Sorghum bicolor (L.))
Authors: Kasegn Melaku Mekonen , Simachew Addis , Redda Yisehak Tsegaye , Gebremedhn Hailay Mehari , Berhanu Addisu Desalegn , Mossa Mohammed Mebrahtu
Published: May 2024

Diversifying the use of climate-smart crops such as sweet sorghum has the potential to solve integrated food, bioenergy, feed, and land management problems. The study’s purpose is to quantify the sugar content of Nech Tinkish (v1) and Hawaye (v2) Ethiopian sweet sorghum varieties and investigate the interaction effect of fermentation parameters to determine their capacity for ethanol production. Sweet sorghum varieties were analyzed to determine their difference in °Brix content by extracting their juices. The juice was clarified using milk lime. Its total soluble sugars, total carbohydrates, and reducing sugars were determined using a digital refractometer, phenol sulfuric acid, and 3,5-dinitrosalicylic acid, respectively. A completely randomized factorial was employed to evaluate ethanol production capacity, and the ethanol content was estimated using a potassium dichromate solution. The °Brix results revealed that v2 had a higher sugar concentration than v1. Additionally, the estimated carbohydrate content of the juice ranged from 37.402 to 157.641 g/L. The estimated reducing sugar also varied from 4.644 to 33.412 g/L. Therefore, the estimated reducing sugar showed the hydrolysis of sweet sorghum juice by invertase and sulfuric acid produced more fermentable sugars. Fermentation at 30 °C with pH 4.5 incubated for 4 days yields the highest ethanol, and v2 yields higher (15.31%) ethanol, compared to v1 produced 14.85%. This study showed a basis for the existence of two sugar-rich climate smart sweet sorghum varieties with an extraordinary amount of sugar used as a source of biofuel and food simultaneously in a single plot of land.
Green nanotechnology: illuminating the effects of bio-based nanoparticles on plant physiology
Authors: Verma Sunil Kumar , Kumar Prashant , Mishra Anshu , Khare Renu , Singh Devendra
Published: May 2024

The use of bio-based nanoparticles in agriculture has gained significant attention due to their potential to enhance plant development, growth, and differentiation. This review aims to provide a comprehensive overview of the impact of bio-based nanoparticles on plant physiology. In this review paper, the various types of bio-based nanoparticles, including cellulose, chitosan, and lignin nanoparticles, and their effects on plant growth and development were discussed. The mechanisms by which these nanoparticles interact with plants at the cellular and molecular levels were also examined. Furthermore, the potential applications of bio-based nanoparticles in agriculture, such as improving nutrient uptake, enhancing stress tolerance, and promoting sustainable crop production, are also highlighted. Overall, this review provides valuable insights into the potential benefits of utilizing bio-based nanoparticles for enhancing plant growth and development while also considering their potential environmental impacts.

Graphical Abstract
Advancements in the biopolymer films for food packaging applications: a short review
Authors: Shah Yasir Abbas , Bhatia Saurabh , Al-Harrasi Ahmed , Khan Talha Shireen
Published: May 2024

Plastic-based films that are commonly used in the food packaging industry are tough to recycle due to their sorting issue and these films do not decay as they photodegrade into microplastics. These microplastics transport from the air and accumulate in soil, storm drains, and waterways. Recent initiatives in the food packaging industry have led to the development of edible and biodegradable films as sustainable alternatives to synthetic polymer-based plastics. These films, which are biocompatible, biodegradable, and serve as protective coatings on food surfaces, are designed to enhance shelf life by guarding against oxidation, moisture, and microbial spoilage. Recent advancements in polymeric films resulted in the development of high-performance, UV-blocking, nano-engineered, and intelligent pH-sensitive films, along with multilayer, heat-sealable, and active variants. These advanced materials not only prevent food deterioration but also facilitate the early detection of spoilage. However commercial success of these films which have been developed at a lab scale is still challenging due to unsatisfactory mechanical, barrier, thermal, and optical properties than synthetic films. Furthermore, an in-depth understanding related to human interaction, biodegradability, safety studies, scalability, and machinability is required to develop sustainable bioplastic films.

