Stepwise Regression Research Articles

Abstract P3-07-22: A miRNA expression profiling of breast cancer to develop a metastases predictor model and identify new molecular players of metastatic outgrowth

Abstract Background: Metastasis is the leading cause of breast cancer-related mortality. Current classification is based mainly on immunohistochemical markers and fails to reliably predict metastatic potentials. In recent decades, microRNAs (miRNAs) have emerged as promising clinical biomarkers due to their capacity to regulate key molecules involved in cancer progression and metastatic spread. Methods: To identify novel miRNA-based biomarkers, we analyzed three retrospective cohorts. A miRNA Affymetrix Gene Chip 4.0 array was used to identify relevant miRNAs in the discovery cohort (n=40). Next, RT-qPCR analysis was performed to evaluate the accuracy of selected miRNAs in identifying patients who developed distant metastases in the extended cohort (n=223). A stepwise logistic regression model was used to construct a prognostic tool for metastases, including miRNA levels and clinicopathological features. The effects that both miRNAs may exert on tumor cell phenotype was preliminary assessed in a panel of breast cancer cell lines in terms of cell viability. Following ectopic modulation of candidate miRNAs by miRVANA miRNA mimics, cell viability was measured by PrestoBlue viability reagent. Results: Global expression analysis of the discovery cohort identified eight miRNAs with differential expression between metastatic and non metastatic tumors. In the extended cohort, miR-3916 and miR-3613-5p were the best miRNAs for identifying patients who developed distant metastases. Increased expression levels of miR-3916 were associated with a reduced risk of developing distant metastases (OR=0.42, 95% CI: 0.23-0.70, p=0.002), while increased expression levels of miR-3613-5p were associated with an elevated risk (OR=2.06, 95% CI: 1.27-3.50, p=0.005). Importantly, by including the expression levels of miR-3916 and miR-3613-5p in a model with clinicopathological covariates, the discriminatory power reached an AUC of 0.85 (95% CI: 0.79-0.91), outperforming a model with clinicopathological covariates only (AUC=0.76, 95% CI: 0.68-0.84) (delta-AUC p=0.001). As expected, the evaluation of the effects of miR-3613-5p and miR-3916 transfection in vitro showed that both miRNAs were able to impair cell viability in breast cancer cell lines. Conclusions: In this study, we identified miR-3613-5p and miR-3916 as putative metastases associated miRNAs. A logistic regression model including both miRNAs and clinicopathological characteristics were able to predict the risk of metastases development supporting their potential utility in the clinical setting. Moreover, our initial in vitro studies suggest both miRNAs may affect tumor cell phenotype. Further in vitro and in vivo are currently ongoing to characterize miR-3613-5p and miR-3916 role in the mechanisms underlying metastases development in breast cancer. Citation Format: Andrea Fontana, Raffaela Barbano, Barbara Pasculli, Tommaso Mazza, Orazio Palumbo, Elena Binda, Tommaso Biagini, Michelina Rendina, Antonio lo Mele, Giuseppina Prencipe, Sara Bravaccini, Roberto Murgo, Luigi Ciuffreda, Maria Morritti, Vanna Maria Valori, Francesca Sofia Di Lisa, Patrizia Vici, Marina Castelvetere, Massimo Carella, Paolo Graziano, Evaristo Maiello, Massimiliano Copetti, Paola Parrella. A miRNA expression profiling of breast cancer to develop a metastases predictor model and identify new molecular players of metastatic outgrowth [abstract]. In: Proceedings of the San Antonio Breast Cancer Symposium 2024; 2024 Dec 10-13; San Antonio, TX. Philadelphia (PA): AACR; Clin Cancer Res 2025;31(12 Suppl):Abstract nr P3-07-22.

Immunoinflammatory biomarkers as predictors of hemorrhagic transformation in acute ischemic stroke patients after endovascular thrombectomy

BackgroundHemorrhagic transformation (HT) and symptomatic intracranial hemorrhage (sICH) are common complications of endovascular thrombectomy (EVT) in acute ischemic stroke (AIS) patients. The role of peripheral immune inflammation in HT after EVT is unclear. This study aimed to evaluate the relationship between immune inflammatory factor levels and HT and sICH occurrence, and to develop predictive models.MethodsWe included 81 AIS patients who underwent EVT. Peripheral blood samples were collected immediately post-EVT to measure immunoinflammatory markers. Least absolute shrinkage and selection operator (LASSO) regression was used to select variables, and backward stepwise multivariable logistic regression identified independent predictors and predictive models for HT and sICH. The models’ discrimination was assessed using the area under the receiver operating characteristic curve (AUC), and calibration was evaluated using the Hosmer–Lemeshow test. Logistic regression models were used to evaluate the impact of HT or sICH on 90-day functional outcomes and mortality.ResultsThe HT rate was 39.51% (32/81), and the sICH rate was 17.07% (14/81). Multivariate analysis revealed that HT after EVT was significantly associated with collateral score [OR 0.27 (95% CI 0.13–0.52), p < 0.001], arteriosclerosis etiology [OR 0.11 (95% CI 0.02–0.46), p = 0.006], puncture to recanalization time [OR 3.72 (95% CI 1.07–14.59), p = 0.04], and levels of IL-6 [OR 7.33 (95% CI 2.1–31.07), p = 0.003; AUC 0.696 (95% CI 0.593–0.799)]. sICH was independently related to direct aspiration (DA) techniques [OR 0.07 (95% CI 0.09–0.35), p = 0.004] and neutrophil-to-albumin ratio (NAR) values [OR 5.69 (95% CI 1.16–37.24), p = 0.044; 0.676 (95% CI 0.550–0.803)]. Both predictive models for HT [AUC 0.898 (95% CI 0.831–0.965)] and sICH [AUC 0.925 (0.853–0.997)] exhibited good discrimination and calibration.ConclusionIL-6 and NAR are potential biomarkers for predicting HT and sICH in AIS patients after EVT. This study developed simple and effective predictive models for HT and sICH based on immunoinflammatory factors. Future research should explore the spatiotemporal effects of immune inflammation on prognosis in AIS patients undergoing EVT.

