Processing imbalanced medical data at the data level with assisted-reproduction data as an example.

OBJECTIVE: Data imbalance is a pervasive issue in medical data mining, often leading to biased and unreliable predictive models. This study aims to address the urgent need for effective strategies to mitigate the impact of data imbalance on classification models. We focus on quantifying the effects of different imbalance degrees and sample sizes on model performance, identifying optimal cut-off values, and evaluating the efficacy of various methods to enhance model accuracy in highly imbalanced and small sample size scenarios. METHODS: We collected medical records of patients receiving assisted reproductive treatment in a reproductive medicine center. Random forest was used to screen the key variables for the prediction target. Various datasets with different imbalance degrees and sample sizes were constructed to compare the classification performance of logistic regression models. Metrics such as AUC, G-mean, F1-Score, Accuracy, Recall, and Precision were used for evaluation. Four imbalance treatment methods (SMOTE, ADASYN, OSS, and CNN) were applied to datasets with low positive rates and small sample sizes to assess their effectiveness. RESULTS: The logistic model's performance was low when the positive rate was below 10% but stabilized beyond this threshold. Similarly, sample sizes below 1200 yielded poor results, with improvement seen above this threshold. For robustness, the optimal cut-offs for positive rate and sample size were identified as 15% and 1500, respectively. SMOTE and ADASYN oversampling significantly improved classification performance in datasets with low positive rates and small sample sizes. CONCLUSIONS: The study identifies a positive rate of 15% and a sample size of 1500 as optimal cut-offs for stable logistic model performance. For datasets with low positive rates and small sample sizes, SMOTE and ADASYN are recommended to improve balance and model accuracy.

Corrigendum: Analysis of factors associated with positive surgical margins and the five-year survival rate after prostate cancer resection and predictive modeling.

[This corrects the article DOI: 10.3389/fonc.2024.1360404.].

Integrative prediction model for radiation pneumonitis incorporating genetic and clinical-pathological factors using machine learning.

Purpose: We aimed to develop a machine learning-based prediction model for severe radiation pneumonitis (RP) by integrating relevant clinicopathological and genetic factors, considering the associations of clinical, dosimetric parameters, and single nucleotide polymorphisms (SNPs) of genes in the TGF-ß1 pathway with RP. Methods: We prospectively enrolled 59 primary lung cancer patients undergoing radiotherapy and analyzed pretreatment blood samples, clinicopathological/dosimetric variables, and 11 functional SNPs in TGFß pathway genes. Using the Synthetic Minority Over-sampling Technique (SMOTE) and nested cross-validation, we developed a machine learning-based prediction model for severe RP (grade ≥ 2). Feature selection was conducted using four methods (filtered-based, wrapper-based, embedded, and logistic regression), and performance was evaluated using three machine learning models. Results: Severe RP occurred in 20.3 % of patients with a median follow-up of 39.7 months. In our final model, age (>66 years), smoking history, PTV volume (>300 cc), and AG/GG genotype in BMP2 rs1979855 were identified as the most significant predictors. Additionally, incorporating genomic variables for prediction alongside clinicopathological variables significantly improved the AUC compared to using clinicopathological variables alone (0.822 vs. 0.741, p = 0.029). The same feature set was selected using both the wrapper-based method and logistic model, demonstrating the best performance across all machine learning models (AUC: XGBoost 0.815, RF 0.805, SVM 0.712, respectively). Conclusion: We successfully developed a machine learning-based prediction model for RP, demonstrating age, smoking history, PTV volume, and BMP2 rs1979855 genotype as significant predictors. Notably, incorporating SNP data significantly enhanced predictive performance compared to clinicopathological factors alone.

Efficient Privacy-preserving Logistic Model With Malicious Security.