Graphical Abstract
An evaluation of the reliability of the Weather Research Forecasting (WRF) model in predicting wind data: a case study of Burundi
Authors: Placide Gatoto , Lollchund Michel Roddy
Published: May 2024

Background

The Weather Research and Forecasting (WRF) Model is an exceptional software for mesoscale climate modeling. It is extensively used to simulate key meteorological variables, including temperature, rainfall, and wind.

Methods

This study thoroughly examined the effectiveness of the WRF model in generating precise wind data for assessing the potential of wind power in Burundi. A meticulous evaluation of various combinations of model physics parameterization schemes was conducted to ensure accuracy.

By comparing the simulated data with measurements from four meteorological stations and utilizing statistical metrics such as root-mean-square error (RMSE) and bias, the accuracy of the WRF model was determined.

Results

 The findings of the study uncovered that utilizing WRF Single-Moment 3-Class (WSM3) for microphysics, Grell-Devenyi ensemble for cumulus physics, and Yonsei University for planetary boundary layer yields highly accurate wind data results for Burundi.

Furthermore, the WRF model was utilized to create detailed seasonal and annual mean wind maps with a high resolution.

Conclusion

These maps demonstrated that the western part of Burundi experiences higher wind speeds (ranging from 4 to 9.7 m/s) during the dry seasons revealing the potential for wind energy harvesting in the different areas of Burundi.
Comparative survival analysis of patients with HER2-low and HER2-positive metastatic breast cancer with or without brain metastases treated with trastuzumab deruxtecan.
Authors: Podder Vivek , Ranjan Tulika , Bardhan Mainak , McCracken Andrea , Hurmiz Charlie , Ganiyani Mohammad Arfat , Ahmad Shahzaib , Mahtani Reshma L. , Ahluwalia Manmeet Singh
Published: May 2024

1036

Background: Breast cancer outcomes vary among HER2-amplified and HER2 low-expression patients, particularly in the presence of brain metastases (BM). Trastuzumab deruxtecan (T-DXd) is FDA approved for the treatment of adult patients with unresectable or metastatic human epidermal growth factor receptor 2 (HER2)-low and HER2-positive (HER2+) patients (pts). This study aims to evaluate the impact of T-DXd treatment on overall survival among HER2 low and HER2-positive metastatic breast cancer (MBC) patients with and without brain metastases (BM). Methods: A retrospective analysis was conducted by using de-identified data available from seven US-based health systems part of Guardian Research Network (GRN). Patients treated with T-DXd from December 2019 to December 2023 with a diagnosis of HER2 low (immunohistochemistry [IHC] 1 + or IHC 2+/negative with HER2 FISH non-amplified) and HER2+ (IHC 3+ or HER2+ and FISH amplified MBC were included. Median overall survival (mOS), calculated from the initiation of T-DXd treatment, was assessed using Kaplan-Meier Curve analysis and Log-rank test. Results: A total of 380 MBC pts were identified. Among these 231 (61.1%) were stratified as having HER2 low and 149 (38.9%) HER2+ MBC. Pts with HER2+ disease had a higher prevalence of BM (n=70; 47%) compared to HER2-low pts (n=55;23.8%). The median age at BM diagnosis was 60.5 years, range: 21.6 to 87.1 years. Within the entire BM group (n=125), there were more pts with ER+ disease (81.9%) in the HER2 low group compared to HER2+ group (55.8%, p=0.01). The majority of these patients had multiple prior lines of therapy. HER2+ BM pts had a median overall survival (mOS) of 9.3 months [CI: 6.79-11.88, log rank p <0.001], compared to 4.3 months [CI: 1.89-6.82, log rank p <0.001] in the HER2-low pts. Without BM, HER2+ pts had a mOS of 10.7 months [CI: 0.71-20.82, log rank p <0.001], compared to 6 months [CI: 3.47-8.67, log rank p <0.001] in the HER2-low pts. Conclusions: In this retrospective study of heavily pretreated MBC patients with multiple prior lines of therapy, pts with HER2+ MBC with and without BM had a longer mOS compared to those with HER2- low disease, suggesting HER2 expression as a key determinant of T-DXd efficacy. Poor survival in MBC patients without BM was attributed to pre-treatment with multiple lines of therapy. Future research should explore combination therapies and predictive biomarkers while conducting clinical trials to optimize treatment strategies for HER2+ metastatic breast cancer patients.
Dissecting racial/ethnic disparities in non-small cell lung cancer staging at diagnosis: Intra-ethnic and geographic differences.
Authors: Liu Qinran , Koru-Sengul Tulay , Lopes Gilberto , Pinheiro Paulo S.
Published: May 2024