Abstract PS18-08: MDA iLobulaRx: An Advanced Clinico-Patho-Therapeutic Tool for Risk Stratification in Early-Stage Invasive Lobular Carcinoma

Abstract Background: Invasive lobular carcinoma (ILC) is a distinct subtype of breast cancer that differs from the more common invasive ductal breast cancer (IDC) in its unique characteristics and arguably inferior prognosis. Recent studies have shown that ILC is associated with worse long-term outcomes compared to IDC. Risk stratification is essential in early breast cancer for tailoring treatment to optimize outcomes. We developed the MDA iLobular prognostic tool to incorporate clinical and pathological features to predict recurrence and survival in early-stage ILC (eILC). Recent studies suggest that the choice of adjuvant endocrine therapy (ET) significantly impacts recurrence and survival in eILC. We present an improved tool, “MDA iLobulaRx,” which includes clinical, pathological, and planned ET to enhance prognostic accuracy. Methods: A retrospective analysis was performed on patients treated at MD Anderson Cancer Center with a diagnosis of stage I-III ILC in our prospectively collected and curated electronic database. Primary endpoints were overall survival (OS) and distant recurrence-free survival (DRFS). Univariate and multivariate Cox Proportional Hazard (PH) regression models assessed the significance of variables. Univariate Cox analysis identified prognostic factors for further multivariate analysis. Backward and stepwise multivariate Cox PH regression identified significant factors for final models. Hazard ratios and 95% confidence intervals were estimated, considering P &amp;lt; 0.05 significant. We evaluated model performance using Harrell's C-index, dividing data into two-thirds training and one-third test datasets. Results: The study included 4,216 female patients with a median age of 56 years. The median pathological tumor size was 20 mm, and the median number of lymph nodes involved was one. Among these patients, 70% were post-menopausal. The ET used were tamoxifen (32%), non-steroidal aromatase inhibitor (NSAI) (30%), and no therapy (38%). The training cohort had 2,950 patients, and the test cohort had 1,266 patients. The best prognostic model for OS and DRFS had a Harrell's C-index of 0.727 and 0.713, respectively. The model included age, lymph nodes, tumor size, ER status, tumor grade, ILC histology, LCIS presence, and choice of adjuvant endocrine therapy data. (Table 1). Conclusion: MDA iLobulaRx represents a significant advancement, offering the first dedicated clinico-patho-therapeutic tool for risk stratifying eILC. Citation Format: Jason A Mouabbi, Akshara S. Raghavendra, Sarah Pasyar, Roland L. Bassett Jr., Azadeh Nasrazadani, Carlos H. Barcenas, Amy A. Hassan, Debu Tripathy, Rachel M. Layman. MDA iLobulaRx: An Advanced Clinico-Patho-Therapeutic Tool for Risk Stratification in Early-Stage Invasive Lobular Carcinoma [abstract]. In: Proceedings of the San Antonio Breast Cancer Symposium 2024; 2024 Dec 10-13; San Antonio, TX. Philadelphia (PA): AACR; Clin Cancer Res 2025;31(12 Suppl):Abstract nr PS18-08.

Factors Affecting Exercise Tolerance in Patients Who Underwent Video-Assisted Thoracoscopic Surgery for Lung Cancer

BackgroundVideo-assisted thoracoscopic surgery (VATS) is increasingly being performed instead of open thoracotomy for early-stage lung cancer because of the minimally invasive nature of VATS. In this study, we aimed to identify the factors related to exercise tolerance in patients with lung cancer who underwent VATS.Methodsin this study, we included 73 consecutive patients who underwent video-assisted lung lobectomy for lung cancer at Shikoku Cancer Center, Matsuyama, Japan. Clinical parameters like the presence or absence of chronic obstructive pulmonary disease (COPD), operative time, intraoperative bleeding, duration of postoperative drain placement, skeletal muscle mass index, muscle strength, physical activity, and exercise tolerance were assessed. Muscle strength was measured using handgrip strength. Exercise tolerance was measured according to the 6-minute walk distance.ResultsAfter surgery, handgrip strength and six-minute walk distance were significantly lower than those observed during the preoperative evaluation of the patients (p < 0.05). Stepwise multiple regression analyses showed that higher postoperative handgrip strength, males, and higher preoperative physical activity had a significant positive effect on postoperative six-minute walk distance (p < 0.05, R2 = 0.348).ConclusionsImproving postoperative exercise tolerance also requires interventions to increase physical activity before surgery and postoperative interventions that also focus on muscle strength.