Conducting secure computations to protect against malicious adversaries is an emerging field of research. Current models designed for malicious security typically necessitate the involvement of two or more servers in an honest-majority setting. Among privacy-preserving data mining techniques, significant attention has been focused on the classification problem. Logistic regression emerges as a well-established classification model, renowned for its impressive performance. We introduce a novel matrix encryption method to build a maliciously secure logistic model. Our scheme involves only a single semi-honest server and is resilient to malicious data providers that may deviate arbitrarily from the scheme. The d -transformation ensures that our scheme achieves indistinguishability (i.e., no adversary can determine, in polynomial time, which of the plaintexts corresponds to a given ciphertext in a chosen-plaintext attack). Malicious activities of data providers can be detected in the verification stage. A lossy compression method is implemented to minimize communication costs while preserving negligible degradation in accuracy. Experiments illustrate that our scheme is highly efficient to analyze large-scale datasets and achieves accuracy similar to non-private models. The proposed scheme outperforms other maliciously secure frameworks in terms of computation and communication costs.

A prediction model for secondary invasive fungal infection among severe SARS-CoV-2 positive patients in ICU.

Background: The global COVID-19 pandemic has resulted in over seven million deaths, and IFI can further complicate the clinical course of COVID-19. Coinfection of COVID-19 and IFI (secondary IFI) pose significant threats not only to healthcare systems but also to patient lives. After the control measures for COVID-19 were lifted in China, we observed a substantial number of ICU patients developing COVID-19-associated IFI. This creates an urgent need for predictive assessment of COVID-19 patients in the ICU environment for early detection of suspected fungal infection cases. Methods: This study is a single-center, retrospective research endeavor. We conducted a case-control study on severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) positive patients. The cases consisted of patients who developed any secondary IFI during their ICU stay at Jilin University China-Japan Union Hospital in Changchun, Jilin Province, China, from December 1st, 2022, to August 31st, 2023. The control group consisted of SARS-CoV-2 positive patients without secondary IFI. Descriptive and comparative analyses were performed, and a logistic regression prediction model for secondary IFI in COVID-19 patients was established. Additionally, we observed an increased incidence of COVID-19-associated pulmonary aspergillosis (CAPA) during this pandemic. Therefore, we conducted a univariate subgroup analysis on top of IFI, using non-CAPA patients as the control subgroup. Results: From multivariate analysis, the prediction model identified 6 factors that are significantly associated with IFI, including the use of broad-spectrum antibiotics for more than 2 weeks (aOR=4.14, 95% CI 2.03-8.67), fever (aOR=2.3, 95%CI 1.16-4.55), elevated log IL-6 levels (aOR=1.22, 95% CI 1.04-1.43) and prone position ventilation (aOR=2.38, 95%CI 1.15-4.97) as independent risk factors for COVID-19 secondary IFI. High BMI (BMI ≥ 28 kg/m2) (aOR=0.85, 95% CI 0.75-0.94) and the use of COVID-19 immunoglobulin (aOR=0.45, 95% CI 0.2-0.97) were identified as independent protective factors against COVID-19 secondary IFI. The Receiver Operating Curve (ROC) area under the curve (AUC) of this model was 0.81, indicating good classification. Conclusion: We recommend paying special attention for the occurrence of secondary IFI in COVID-19 patients with low BMI (BMI < 28 kg/m2), elevated log IL-6 levels and fever. Additionally, during the treatment of COVID-19 patients, we emphasize the importance of minimizing the duration of broad-spectrum antibiotic use and highlight the potential of immunoglobulin application in reducing the incidence of IFI.

An augmented illness-death model for semi-competing risks with clinically immediate terminal events.

Preeclampsia is a pregnancy-associated condition posing risks of both fetal and maternal mortality and morbidity that can only resolve following delivery and removal of the placenta. Because in its typical form preeclampsia can arise before delivery, but not after, these two events exemplify the time-to-event setting of "semi-competing risks" in which a non-terminal event of interest is subject to the occurrence of a terminal event of interest. The semi-competing risks framework presents a valuable opportunity to simultaneously address two clinically meaningful risk modeling tasks: (i) characterizing risk of developing preeclampsia, and (ii) characterizing time to delivery after onset of preeclampsia. However, some people with preeclampsia deliver immediately upon diagnosis, while others are admitted and monitored for an extended period before giving birth, resulting in two distinct trajectories following the non-terminal event, which we call "clinically immediate" and "non-immediate" terminal events. Though such phenomena arise in many clinical contexts, to-date there have not been methods developed to acknowledge the complex dependencies between such outcomes, nor leverage these phenomena to gain new insight into individualized risk. We address this gap by proposing a novel augmented frailty-based illness-death model with a binary submodel to distinguish risk of immediate terminal event following the non-terminal event. The model admits direct dependence of the terminal event on the non-terminal event through flexible regression specification, as well as indirect dependence via a shared frailty term linking each submodel. We develop an efficient Bayesian sampler for estimation and corresponding model fit metrics, and derive formulae for dynamic risk prediction. In an extended example using pregnancy outcome data from an electronic health record, we demonstrate the proposed model's direct applicability to address a broad range of clinical questions.