1575

Background: Lung cancer outcomes significantly rely on accurate and early detection. This population-based study aims to identify factors contributing to disparities in the stage of non-small cell lung cancer (NSCLC) diagnoses, focusing on Hispanic ethnic subgroups. Methods: Incident cases diagnosed from 2005–2018 were extracted from the Florida State Cancer Registry. Stage was categorized as resectable (Stage I-IIIA) or nonresectable (Stage IIIB/IIIC/IV) NSCLC. Multivariable logistic regression models were used to assess the association between race/ethnicity and stage at diagnosis, adjusted for socioeconomic, smoking status, and clinical factors. Regional stratification involved separate analyses for heavily Hispanic South Florida and the rest of Florida. Results: Among 157,034 NSCLC patients, with a racial distribution of 81.0% White, 8.3% Black, and 9.2% Hispanic, 43.2% were diagnosed at a resectable stage. Age-adjusted models showed Black patients had 22% higher odds (odds ratio [OR]=1.22; 95% CI: 1.21-1.23) and Hispanic patients had 11% higher odds (OR=1.11; 95% CI: 1.10-1.12) of non-resectable stage across all subgroups diagnoses compared to White patients. However, after further adjustment, higher odds were significant only for NHB (ORadj=1.12; 95% CI: 1.11-1.14) and Central American Hispanics (ORadj=1.41; 95% CI: 1.35-1.47). Regional differences were significant, with Hispanic patients in South Florida showing more favorable staging outcomes than Whites (ORadj=0.95; 95% CI: 0.94-0.97), while in the rest of Florida, Hispanic patients were at a disadvantage (ORadj=1.09; 95% CI: 1.07-1.10). Conclusions: Pronounced NSCLC staging disparities are manifest in Black and Hispanic populations, coupled with striking regional variations among Florida's Hispanic groups which underscores the necessity for targeted research to dissect and address the underlying causes of these inequities in the cancer care continuum.
Association of food insecurity with reduced cancer screening rates.
Authors: Shah Mohammed Masoom , Islam Shahidul , Braunstein Marc Justin
Published: May 2024

1592

Background: Social determinants of health, including access to adequate food and nutrition, influence many aspects of health, including cancer screening. This study explored the associations between food insecurity and cancer screening. Methods: A retrospective analysis was conducted using the National Health Interview Series from 2018-2022 to capture contemporary screening events. Based on scaled questionnaires, food security was stratified into either food secure or insecure, and then subgrouped into high, marginal, or low security. Associations were made with rates of undergoing at least one screening test. Relationships between variables were evaluated using Chi-Square and Cochran-Armitage trend tests. Logistic regression models were applied to find factors associated with cancer screening. Results: Patients included in analyses included: 51,957 for colon, 40,103 for breast, 45,983 for cervical, and 25,781 for prostate cancer screening. Between 2018-2022, those with food insecurity had significantly lower rates of cancer screening than those with food security across all four cancer screens, including colon (68.1% vs. 76.1%, p < 0.0001), cervical (83.3% vs. 87.5%, p < 0.0001), breast (73.3% vs. 79.5%, p < 0.0001) and prostate (41.3% vs. 54.0%, p < 0.0001) (Table). This was similar when comparing low, marginal, and high food security, respectively, for colon (68.1% vs. 68.2% vs. 76.1%, p < 0.0001), cervical (82.8% vs. 84.1% vs. 87.5%; p < 0.0001), breast (72.7% vs. 74.1% vs. 79.5%, p < 0.0001), and PSA screening (40.7% vs. 42.2% vs. 54.0%; p < 0.0001). Other variables were also analyzed, including insurance type, age, race, BMI, and health status. Those with public health insurance had lower screening rates for cervical cancer compared to those with private insurance (p < 0.0001). Individuals with food insecurity reported worse health than those who were food secure (p < 0.0001). Lower food security disproportionately affected Black patients (p < 0.001), and was associated with younger age (p < 0.0001) and higher rates of obesity (p < 0.0001). Conclusions: Individuals with self-reported food insecurity were found to have lower cancer screening rates than those who were food secure. Further analyses revealed associations between low food security status and obesity, public health insurance, younger age, and Black race. These data suggest that addressing food insecurity should be a priority, as these populations appear most vulnerable to lacking appropriate cancer screening. [Table: see text]
Long term impact of COVID-19 pandemic on mammographic breast cancer screening.
Authors: Karam Prasad Dhauna , McGary Alyssa , Kosiorek Heidi E. , Pritchett Joshua , Ellis Richard , Ruddy Kathryn Jean , Haddad Tufia C. , Yousufuddin Mohammed
Published: May 2024