Slaughter weight and rib-eye area as a predictor of some carcass characteristics and premium meat production in three cattle breeds

This research examines the predictive capacity of age, slaughter weight (SW), and musculus longissimus dorsi (MLD=rib-eye) areas on carcass characteristics and the quantity of high-quality meat across three cattle breeds: Holstein, Brown Swiss, and Simmental. Correlation and stepwise regression analyses were conducted on 64 bull carcasses to assess the predictive power of SW and MLD in terms of carcass characteristics and valuable meat yield. The findings indicated significant positive correlations between SW and premium cuts, especially in the Holstein and Simmental. Furthermore, significant correlations existed between MLD and valuable meats, indicating that both SW and MLD areas are essential determinants in valuable meat production. The regression models established to predict premium meat yields based on SW achieved an explanatory power (R-squared) of 80% and higher for cold carcass weight (CCW), roast (Ro), knuckle (K), topside (TS), total high-value meat (THM), roast percentage (RoP), knuckle percentage (KP), topside percentage (TSP), total high-value meat percentage (THMP). However, low R-squared values in the regression models revealed that the MLD area had a lower predictive value for premium meat production. Despite the MLD area's strong correlation with the factors analyzed for prediction, the result implies that SW is an excellent predictor of meat production. The findings indicate methods for enhancing carcass quality and meat production, with the Simmental breed yielding the most valuable meat, followed by Brown Swiss and Holstein. These findings can guide breeders in enhancing meat quality and profitability in enterprises.

Predicting Nitrous Oxide Emissions from China’s Upland Fields Under Climate Change Scenarios with Machine Learning

Upland fields are a significant source of N2O emissions. Thus, an accurate estimation of these emissions is essential. This study employed four classical modeling approaches—the Stepwise Regression Model, Decision Tree Regression, Support Vector Machine, and Random Forest (RF)—to simulate soil N2O emissions from Chinese upland fields. The upland crops considered in this study covered food crops, oil crops, cash crops, sugar crops, fruits, and vegetables, excluding flooded rice. Comparative analysis revealed that the RF algorithm performed the best, with the highest R2 at 0.66 and the lowest root mean square error at 0.008 kg N2O ha−1 day−1. The application rate of mineral nitrogen fertilizers, mean temperature during the growing season, and soil organic carbon content were the key driving factors in the N2O emission model. Utilizing the RF model, total N2O emissions from Chinese upland fields in 2020 were estimated at 183 Gg. Future projections under Representative Concentration Pathway (RCP) scenarios indicated a 2.80–5.92% increase in national N2O emissions by 2050 compared to 2020. The scenario analysis demonstrated that the proposed nitrogen reduction strategies fail to counteract climate-driven emission amplification. Under the combined scenarios of RCP8.5 and nitrogen reduction strategies, a net 4% increase in national N2O emissions was projected, highlighting the complex interplay between anthropogenic interventions and climate feedback mechanisms. This study proposes that future attention should be paid to the development of nitrogen optimization strategies under the impact of climate change.

Abstract P3-03-03: Disparities in Quality of Life Among Breast Cancer Survivors in the All of Us Research Program