Socio-economic inequalities in the use of flu vaccination in Europe: a multilevel approach.

BACKGROUND: The European-wide statistics show that the use of flu vaccination remains low and the differences between countries are significant, as are those between different population groups within each country. Considerable research has focused on explaining vaccination uptake in relation to socio-economic and demographic characteristics, health promotion and health behavior factors. Nevertheless, few studies have aimed to analyze between-country differences in the use of flu vaccination for the EU population. To address this gap, this study examines the socio-economic inequalities in the use of influenza vaccination for the population aged 15 years and over in all 27 EU Member States and two other non-EU countries (Iceland and Norway). METHODS: Using data from the third wave of European Health Interview Survey (EHIS) 2019, we employed a multilevel logistic model with a random intercept for country, which allows controlling simultaneously the variations in individuals' characteristics and macro-contextual factors which could influence the use of flu vaccination. In addition, the analysis considers the population stratified into four age groups, namely adolescents, young adults, adults and elderly, to better capture heterogeneities in flu vaccination uptake. RESULTS: The main findings confirm the existence of socio-economic inequalities between individuals in different age groups, but also of significant variation between European countries, particularly for older people, in the use of influenza vaccination. In this respect, income and education are strong proxy of socio-economic status associated with flu vaccination uptake. Moreover, these disparities within each population group are also explained by area of residence and occupational status. Particularly for the elderly, the differences between individuals in vaccine utilization are also explained by country-level factors, such as the type of healthcare system adopted in each country, public funding, personal health expenditure burden, or the availability of generalist practitioners. CONCLUSIONS: Overall, our findings reveal that vaccination against seasonal influenza remains a critical public health intervention and bring attention to the relevance of conceiving and implementing context-specific strategies to ensure equitable access to vaccines for all EU citizens.

Socio-ecological predictors of dengue in high incidence area of Delhi, India.

Despite the amplified vector-control measures, sporadic-epidemic outbreaks of dengue incidence occurred in Delhi, intermittently. This study aimed to identify the major individual, household, and community level predictors of dengue cases in the hot-spots of Delhi. Primary survey data was collected from the selected sample of 347 households, at the South- West district of Delhi. This survey has interviewed the head of the household to collect the information of dengue incidences within last one year and household information related to socio-economic, demographic, environmental factors, such as housing pattern, density, water storage containers, drainage and garbage collection site and method, mosquito protection measures and awareness. Among 347 households, 54 households had reported dengue cases, and 69 individuals had reported dengue cases in last one year. Garbage and water collection site and methods, drainage and household type, household monthly income, indoor bamboo plants, construction site (within 500 m), presence of tertiary care hospital, were the significant predictors of dengue incidences in Delhi. In conclusion, strategic control measures and intense social interventions such as household and community awareness, promotion of healthy practices should be promoted to control the dengue incidences.

Assessing Marginal Effects of Non-Pharmaceutical Interventions on the Transmission of SARS-CoV-2 across Africa: A Hybrid Modeling Study.

Since 2019, a new strain of coronavirus has challenged global health systems. Due its fragile healthcare systems, Africa was predicted to be the most affected continent. However, past experiences of African countries with epidemics and other factors, including actions taken by governments, have contributed to reducing the spread of SARS-CoV-2. This study aims to assess the marginal impact of non-pharmaceutical interventions in fifteen African countries during the pre-vaccination period. To describe the transmission dynamics and control of SARS-CoV-2 spread, an extended time-dependent SEIR model was used. The transmission rate of each infectious stage was obtained using a logistic model with NPI intensity as a covariate. The results revealed that the effects of NPIs varied between countries. Overall, restrictive measures related to assembly had, in most countries, the largest reducing effects on the pre-symptomatic and mild transmission, while the transmission by severe individuals is influenced by privacy measures (more than 10%). Countries should develop efficient alternatives to assembly restrictions to preserve the economic sector. This involves, e.g. training in digital tools and strengthening digital infrastructures.