1597

Background: The impact of COVID19 pandemic on patient health beliefs and healthcare systems has adversely affected mammographic breast cancer screening (MBCS) during the initial stages of the pandemic. The aims of our study were to determine whether the initial decrease in MBCS rates persisted or if there was a rebound in later stages and to quantify the impact on urban and rural populations. Methods: This retrospective observational study analyzed data from March 2017 to March 2023 to assess whether eligible women received annual MBCS during this timeframe. We included women aged 40-75 years, drawn from the integrated Mayo Clinic system, encompassing nearly 60 primary care practices in Arizona, Florida, Iowa, Minnesota, and Wisconsin, representing a broad cross-section of the United States (US) population. US census data was utilized to classify a county as urban or rural. We divided the study period into 4 categories:1) pre-COVID-19 pandemic (March 1, 2017-March 10, 2020), 2) early pandemic (March 11, 2020-March 10, 2021; pre-vaccination period), 3) mid- pandemic (March 11, 2021-March 10, 2022; delta and omicron variant dominant period), and 4) late pandemic (March 11, 2022-March 1, 2023; recovery period). Utilizing the Poisson regression model, we compared the incidence rate ratio (IRR) for rates of MBCS across the 4 predefined time periods. Multivariable modeling was performed to assess the significance of MBCS rates with urbanicity while controlling for the year (2017-2022) and age. Results: By race and ethnicity, the women in this cohort were White (92%), Black (3.1%), Asian (3%) and other (1.9%). The median age was 57 years and the cohort included 239, 804 women aged 40-75 years. MBCS rates was 49.2% in 2017, 47.1% in 2018, 46.4% in 2019, and decreased to 42.1% in 2020 and improved to 44.7% in 2021 and 43.8% in 2022. Compared with the pre-COVID-19 pandemic period, there was a significant decrease in MBCS rate in the early pandemic (IRR= 0.886, 95% CI 0.879, 0.892, p<0.001). Though the MBCS rate improved in the mid-pandemic period, it remained below the pre-pandemic level (IRR=0.941, 95% CI 0.934, 0.948, p<0.001). Similarly, during the late stage of the pandemic, the MBCS rate remained lower than the pre-pandemic level (IRR=0.922, 95% CI 0.915, 0.928, p<0.001). Rural counties had a lower MBCS rate compared with urban counties (IRR = 0.936, 95% CI 0.931, 0.941, p<0.001). Conclusions: This study confirms the negative impact of the early COVID-19 pandemic on MBCS, and it revealed its persistent effect, as the current MBCS rate remains below the pre-pandemic rate. These findings were more evident in rural counties. Factors contributing to the long-lasting impact are being analyzed with the intent to develop and implement targeted solutions to improve the MBCS rate.
Assessment of endocrine therapy adherence in a nurse-led cancer survivorship clinic: Results from the Linking You to Support and Advice (LYSA) trial.
Authors: Ghassemi Rad Mohammad Javad , O'Connell Kate , Raigal Laia Aran , Johnston Katie E , Flynn Naoimh Marie , O'Reilly Seamus , Noonan Sinead , Medved Katarina , McInerney Veronica B , Lowery Aoife J , Palmer Brendan Anthony , Dahly Darren L , Hegarty Josephine , Connolly Roisin M.
Published: May 2024