Abstract Background: Breast cancer is the most common cancer among women. Advancements in breast cancer treatment and early diagnosis have resulted in higher survival rates necessitating the importance of studying quality of life (QOL) among breast cancer survivors. QOL is an important endpoint in clinical trials as it provides insights into the overall well being and long-term outcomes of patients. Understanding QOL is also crucial for guiding treatment decisions, supporting survivorship care, and shaping healthcare policy. Previous studies have shown that racial/ethnic disparities in QOL exist, and factors affecting QOL have been investigated. However, inconsistencies remain regarding specific determinants, and a large-scale study examining disparities in QOL among breast cancer survivors in the U.S. has not been done. Methods: We analyzed data from 2,022 female breast cancer survivors in the National Institutes of Health's All of Us Research Program database. QOL (scored from 1 to 5) was measured using survey response data in which participants answered the question: “In general, would you say your quality of life is – excellent (5), very good (4), good (3), fair (2), or poor (1).” Univariable and multivariable linear regression analyses were performed to identify demographic, socioeconomic and psychosocial factors associated with QOL. The multivariable analysis consisted of using Bonferroni correction and stepwise regression to adjust the p-values for multiple tests and to select the most statistically relevant variables contributing to QOL, respectively. Results: The cohort was predominantly non-Hispanic White (84%), with the remaining participants being non-Hispanic Black (5.5%), Hispanic (3.1%), Other/Mixed (2.6%), and non-Hispanic Asian (1.6%). The average age was 70 years old. In the multivariable analyses, non-Hispanic Black (β= -0.33, 95%CI: [−0.49, −0.18], p &amp;lt; 0.005) and Hispanic (β= -0.38, 95%CI: [−0.58, −0.18], p &amp;lt; 0.005) survivors had lower QOL compared to non-Hispanic white survivors. For each additional year of age, the QOL score slightly increased by 0.01 units (95%CI: [0.009, 0.018], p &amp;lt; 0.001). Lower education (high school or lower) (β= -0.28, 95%CI: [-0.42, -0.14], p &amp;lt; 0.005) and lower household income levels (annual household income less than $25,000K) (β= -0.66, 95%CI: [-0.84, -0.48], p &amp;lt; 0.001) were also significantly associated with lower QOL. Interestingly, lacking confidence in filling out medical forms (β= -0.53, 95%CI: [-0.79, -0.28], p &amp;lt; 0.005) and feeling that medical providers were not listening (β= -0.22, 95%CI: [-0.29, -0.15 AZ], p &amp;lt; 0.001) were associated with lower QOL. Conversely, having assistance with daily tasks all or most of the time (β= 0.32, 95%CI: [0.19,0.45], p &amp;lt; 0.001) was linked to a higher quality of life. Conclusion: In the national All of Us cohort, quality of life was associated with age, race and ethnicity, age, other socioeconomic factors, and psychosocial factors. Enhancing the availability of daily assistance and improving patient-provider communication can help mitigate some of these disparities. Our findings suggest that recognizing populations at risk and connecting them to resources with social work, support groups, and survivorship clinics will impact survivors’ quality of life. Citation Format: Gagandeep Kaur, Jiayuan Wang, An D Truong, Hester Nguyen, Leah Puglisi, Carrie Costantini, Ritesh Parajuli, Hannah Lui Park. Disparities in Quality of Life Among Breast Cancer Survivors in the All of Us Research Program [abstract]. In: Proceedings of the San Antonio Breast Cancer Symposium 2024; 2024 Dec 10-13; San Antonio, TX. Philadelphia (PA): AACR; Clin Cancer Res 2025;31(12 Suppl):Abstract nr P3-03-03.

Abstract P1-04-01: Impact of Obesity, Skeletal Muscle Index, and Comorbidities on Chemotherapy-Related Outcomes in Early Breast Cancer: A Retrospective Subanalysis

Abstract Introduction: Women with early breast cancer (EBC) generally have a very good prognosis, with 5-year survival rates approaching 90%. Chemotherapy can cause significant side effects that decrease quality of life. Many patients with EBC also have comorbidities, but it is unclear which of these is more strongly associated with chemotherapy intolerance. In our previous retrospective study, we found correlation between sarcopenia detected by bioelectrical impedance spectrometry and worse chemotherapy tolerance; in addition, older age and the presence of multiple comorbidities were also associated with greater chemotherapy toxicity in unadjusted analyses. Here we seek to better understand the relationship between specific comorbid conditions and chemotherapy tolerance. Methods: This retrospective sub-analysis included 323 patients who received chemotherapy for EBC Patient characteristics, treatment details, and toxicity-related outcomes were obtained. Multivariate logistic regression models were used to associate sarcopenia status with toxicity endpoints, adjusting for other patient characteristics. Age (&amp;lt;65 years old), obesity (BMI&amp;gt;30), and sarcopenia (low SMI &amp;lt;6.75 kg/m2) were forced into the final model. Analysis included 16 comorbidities: hypertension (HTN) , diabetes mellitus (DM), congestive heart failure, cirrhosis, renal disease, chronic obstructive pulmonary disease, tobacco use, hypothyroidism, previous breast cancer, previous cancer, osteoarthritis (OA), rheumatic arthritis, coronary artery disease, peripheral vascular disease, osteoporosis/osteopenia, and stroke. Significant comorbidities were identified through a backward elimination procedure. Results: Obesity was significantly associated with higher chemotherapy toxicity (OR = 3.58, 95% CI: 1.39-9.24, p=0.02). Patients with sarcopenia also had a notably increased risk of chemotherapy toxicity (OR = 6.94, 95% CI: 3.07-15.62, p&amp;lt;0.0001), as did those with HTN (OR = 1.98, 95% CI: 1.01-3.87, p=0.048). Regarding chemotherapy dose delay or reduction, patients with an SMI below 6.75 kg/m2 were more likely to experience dose delays (OR = 2.48, 95% CI: 1.10-5.56, p=0.03). Patients with renal disease had an elevated risk of dose delays as well (OR = 4.41, 95% CI: 1.17-16.66, p=0.03). Early termination of chemotherapy was more common in obese patients compared to those of normal weight (OR = 4.67, 95% CI: 1.36-16.07, p=0.006), and in patients with osteoporosis (OR = 3.46, 95% CI: 1.24-9.58, p=0.02). Hospitalization rates were significantly higher among obese patients (OR = 8.00, 95% CI: 1.75-36.64, p=0.003), those with SMI below 6.75 kg/m2 (OR = 8.70, 95% CI: 2.53-30.30, p=0.0006), patients with DM (OR = 3.22, 95% CI: 1.07-9.67, p=0.04), renal disease (OR = 7.09, 95% CI: 1.56-32.171, p=0.01), and those with OA (OR = 4.34, 95% CI: 1.35-13.98, p=0.01). For neuropathy, likelihood was higher in obese patients (OR = 3.62, 95% CI: 1.20-10.87, p=0.006) and in those with an SMI below 6.75 kg/m2 (OR = 5.92, 95% CI: 2.27-25.38, p=0.0003). No other comorbidity was significantly associated with neuropathy status. Conclusion: Obesity and low SMI significantly increase the risk of adverse chemotherapy outcomes in EBC patients. Notably, specific comorbidities such as HTN, kidney disease, DM, and OA play a crucial role in predicting chemotherapy-related toxicity and complications. Our subanalysis provides a more detailed examination of how individual comorbidities uniquely contribute to chemotherapy tolerance. This underscores the importance of a comprehensive, personalized approach in managing EBC patients, considering not only body composition but also the presence of specific comorbidities. Tailoring interventions to address these factors could improve treatment outcomes and quality of life. Future research should aim to develop and validate personalized interventions in clinical settings to manage these risks better. Citation Format: Jasmin Hundal, Gabriel F. P. Aleixo,, Stephanie A. Valente, Chen, Po-Hao Wei, Halle C. F. Moore. Impact of Obesity, Skeletal Muscle Index, and Comorbidities on Chemotherapy-Related Outcomes in Early Breast Cancer: A Retrospective Subanalysis [abstract]. In: Proceedings of the San Antonio Breast Cancer Symposium 2024; 2024 Dec 10-13; San Antonio, TX. Philadelphia (PA): AACR; Clin Cancer Res 2025;31(12 Suppl):Abstract nr P1-04-01.