Utilizing Bayesian inference in accelerated testing models under constant stress via ordered ranked set sampling and hybrid censoring with practical validation.

This research investigates the application of the ordered ranked set sampling (ORSSA) procedure in constant-stress partially accelerated life-testing (CSPALTE). The study adopts the assumption that the lifespan of a specific item under operational stress follows a half-logistic probability distribution. Through Bayesian estimation methods, it concentrates on estimating the parameters, utilizing both asymmetric loss function and symmetric loss function. Estimations are conducted using ORSSAs and simple random samples, incorporating hybrid censoring of type-I. Real-world data sets are utilized to offer practical context and validate the theoretical discoveries, providing concrete insights into the research findings. Furthermore, a rigorous simulation study, supported by precise numerical calculations, is meticulously conducted to gauge the Bayesian estimation performance across the two distinct sampling methodologies. This research ultimately sheds light on the efficacy of Bayesian estimation techniques under varying sampling strategies, contributing to the broader understanding of reliability analysis in CSPALTE scenarios.

FastBiCmrMLM: a fast and powerful compressed variance component mixed logistic model for big genomic case-control genome-wide association study.

Large sample datasets have been regarded as the primary basis for innovative discoveries and the solution to missing heritability in genome-wide association studies. However, their computational complexity cannot consider all comprehensive effects and all polygenic backgrounds, which reduces the effectiveness of large datasets. To address these challenges, we included all effects and polygenic backgrounds in a mixed logistic model for binary traits and compressed four variance components into two. The compressed model combined three computational algorithms to develop an innovative method, called FastBiCmrMLM, for large data analysis. These algorithms were tailored to sample size, computational speed, and reduced memory requirements. To mine additional genes, linkage disequilibrium markers were replaced by bin-based haplotypes, which are analyzed by FastBiCmrMLM, named FastBiCmrMLM-Hap. Simulation studies highlighted the superiority of FastBiCmrMLM over GMMAT, SAIGE and fastGWA-GLMM in identifying dominant, small α (allele substitution effect), and rare variants. In the UK Biobank-scale dataset, we demonstrated that FastBiCmrMLM could detect variants as small as 0.03% and with α ≈ 0. In re-analyses of seven diseases in the WTCCC datasets, 29 candidate genes, with both functional and TWAS evidence, around 36 variants identified only by the new methods, strongly validated the new methods. These methods offer a new way to decipher the genetic architecture of binary traits and address the challenges outlined above.

Analysis of factors associated with positive surgical margins and the five-year survival rate after prostate cancer resection and predictive modeling.

Background: This study analyzed the risk factors associated with positive surgical margins (PSM) and five-year survival after prostate cancer resection to construct a positive margin prediction model. Methods: We retrospectively analyzed the clinical data of 148 patients treated with prostatectomy. The patients were divided into PSM group and Negative surgical margins (NSM) group. Several parameters were compared between the groups. All patients were followed up for 60 months. The risk factors for PSM and five-year survival were evaluated by univariate analysis, followed by multifactorial dichotomous logistic regression analysis. Finally, ROC curves were plotted for the risk factors to establish a predictive model for PSM after prostate cancer resection. Results: (1) Serum PSA, percentage of positive puncture stitches, clinical stage, surgical approach, Gleason score on puncture biopsy, and perineural invasion were significantly associated with the risk of PSM (P < 0.05). Serum PSA, perineural invasion, Gleason score on puncture biopsy, and percentage of positive puncture stitches were independent risk factors for PSM. (2) Total prostate-specific antigen (tPSA) by puncture, nutritional status, lymph node metastasis, bone metastasis, and seminal vesicle invasion may be risk factors for five-year survival. Lymph node metastasis and nutritional status were the main risk factors for the five-year survival of patients with prostate cancer. (3) After plotting the ROC curve, the area under the curve (AUC) [AUC: 0.776, 95%, confidence interval (CI): 0.725 to 0.854] was found to be a valid predictor of PSM; the AUC [AUC: 0.664, 95%, confidence interval (CI): 0.576 to 0.753] was also a valid predictor of five-year survival (P < 0.05). (4) The scoring system had a standard error of 0.02 and a cut-off value of 6. It predicted PSM after prostate cancer resection with moderate efficacy. Conclusions: Serum PSA, perineural invasion, puncture biopsy Gleason score, and percentage of positive puncture stitches were independent risk factors for positive surgical margins (PSM). Also, lymph node metastasis and nutritional status were the main risk factors for the five-year survival of patients with prostate cancer. Overall, the prediction efficacy of this scoring system concerning the risk of PSM after prostate cancer resection was moderate.