1627

Background: The care of those living with and beyond a cancer diagnosis requires dedicated cancer survivorship services to ensure optimal symptom control and quality of life. Supporting adherence to adjuvant endocrine therapy is also an essential component of care. We examined endocrine therapy adherence rates, and factors associated with non-adherence, in patients with early-stage breast cancer enrolling in a survivorship trial (LYSA). Methods: Women with early-stage hormone positive breast (n=168) cancers within 12 months of completing primary therapy, were enrolled in a randomized control trial with parallel arms (PMID 36357934). Experimental arm had access to nurse-led symptom management, dietetic consultation, and undertook bimonthly electronic patient reported outcome (ePRO). Active comparator group had access to conventional care and ePRO was completed at beginning and end of study. Endocrine therapy adherence was reported as the ability to take medication as prescribed over prior 4 weeks (Michigan Oncology Quality Consortium Adherence Tool), with response quantified as Excellent (adherence close to 100%), Very Good (80%), Good (60%), Fair (40%) and Poor (20%). Adherence Objective (Secondary) was adherence rate at 12 months after study entry in both arms, and factors associated with non-adherence. Results: A total of 148/168 women with breast cancer who completed the LYSA study reported adjuvant endocrine therapy use (74 experimental arm, 74 active comparator) during study conduct. Therapies included aromatase inhibitors (53%), tamoxifen (39%) and ovarian suppression combinations (8%). At study entry ≥ 80% adherence was reported in 74% of overall cohort (75% experimental, 72% comparator, p=0.7), with 1.6% reporting not taking any medication. By end of study (12 months), ≥ 80% adherence was reported in 74% overall (77% experimental, 71% comparator, p=0.4), while non-adherence rates were 2.9%. Among the pre-specified reasons for non-adherence at study end, “simply missing it” ranked highest (10%), and 5% “experienced side effects”. Post-menopausal patients were more likely than pre-menopausal to report excellent (versus sub-excellent) adherence at study end (OR 4.60, 95%CI 1.45 – 16.13, p = 0.012, adjusted for arm, age, and ECOG). Additional exploration of reasons for non-adherence will be presented. Conclusions: Adjuvant endocrine therapy adherence rates in both arms at 12 months after study entry collected via ePRO were good (≥ 90% reporting excellent or very good adherence). Additional factors potentially associated with non-adherence are being analyzed including self-care agency, symptom burden and quality of life. Clinical trial information: NCT05035173 .
The use of telehealth in a large community oncology practice prior to and during the COVID-19 pandemic.
Authors: Nickleach Dana , Saravanan Priya , Meyer Gary , Mehring Kiana , Mumuney Ganiat , Gordan Lucio N.
Published: May 2024