Abstract P3-07-19: Survival Outcomes in Breast Cancer with Brain Metastasis Based on Prior Lines of Systemic Therapy for Metastatic Disease

Abstract Background: Breast cancer brain metastases (BCBM) are often associated with short survival. However, specific prognostic factors for patients with BCBM are poorly understood. This single institution retrospective study compares Overall Survival (OS) in patients with BCBM based on number of lines of prior systemic therapy (ST) for metastatic (M) BC at the time of their BCBM diagnosis. Methods: This study included patients with BCBM, diagnosed from Jan 2010 to June 2021. Patients were classified into Early BM [BCBM that developed either de-novo (at the time of MBC diagnosis) or during 1st line of ST] and Late BM (BCBM that developed during or after 2nd lines of ST for MBC). Baseline characteristics were compared using Fisher’s Exact &amp; Wilcoxon Rank-sum tests. OS was estimated using Kaplan Meier methods and compared using Log Rank test. Important covariates were adjusted for using Cox-regression model. Results: 123 BCBM patients were identified and divided into Early BM (n=83) and Late BM (n=40) groups.30% of Early BM were HR+/HER2- vs 60% of Late BM (p=0.005). 47% of Early BM had visceral metastasis vs 75% of Late BM (p=0.0004). Other baseline variables were not significantly different between the groups. Median OS (years, 95% CI) for Early BM was 2.8 (1.6-4.6) vs. 0.5 (0.4-1.5) for Late BM (p=&amp;lt;0.0001). In a Cox regression model, Late BM was associated with significantly higher risk of death vs Early BM [Hazard Ratio (95% CI) 2.246 (1.311-3.850)]. Older age, triple negative subtype (vs HR+/HER2- or HER2+) and worse performance status were also significantly associated with shorter OS. A reduced cox model for all factors significantly associated wtih OS obtained using a backward elimination procedure showed similar HR estimates. Conclusion: This single institution study showed that OS in BCBM patients was significantly associated with number of prior line of systemic therapy for MBC at the time of diagnosis of BM. If validated, these results could provide important prognostic information for BC patients with BM. Citation Format: Harkarandeep Singh, Myla Strawderman, Ruth M. O'Regan, Nimish A. Mohile, Carey K. Anders, Sarah Sammons, Allison Magnuson, Anna Weiss, Ajay Dhakal. Survival Outcomes in Breast Cancer with Brain Metastasis Based on Prior Lines of Systemic Therapy for Metastatic Disease [abstract]. In: Proceedings of the San Antonio Breast Cancer Symposium 2024; 2024 Dec 10-13; San Antonio, TX. Philadelphia (PA): AACR; Clin Cancer Res 2025;31(12 Suppl):Abstract nr P3-07-19.

Previous Lactation Risk Factors Associated with Hyperketonemia in the First Week Postpartum in Dairy Cows: A Retrospective Analysis