D-optimal designs for two-variable logistic regression model with restricted design space.

The problem of constructing locally D-optimal designs for two-variable logistic model with no interaction has been studied in many literature. In Kabera, Haines, and Ndlovu (2015), the model is restricted to have positive slopes and negative intercept for the assumptions that the probability of response increases with doses for both drugs and that the probability of response is less than 0.5 at zero dose level of both drugs. The design space mainly discussed is the set [0, ∞) × [0, ∞), while the finite rectangular design space is presented only in scenarios where the results for the unlimited design space are still appropriate. In this paper, we intend to loose these restrictions and discuss the rectangular design spaces for the model where the D-optimal designs can not be obtained. The result can be extended to the models where drugs have negative or opposite effects, or the models with positive intercept, by using translation and reflection in the first quadrant.

Regional perinatal mortality differences in Indonesia: Evidence from Indonesian demographic health survey.

Aim: Perinatal mortality can be used as a reference to assess health status in a country. In Indonesia, none of previous studies specifically discuss the incidence of prenatal mortality by region. The objective of this study was to analyze perinatal mortality difference by region of Indonesia. Study design: This study used a cross-sectional approach. Method: The sample in this study was 13,310 women of childbearing age obtained from the Indonesian Demographic Health Survey (IDHS) 2017. The perinatal mortality rate was calculated using data on stillbirths with a gestational duration of seven months or more and early neonatal deaths. Perinatal mortality was analyzed by region using a binary logistic regression statistical test to examine the relationship between perinatal mortality and its factors (socio-demographic factors, individual disease control factors, and maternal factors). Results: This study shows that the proportion of perinatal mortality in Indonesia is 1.5 % of total births. The highest proportion of perinatal mortality (2.5 %) was in the Papua region, while the lowest proportion (1.3 %) was in the Java region. The results of this study indicated that women in the Maluku Islands had a 1.82 times higher chance of perinatal mortality compared to the Java-Bali region. The causative variable associated with perinatal mortality in the Java-Bali and Papua regions was employment status. The causative variables associated with perinatal mortality in Kalimantan were the quality of antenatal care and delivery assistance. The causative variable associated with perinatal mortality in Nusa Tenggara and Papua was the location of delivery. The causative variable associated with perinatal mortality in Kalimantan, Maluku, and Papua was the mother's age. The causative variable associated with perinatal mortality in the Java-Bali region was parity. The causative variable associated with perinatal mortality in Sumatra was the type of delivery. Conclusion: This study show that there were disparities in the incidence of perinatal mortality between regions in Indonesia. The government needs to re-adjust the existing strategies to improve health status and focus on community empowerment for women to prevent perinatal mortality.

Sympathetic 123I-metaiodobenzylguanidine index for Lewy body disease: probability-based diagnosis and identifying patients exempt from late imaging.