1631

Background:The COVID-19 pandemic prompted implementation of telehealth services in oncology following CMS approval for reimbursement. Using data from a single large US-based community oncology practice, we characterized the adoption of telehealth, evaluated telehealth’s impact on patient transit to the site of care, and the frequency of ER visits and hospitalizations. Methods: A retrospective cohort analysis was conducted using data from January 2019 through June 2022 from the FCS EMR database, IQSS’s RefineIQ database, which captures and standardizes patient treatments for consistent analysis, InfoDive, a reimbursement platform with data on clinic visits, and Oncology Care Model data. The target population included all patients with cancer aged 18 or older under active management at FCS during the study window with at least 60 days of follow-up. Patient disease and demographic characteristics and ER/hospital care visits were compared between telehealth groups, as well as impact of telehealth use on transit time and distance traveled for patients was summarized using descriptive statistics. Results:178,531 patients were analyzed. Median age was 68, with 64% female and 47% white. 97% of patients lived in a metropolitan area, 97% in Florida, and 77% in areas with less than $78,000 median income. Median duration of disease was 41 months, 24% of patients were stage IV and 93% ECOG 0-1. 20% of patients used telehealth, with 81% having only one telehealth use per year. Patients using telehealth, compared to those that did not, were more likely to be white (50% vs 46%), have commercial insurance (30% vs 27%), live in areas where annual median income was > $78,000 (25% vs 22%) and ≥95% of the population graduated high school (31% vs 26%). Telehealth saved a median of 5 miles driven per user annually. Users tended to have more advanced disease than non-users, with a higher proportion of metastatic disease (27% vs 23%), high CMS risk score (16% vs 15%), and line of therapy 2 or greater (10% vs 7%). Users had higher median annual oncology clinic visits both pre- (5 vs 3) and post-telehealth approval (6 vs 5). Hospital admissions after ER visits were more frequent among users both pre- (14% vs 13%) and post-telehealth approval (29% vs 25%). Conclusions: Telehealth was somewhat more likely to be used by patients with cancer in more affluent communities, white patients, patients who had more advanced disease and more frequent clinic and hospital visits. Most patients who used telehealth used it only once, and the low utilization of telehealth had limited impact on patient transit effort to the clinic. This study is limited by its concentration at a single large practice in Florida, the relatively low use of telehealth, retrospective design, and selection bias, as the telehealth population had more advanced disease and likely needed more frequent intervention.
Associations of predictive biomarkers, MHC-I and MHC-II, with clinical and molecular features in a diverse breast cancer cohort.
Authors: Reid Sonya A. , Sun Xiaopeng , Kennedy Laura Carpin , Gonzalez-Ericsson Paula , Sanchez Violeta , Sanders Melinda , Perou Charles M. , Troester Melissa A. , Balko Justin M
Published: May 2024

517

Background: The addition of immunotherapy (IO) to chemotherapy in triple negative breast cancer (TNBC) has improved survival outcomes and recent studies have also shown improvements in pCR rates in early high-risk HR+/HER2- breast cancer. However, these improvements are not without toxicities and thus there is a critical need for biomarkers to select patients that are most likely to benefit from immunotherapy. MHC-I and tumor-specific MHC-II (tsMHC-II) expression are candidate biomarkers for predicting PD-(L)1 checkpoint inhibition benefit and have previously shown potential for predicting treatment response in multiple tumor types, including breast cancer. However, the existing data were generated from clinical trials with limited racial/ethnic diversity. Here we report associations between MHC-I and MHC-II expression and clinicopathological features in a large, racially and ethnically diverse breast cancer cohort. Methods: We performed multiplexed immunofluorescent tissue-based analysis of participants in the Carolina Breast Cancer Study (CBCS). Intrinsic subtype and P53 mutant-like status were identified using RNA-based multigene assays. To evaluate MHC-I we ranked all participants based on tumoral MHC-I intensity, with the top 33% categorized as "MHC-IHigh"; MHC-II+ expression on tumor cells was defined as ≥5% of tumor cells. Association between MHC-I/II and clinicopathological features by race was tested using Chi-square test or fisher-exact test and relative frequency difference (RFD) was calculated. Results: This study included 1630 participants (48% Black and 52% non-Black) with Stage I-III breast cancer. Black participants had a higher incidence of TNBC (25% vs. 12.5%, p=<0.001) and Basal-like (29% vs. 14%, p=<0.001) tumors. Basal-like and TP53 mutant tumors were more frequently observed among Black participants with HR+/HER2- tumors (11% vs. 5.5%, p=0.006 for Basal-like; 26% vs. 17%, p=0.003 for p53 mutant-like). While there was a similar distribution of tsMHC-II+ tumors between Black and non-Black participants, the relative frequency of tsMHC-II+ tumors was significantly more common in Black participants with HR+/HER2- breast cancer compared to non-Black participants (7.9% vs. 5%, p=0.04). Moreover, Black participants also were significantly more likely to have MHC-Ihigh tumors (38.9% vs. 28.4%, p=0.01), which, after IHC subtype stratification, was only significant among participants with HR+/HER2- breast cancer (28.2% vs. 22.3%, p=0.02). Conclusions: In this diverse breast cancer cohort, MHC-I and MHC-II expression, candidate predictive biomarkers of IO benefit, were more prevalent in tumors from Black women with HR+/HER2- breast cancer. These results underscore the importance of diverse and equitable clinical trial enrollment in future IO trials, especially in HR+/HER2- breast cancer.
Breast Cancer Index re-stratifies 21-gene assay risk groups for late distant recurrence and extended endocrine therapy benefit: Results from the BCI Registry Study.
Authors: Chuy Valerie , Siuliukina Natalia , O'Neal Brandon , Anderson Amanda Kristine Laust , Hoskins Kent , Zhang Yi , Treuner Kai , Gadi VK
Published: May 2024