Hyperketonemia (HYK) is a common disorder in high-producing dairy cows, resulting in significant economic losses. Defined by elevated beta-hydroxybutyrate (BHB; ≥1.2 mmol/L) without clinical signs, HYK is often considered a gateway disease, predisposing cows to other metabolic and infectious problems. Our objective was to investigate the association between previous lactation risk factors and both BHB concentration and HYK status during the first week postpartum in the subsequent lactation. A retrospective study was conducted using previously collected blood samples from 2336 Holstein multiparous dairy cows from 7 dairy herds, where BHB concentration was measured during the first week postpartum. Data from the previous lactation were extracted from electronic farm records. Log-transformed BHB concentrations and HYK status were each modeled using separate linear mixed models. Both models included the same set of risk factors—lactation, previous lactation total times bred, dry length period, previous lactation days in milk, previous lactation days open, previous lactation days carried calf, previous lactation peak milk production, previous lactation total milk production, previous lactation total milk fat, and previous lactation total milk protein—to investigate their association with these outcomes. Potential confounding variables were offered to the models, and stepwise backward elimination was used to determine which covariates to retain. Significant associations were detected between BHB concentration and dry period length (DDRY), lactation number (LACT), previous lactation total milk protein (TOTP), and previous lactation days open (PDOPN). Inclusive, significant associations were detected between HYK status and previous lactation total milk production (PTOTM), DDRY, LACT, TOTP, and PDOPN. Our results suggest that a dry period longer than 60 days, days open exceeding 130 days, being in their third or greater lactation, and each additional 1000 kg of milk produced in the previous lactation are associated with an increased risk of having high BHB and HYK in the first week postpartum in the subsequent lactation.

Impact of Adenotonsillectomy on Quality of Life in Pediatric Obstructive Sleep Apnoea (OSA): Insights from the OSA-18 Questionnaire

PurposeHypertrophy of the adenoids and tonsils is a common cause of obstructive sleep apnoea (OSA) in children, with adenotonsillectomy as the primary treatment. This study aimed to assess the efficacy of surgical options (adenotonsillectomy, adenoidectomy, and tonsillectomy) in managing pediatric OSA and their impact on quality of life, using the OSA-18 questionnaire.MethodsThis retrospective cohort study analyzed data from parents of 196 children who underwent adenoidectomy, tonsillectomy, or both. The OSA-18 questionnaire was administered online via Google Form to assess quality-of-life issues. Data collection occurred between November 4 and December 25, 2022. Statistical analysis included paired t-tests, ANOVA, Pearson’s correlation, and stepwise linear regression to evaluate pre- and post-surgery differences and associated factors.ResultsA significant improvement in quality of life was observed following adenotonsillectomy, with a mean reduction of 15.14 points in OSA-18 scores. The greatest improvements were noted in the domains of physical symptoms and sleep disturbance, particularly among children with severe OSA. Most participants were male (63%), with an average pre-surgery weight of 25.5 kg. Prior to surgery, 34.18% used CPAP and 56.12% nasal steroids. Post-surgery, 83.16% were hospitalized for 1–2 days, with 4.08% requiring ICU care, and 26.53% experienced postoperative complications.ConclusionSurgical interventions, particularly adenotonsillectomy, significantly improved quality of life in pediatric OSA patients, with marked benefits in severe cases.

Informative Wavelength Selection for Evaluation of Bacterial Spoilage in Raw Salmon (Salmo salar) Fillet Using FT-NIR Spectroscopy

This study highlights the potential of Fourier-transform near-infrared (FT-NIR) spectroscopy for the on-site, nondestructive detection of spoilage caused by bacterial action in raw salmon (Salmo salar) fillets. A stepwise multiple linear regression model with first-derivative spectrum transformation was combined with the standard normal variate and detrend preprocessing techniques. The model achieved correlation values of 0.97 in both the calibration and validation sample sets, with root mean square error values of 0.18 and 0.20 log CFU/mL, respectively. These accurate results reveal the precision of FT-NIR spectroscopy for assessing the spoilage caused by bacteria. The most informative wavelengths (885.27 nm, 1026.27 nm, 1039.93 nm, 1068.38 nm, 1257.55 nm, 1267.75 nm, and 1453.49 nm) related to the total bacterial count’s identification were obtained. The innovative, cost-effective, and feasible approach outlined in this article is a promising methodology for enhancing the safety and quality standards of various fishery products.

Impact of Urban Greenspace Pattern Dynamics on Plant Diversity: A Case Study in Yangzhou, China

Accelerating urbanization leads to the scarcity and fragmentation of greenspaces. Keeping biodiversity alive, i.e., enhancing greenspaces’ impacts on plant diversity in and around urban areas, is essential. This study evaluated greenspace patterns (GSPs) using landscape metrics, and calculated plant α- and β-diversity using field surveys. Bivariate correlation analysis was used to analyze the correlations among plant α- and β-diversity and landscape metrics from 2009 to 2022. Significant models were selected using stepwise regression analysis and verified by comparing fitted and field values. The results indicate that α-diversity was primarily influenced by the number of patches, wetland landscape shape index and patch richness density, imperviousness of surfaces, and forest and grassland at the 100–1000 m scale. The correlation between GSPs and α-diversity weakened with an increase in scale. Current patch richness density, Shannon’s diversity index, Shannon’s evenness index, and percentage of impervious surface and wetland significantly influenced β-diversity at the 100–300 m scale. By contrast, β-diversity was influenced by greenspace patterns at the 300–1000 m scale. There was an observed positive correlation between GSPGSPs and β-diversity that strengthened as the scale increased. These findings highlight the scale-dependent legacy effects of GSPs on plant diversity, primarily driven by the landscape pattern characteristics of urban greenspaces and the diversity of plant groups. Therefore, prioritizing the protection of large green patches and establishing designated protected areas or points for on-site conservation are crucial strategies for urban plant diversity conservation.