OBJECTIVE: We aimed to establish a practical diagnostic index for Lewy body diseases (LBD), such as Parkinson's disease and dementia, with Lewy bodies in outpatient settings and criteria for exempting patients from late imaging. METHODS: We acquired early and late 123I-metaiodobenzylguanidine (MIBG) images from 108 consecutive patients with suspected LBD and standardized heart-to-mediastinum (H/M) ratios for collimator conditions. Exclusions included young-onset Parkinson's disease (age < 50 years) and genetic transthyretin-type amyloidosis. We developed logistic models incorporating H/M ratios with or without age (n = 92). The sympathetic MIBG index for LBD (SMILe index), categorized LBD likelihood from 0 (lowest) to 1 (highest). Diagnostic accuracy was assessed as the area under the receiver operating characteristic (ROC) curve (AUC). The characteristics of the new index were compared with H/M ratios. The need for late imaging was explored using the SMILe index. RESULTS: Early or late SMILe indexes using a single H/M ratio variable discriminated LBD from non-LBD. The AUC values for early and late SMILe indexes were 0.880 and 0.894 (p < 0.0001 for both), identical to those for early and late H/M ratios. The sensitivity and the specificity of early SMILe indexes with a 0.5 threshold were 76% and 90%, achieving accuracy of accuracy 86%. Similarly, the late SMILe index demonstrated a sensitivity of 76% and specificity of 87%, with an accuracy of 84%. Early SMILe indexes < 0.3 or > 0.7 (representing 84% patients) indicated a diagnosis without a late MIBG study. CONCLUSION: The 123I-MIBG-derived SMILe indexes provide likelihood of LBD, and those with a 50% threshold demonstrated optimal diagnostic accuracy for LBD. The index values of either < 0.3 or > 0.7 accurately selected patients who do not need late imaging.

A novel multivariate logistic model for predicting risk factors of failed treatment with carbapenem-resistant Acinetobacter baumannii ventilator-associated pneumonia.

Background: This study aimed to explore the risk factors for failed treatment of carbapenem-resistant Acinetobacter baumannii ventilator-associated pneumonia (CRAB-VAP) with tigecycline and to establish a predictive model to predict the incidence of failed treatment and the prognosis of CRAB-VAP. Methods: A total of 189 CRAB-VAP patients were included in the safety analysis set from two Grade 3 A national-level hospitals between 1 January 2022 and 31 December 2022. The risk factors for failed treatment with CRAB-VAP were identified using univariate analysis, multivariate logistic analysis, and an independent nomogram to show the results. Results: Of the 189 patients, 106 (56.1%) patients were in the successful treatment group, and 83 (43.9%) patients were in the failed treatment group. The multivariate logistic model analysis showed that age (OR = 1.04, 95% CI: 1.02, 1.07, p = 0.001), yes. of hypoproteinemia (OR = 2.43, 95% CI: 1.20, 4.90, p = 0.013), the daily dose of 200 mg (OR = 2.31, 95% CI: 1.07, 5.00, p = 0.034), yes. of medication within 14 days prior to surgical intervention (OR = 2.98, 95% CI: 1.19, 7.44, p = 0.019), and no. of microbial clearance (OR = 0.31, 95% CI: 0.14, 0.70, p = 0.005) were risk factors for the failure of tigecycline treatment. Receiver operating characteristic (ROC) analysis showed that the AUC area of the prediction model was 0.745 (0.675-0.815), and the decision curve analysis (DCA) showed that the model was effective in clinical practice. Conclusion: Age, hypoproteinemia, daily dose, medication within 14 days prior to surgical intervention, and microbial clearance are all significant risk factors for failed treatment with CRAB-VAP, with the nomogram model indicating that high age was the most important factor. Because the failure rate of CRAB-VAP treatment with tigecycline was high, this prediction model can help doctors correct or avoid risk factors during clinical treatment.

Statistical Models for High-Risk Intestinal Metaplasia with DNA Methylation Profiling.

We consider the newly developed multinomial mixed-link models for a high-risk intestinal metaplasia (IM) study with DNA methylation data. Different from the traditional multinomial logistic models commonly used for categorical responses, the mixed-link models allow us to select the most appropriate link function for each category. We show that the selected multinomial mixed-link model (Model 1) using the total number of stem cell divisions (TNSC) based on DNA methylation data outperforms the traditional logistic models in terms of cross-entropy loss from ten-fold cross-validations with significant p-values 8.12×10-4 and 6.94×10-5. Based on our selected model, the significance of TNSC's effect in predicting the risk of IM is justified with a p-value less than 10-6. We also select the most appropriate mixed-link models (Models 2 and 3) when an additional covariate, the status of gastric atrophy, is available. When the status is negative, mild, or moderate, we recommend Model 2; otherwise, we prefer Model 3. Both Models 2 and 3 can predict the risk of IM significantly better than Model 1, which justifies that the status of gastric atrophy is informative in predicting the risk of IM.

Maize grain filling characteristics in China: Response to meteorological factors.