529

Background: Patients with hormone receptor-positive (HR+) breast cancer face a prolonged risk for late distant recurrence (DR), even after 5 years of adjuvant endocrine therapy. The BCI (Breast Cancer Index, Biotheranostics) is a genomic assay that provides the risk of late DR and predicts the likelihood of benefit from extended endocrine therapy (EET) based on the H/I (HOXB13/IL-17BR) ratio. The 21-gene RS (Recurrence Score; Oncotype DX, Exact Biosciences) test provides the 9-year risk of DR and the benefit from adjuvant chemotherapy (CT) in HR+/HER2− breast cancer. However, the RxPONDER trial showed that RS was not predictive of CT benefit in premenopausal women with N1 disease. Here, we investigated the concordance between BCI prognostic score and H/I versus RS in HR+/HER2- patients from the BCI Registry study. Methods: The BCI Registry study is a prospective, multi-institutional study to evaluate the long-term clinical outcome, decision impact and medication adherence in early-stage HR+ breast cancer patients. Based on TAILORx and RxPONDER results, RS stratified patients into four risk groups: Age ≤ 50, No CT benefit (RS ≤ 15); Age ≤ 50, CT benefit (RS>15); Age > 50, No CT benefit (RS ≤ 25); and Age > 50, CT benefit (RS > 25). Kendall’s correlation coefficient (R) was used to estimate the correlation between BCI prognostic score, BCI (H/I) and RS as continuous variables. Results: BCI and RS results were reported for 847 patients (76% T1; 59% grade II; 85% N0). H/I classified 559 patients (66%) as H/I-Low and 288 (34%) as H/I-High, respectively, while BCI prognostic risk scores classified 434 patients (51%) as low-risk and 413 (49%) as high-risk for late DR. RS classified 30 patients (4%) as Age ≤ 50, No CT benefit, 38 (5%) as Age ≤ 50, CT benefit, 665 (78%) as Age > 50, No CT benefit and 114 (13%) as Age > 50, CT benefit. When analyzed as continuous variables, BCI H/I and prognostic score showed a weak correlation with RS with R=0.18 and R=0.2, respectively. Based on categorical groups, H/I had low concordance with RS for younger women (Age ≤ 50) but showed some correlation with RS for older women (Age > 50). Among those older than Age 50, H/I identified 31% of those with no CT benefit as likely to benefit from EET while 39% of those with CT benefit as not likely to benefit from EET. Conclusions: BCI H/I and prognostic risk scores exhibited low concordance with RS risk categories. H/I consistently re-stratified RS risk categories into distinct H/I-Low and -High groups, further substantiating that prediction of CT benefit does not equate to prediction of EET benefit. [Table: see text]
Predicting epirubicin response in Danish patients with breast cancer.
Authors: Buhl Ida Kappel , Nart Jan , Papadovasilakis Zacharias , Sørensen Simon , Jensen Peter Buhl , Ejlertsen Bent , Buhl Ulla Hald , Tsamardinos Ioannis
Published: May 2024