Comprehensive conditional survival analysis of pancreatic signet ring cell carcinoma: chemotherapy’s role and predictive model development using the SEER database

BackgroundPancreatic signet ring cell carcinoma (PSRCC) is a rare and aggressive subtype of pancreatic cancer, with a poor prognosis and limited evidence on the survival benefit of chemotherapy. From the perspective of conditional survival (CS) prognosis, this study sought to assess the effect of chemotherapy on PSRCC survival and to construct a predictive model integrating CS analysis.MethodsUsing the SEER database, 708 PSRCC patients diagnosed between 2000 and 2019 were analyzed. Propensity score matching (PSM) and Kaplan-Meier curves were employed to assess chemotherapy’s impact on survival. The CS analysis was performed to evaluate dynamic survival probabilities. A nomogram was developed based on key prognostic factors identified through random survival forests (RSF), least absolute shrinkage and selection operator (LASSO) regression, and multivariate Cox analysis with a stepwise backward elimination procedure. And multiple evaluation methods were employed to assess the performance of the nomogram.ResultsThe CS analysis for all cohort showed a rapid decline in survival probability within the first few years, dropping to 18% by year 1, 5% by year 3 and 3% by year 5. Chemotherapy improved short-term survival, with a 30% one-year survival rate compared to 8% in the non-chemotherapy group. However, long-term survival probabilities converged after the first year. Key prognostic factors included age, tumor size, stage, site, surgery, and chemotherapy were identified to develop a CS-integrated nomogram. And the nomogram was found to have strong predictive accuracy and clinical utility, validated by calibration, ROC, and decision curve analyses.ConclusionChemotherapy offered significant early survival benefits in PSRCC, although its long-term impact is limited. The developed nomogram provided a reliable tool for personalized survival prediction, with further validation needed in prospective studies.

Development and validation of a risk score to predict neonatal mortality among NICU admissions in Southern Ethiopia: a retrospective follow-up study

BackgroundThe World Health Organization reported 2.6 million neonatal deaths in 2016, accounting for nearly 46% of all under-five deaths globally. Ethiopia is among the top 10 countries with the highest neonatal mortality, with an estimated 122,000 newborn deaths annually. This study aimed to develop and validate a risk score to predict neonatal mortality.MethodsWe conducted a retrospective follow-up study among 845 neonates admitted tot Hawassa University Comprehensive Specialized Hospital, Southern Ethiopia. Data were entered into EpiData version 4.6 and analyzed using R version 4.0.5. Variables with p < 0.25 in the bivariable analysis were entered into the multivariable model. A stepwise backward elimination technique with p < 0.1 for the likelihood ratio test to fit the reduced model. Finally, variables with p < 0.05 were considered statistically significant.ResultsOf the 845 neonates included in the study, 130 died, resulting in a neonatal mortality incidence proportion of 15.4% (95% CI: 13%, 17%). Seven variables, namely, residence, primigravida, low birth weight, amniotic fluid status, Apgar score, perinatal asphyxia, and breastfeeding, were included in the model. The AUC of the final reduced validated model was 0.781 (95% CI: 0.73, 0.82). The accuracy of the model was also assessed by calibration and resulted in a p-value of 0.781. The model had a sensitivity and specificity of 80% and 66%, respectively. Decision curve analysis of the model provides a higher net benefit across ranges of threshold probabilities.ConclusionWe constructed and internally validated a prediction model with good performance. This model is feasible and applicable in healthcare settings to reducing neonatal mortality and improving overall maternal and child healthcare.

Optimization of Prediction for Cancellation of Hotel Room Reservation Using Decision Tree with Feature Selection and Resampling

The hotel industry is highly competitive and faces challenges, such as fluctuating demand, intense competition, and shifting consumer preferences. One critical issue that hotels frequently encounter is the cancellation of room reservations, which disrupts operational planning and resource management and leads to significant financial losses. Accurately predicting the likelihood of reservation cancellation is essential to mitigate these negative impacts and optimize revenue management strategies. This study focuses on the development of a predictive model for hotel room reservation cancellations using a decision-tree algorithm. The Decision Tree was selected for its ability to manage complex relationships between variables and ease of interpretation, making it accessible to hotel managers without technical expertise. To enhance the performance of the model, a forward selection technique was employed to identify the most relevant features, ensuring a balance between the model complexity and predictive accuracy. Additionally, resampling techniques were applied to address class imbalance in the dataset, which is common in cancellation cases where non-cancelled reservations outnumber cancelled reservations. This study explores the prediction of hotel room reservation cancellations using a decision tree algorithm enhanced by feature selection and resampling. The model achieved an accuracy improvement to 90%, with precision and recall each increasing by 5,5% after applying these techniques. These findings suggest practical applications for improving cancellation predictions and optimizing revenue management strategies for hotels. The study provides insights into how data-driven approaches can enhance decision-making processes within the competitive hospitality industry.

Telehealth Utilization During COVID-19: An Examination Among Young Adults Using Andersen's Behavioral Model of Health Care Utilization.