To investigate the dynamic changes in dry matter accumulation in maize after anthesis, we established a logistic model to describe grain filling characteristics (GFC), and analyzed differences between spring and summer maize, and the influence of meteorological factors. The results showed that the logistic model accurately simulated the dynamic changes in grain growth. For spring maize, the fitted hundred-grain weight at maturity was closely related to the average grain filling rate until maturity, days of the active grain filling period, time of the maximum grain filling rate, and duration of the rapid increase in grain weight. For summer maize, it was closely related to the time of the maximum grain filling rate, days of active grain filling period, duration of gradual grain weight, and the rapidly increasing period. The filling characteristics of spring and summer maize differed because of the different meteorological conditions and biological characteristics. The grain filling duration of spring maize was longer than that of summer maize. The maximum grain filling rate of spring maize occurred later than that of summer maize. Temperature and precipitation were the main meteorological factors affecting the hundred-grain weight of spring maize, whereas temperature was the main factor affecting summer maize. The response of spring maize GFC to meteorological factors was more complex than that of summer maize. These results are important for the development of appropriate strategies for improving maize productivity in China.

Clinical T1/2 renal cell carcinoma: multiparametric dynamic contrast-enhanced MRI features-based model for the prediction of individual adverse pathology.

BACKGROUND: The detection of renal cell carcinoma (RCC) has been rising due to the enhanced utilization of cross-sectional imaging and incidentally discovered lesions with adverse pathology demonstrate potential for metastasis. The purpose of our study was to determine the clinical and multiparametric dynamic contrast-enhanced magnetic resonance imaging (CEMRI) associated independent predictors of adverse pathology for cT1/2 RCC and develop the predictive model. METHODS: We recruited 105 cT1/2 RCC patients between 2018 and 2022, all of whom underwent preoperative CEMRI and had complete clinicopathological data. Adverse pathology was defined as RCC patients with nuclear grade III-IV; pT3a upstage; type II papillary RCC, collecting duct or renal medullary carcinoma, unclassified RCC; sarcomatoid/rhabdoid features. The qualitative and quantitative CEMRI parameters were independently reviewed by two radiologists. Univariate and multivariate binary logistic regression analyses were utilized to determine the independent predictors of adverse pathology for cT1/2 RCC and construct the predictive model. The receiver operating characteristic (ROC) curve, confusion matrix, calibration plot, and decision curve analysis (DCA) were conducted to compare the diagnostic performance of different predictive models. The individual risk scores and linear predicted probabilities were calculated for risk stratification, and the Kaplan-Meier curve and log-rank tests were used for survival analysis. RESULTS: Overall, 45 patients were pathologically confirmed as RCC with adverse pathology. Clinical characteristics, including gender, and CEMRI parameters, including RENAL score, tumor margin irregularity, necrosis, and tumor apparent diffusion coefficient (ADC) value were identified as independent predictors of adverse pathology for cT1/2 RCC. The clinical-CEMRI predictive model yielded an area under the curve (AUC) of the ROC curve of 0.907, which outperformed the clinical model or CEMRI signature model alone. Good calibration, better clinical usefulness, excellent risk stratification ability of adverse pathology and prognosis were also achieved for the clinical-CEMRI predictive model. CONCLUSIONS: The proposed clinical-CEMRI predictive model offers the potential for preoperative prediction of adverse pathology for cT1/2 RCC. With the ability to forecast adverse pathology, the predictive model could significantly benefit patients and clinicians alike by providing enhanced guidance for treatment planning and decision-making.

Regression analysis of doubly censored failure time data with ancillary information.

Doubly censored failure time data occur in many areas and for the situation, the failure time of interest usually represents the elapsed time between two related events such as an infection and the resulting disease onset. Although many methods have been proposed for regression analysis of such data, most of them are conditional on the occurrence time of the initial event and ignore the relationship between the two events or the ancillary information contained in the initial event. Corresponding to this, a new sieve maximum likelihood approach is proposed that makes use of the ancillary information, and in the method, the logistic model and Cox proportional hazards model are employed to model the initial event and the failure time of interest, respectively. A simulation study is conducted and suggests that the proposed method works well in practice and is more efficient than the existing methods as expected. The approach is applied to an AIDS study that motivated this investigation.