539

Background: Anthracyclines are highly effective chemotherapy drugs. However, they are toxic and feared for their cardiotoxicity. There is a need for improved personalized prediction of treatment benefit. For example, state-of-the-art guidelines (NCCN Guidelines Version 1.2024 and meta-analysis PMID: 37061269) mark anthracyclines as optional adjuvant treatment of estrogen receptor (ER) positive, HER2 negative early high-risk breast cancer. A biomarker to predict whether a patient will benefit from anthracycline treatment could potentially lead to increased cure rate and overall reduced toxicity. However, no such test exists. Here we introduce a clinically validated, predictive test based on clinical tumor transcriptome data and the cell line panel NCI60 that can forecast epirubicin response in breast cancer patients with high accuracy. Methods: Microarray gene expression levels and growth inhibition levels (GI50) from the NCI60 cell line panel were acquired from NCBI to build a prediction algorithm. A tumor-specific variance-based filter based on >6.000 patient tumors was applied to select the most relevant genes. AUC (Area Under Curve) was used to evaluate the performance of numerous configurations and combinations of parameters that led to different gene-signature sets. Response scores based on the gene expression level of all genes in each signature were generated for each sample in a published cohort (PMID: 23340299) and a final signature with 141 genes was the best-performing signature (Progressive Disease versus Response AUC 0.81 and ANOVA-test p-value < 0.01). The model was validated on a cohort of Danish breast cancer patients treated with epirubicin in the metastatic setting (n=153), with time-to-progression (TTP) as endpoint (PMID: 31654283, PMID: 30099635). Finally, we used an established method (PMID: 30289602) to test if the gene signature was correlated between microarray- and RNA sequencing-based datasets. Results: The hazard ratio (HR) for the best-performing signature was 0.31 when comparing patients with a difference of at least 50 in response score (two-sided p-value = 0.012, 95% confidence interval 0.12-0.77, endpoint TTP, continuous scoring). This was consistent in a multivariate model including ER and HER2. Using the data and method provided by Pedersen et al (PMID: 30289602), we also saw a high correlation between matching microarray and RNA sequencing datasets for the genes in our model (mean Pearson’s r = 0.87). Conclusions: Here is presented a development and clinical validation of an accurate epirubicin response signature for breast cancer patients. The expression of the genes in the signature is highly correlated between microarray- and RNA sequencing data. Moving forward, we will seek to validate the model independently on RNA sequencing data. Our aim is that these models will be accepted for prospective clinical tests at local hospitals in Denmark in early 2025.
Artificial intelligence algorithms for recurrence risk score prediction in early-stage breast cancer: A multicenter study of 437 cases.
Authors: Bayram Ertugrul , Basaran Gul , Gokmen Erhan , Mandel Nil Molinas , Oyan Basak , Uskent Necdet , Karagoz Bulent , Yildirim Yesim , Karaaslan Sinan , Guler Emine Nilufer , Eralp Yesim , ER ÖZLEM , Karaoglu Aziz , Komurcu Seref , Mete Burak , Gemci Fahriye , Ibrikci Turgay , Paydas Semra
Published: May 2024

545

Background: In non-developed and developing countries, the 21 gene assay (Oncotype DX) test is expensive and not covered by private and social health insurance, which limits its use. However, clinicians need this test for treatment prediction in early stage breast cancer. Therefore, we aimed to predict the risk of recurrence in patients using artificial intelligence (AI) algorithms. Methods: This study was conducted with 437 early-stage breast cancer patients whose recurrence risk scores were determined by 21 Oncotype DX and were followed in 14 oncology centers in Turkey between 2012 and 2022. The optimum cut-off for the 21 gene assay risk score (RS) was found by ROC analysis using the patients' recurrence information (scale of RS ≥17: high risk, <17: low risk). In artificial intelligence prediction models, patients with a 21 gene assay risk score ≥17 were considered high risk. Additionally, algorithm predictions were made within other cut-offs recommended in the literature (<10, 11-25, >25). Python programming language was used in the analyses. The Random Forest (RF), Logistic regression (LG) and K-Nearest Neighbors (KNN) were used as AI prediction algorithms. Results: The study analyzed 437 women with an average age of 49.49±10.30. During the follow-up period from 2012 to 2022, 7.1% of the patients experienced a recurrence. AI prediction algorithms which were RF, LG, and KNN generated confusion matrices for two classes, with a threshold of 17 and 25. The algorithms were then applied to the dataset with risk score thresholds of 1-10, 11-25, and greater than 25, resulting in three classes. The dataset was analyzed using recall, precision, and F1 score values. The results showed that RF had the most accurate predictions for RS = 17 (Table). Conclusions: The use of artificial intelligence algorithms in predicting the risk of recurrence in early stage breast cancer is promising in determining treatment decisions for clinicians who cannot access genetic results. [Table: see text]