Young adulthood is a critical time for understanding mental health needs and young adults experience adverse symptoms at alarmingly increased rates. Following COVID-19, telehealth services came to the forefront of care for all ages. Despite increased use of telehealth services for behavioral health needs, a gap remains between service need and service use among young adults. Informed by Andersen's Behavioral Model of Health Care Utilization, the current study examined theoretically related factors for telehealth service utilization among young adults. Data were from the 2021 National Survey of Drug Use and Health. Participants were ages 18 to 25years old (N = 13,979). Predictors included predisposing factors (sociodemographic characteristics), enabling factors (income, geographic location, insurance, and government assistance), and need factors (health, mental health, and substance use). A forward selection logistic regression was used to determine their impact on past-year telehealth use. Findings revealed factors associated with increased likelihood of telehealth use, including being female, being older, enrolled in school, being employed, earning over $75,000 per year, living in a metropolitan area, and having mental health, substance use, or health concerns. In contrast, identifying as non-white, being unemployed, earning between $20k-$74k, being on government assistance, or having insurance were associated with a decreased likelihood of telehealth use. Findings reveal important disparities and highlight the ongoing need to address structural and systemic barriers in telehealth access. Implications for practice and policy include expanding digital access, ensuring insurance flexibility that supports telehealth services, and investing in culturally responsive care models and training.

Identifying health disparities in lenalidomide access.

IntroductionLenalidomide is dispensed through a limited distribution network, leading to longer prescription fill times. Acquiring lenalidomide involves navigating the risk evaluation and mitigation strategies (REMS) program, insurance prior authorization, and financial assistance for out-of-pocket costs. Health disparities, including socioeconomic status, health literacy, language barriers, and geography, can further delay access. This study aimed to identify and analyze factors contributing to delays in access to lenalidomide.MethodsThis retrospective study used electronic health records within the Brown University Health system to identify adults newly diagnosed with hematologic malignancies between June 1, 2019 and June 1, 2024, with a lenalidomide prescription. A stepwise multiple linear regression evaluated factors affecting dispensation time. The primary endpoint was to identify factors contributing to delays in lenalidomide acquisition from a health equity perspective. The secondary endpoint was to determine the median time from prescription generation to dispensation.ResultsWe included 328 patients with a mean age of 71 years; 53% were male and 80% were White. Comorbidities included cardiovascular disease (53%), diabetes (15%), obesity (21%), respiratory disease (8%), and chronic kidney disease (7%). Eighteen percent of patients required transportation assistance. The primary endpoint revealed 54% required financial assistance, which was associated with delayed lenalidomide dispensation (P < 0.05). The secondary endpoint showed a median time of 8 days (IQR, 5-14) to dispense the prescription.ConclusionThis study found that lenalidomide prescriptions took a median of 8 days to fill, with those requiring financial assistance experiencing longer delays. These findings suggest that insurance processes and health disparities hinder timely access.

Gender and tenure predict Filipino science teachers’ instrumentation skills

Instrumentation skills coupled with appropriate pedagogical skills are important in the science classroom. This study examined the instrumentation skills of science teachers from public urban schools representing the three major island groups in the Philippines. Using post-positivist descriptive-correlational survey research, a total of 200 science teachers served as the respondents of this study. A valid (CVI=0.83) and reliable (α=0.98) researcher-made instrument was used to gather data. Stepwise multiple linear regression was used to analyze the data. The results suggest that gender and tenure are significant predictors of instrumentation skills among secondary science teachers. Workshops emphasizing practical hands-on experiences, focusing on female teachers who have stayed longer in the service, are recommended.

Asymmetric Knee Joint Loading in Post-Stroke Gait: A Musculoskeletal Modeling Analysis of Medial and Lateral Compartment Forces

Background/Objectives: Stroke survivors often develop asymmetric gait patterns that may lead to abnormal knee joint loading and potentially increased risk of osteoarthritis. This study aimed to investigate differences in knee joint loading between paretic and non-paretic limbs during walking in individuals post-stroke. Methods: Twenty-one chronic stroke survivors underwent three-dimensional gait analysis. A modified musculoskeletal model with a specialized knee mechanism was used to estimate medial and lateral tibiofemoral contact forces during the stance phase. Statistical parametric mapping was used to identify significant differences in joint kinematics, kinetics, and contact forces between limbs. Stepwise regression analyses examined relationships between knee moments and compartmental contact forces. Results: Significant differences in knee loading were observed between limbs, with the non-paretic limb experiencing higher medial compartment forces during early stance (6.7–15.1%, p = 0.001; 21.9–30.7%, p = 0.001) and late stance (72.3–93.7%, p &lt; 0.001), and higher lateral compartment forces were recorded during pre-swing (86.2–99.0%, p &lt; 0.001). In the non-paretic limb, knee extensor moment was the primary predictor of first peak medial contact force (R2 = 0.573), while knee abductor moment was the primary predictor in the paretic limb (R2 = 0.559). Conclusions: Musculoskeletal modeling revealed distinct asymmetries in knee joint loading between paretic and non-paretic limbs post-stroke, with the non-paretic limb experiencing consistently higher loads, particularly during late stance. These findings suggest that rehabilitation strategies should address not only paretic limb function but also potentially harmful compensatory mechanisms in the non-paretic limb to prevent long-term joint degeneration.