Search Results (63)

Statistical distributions play a crucial role in analyzing real data with varying behavior. In this study, the exponentiated generalized Weibull-G family is discretized using the survival discretization method. Furthermore, a three-parameter discrete distribution, called the exponentiated generalized Weibull–Rayleigh distribution, is generated from this discretized family. This distribution is flexible in modeling various data types, as evidenced by the distinct structures of its probability mass function and hazard rate function. Some statistical properties of both the family and the proposed distribution are discussed. Three estimation approaches—the maximum likelihood, the minimum chi-square, and the method of moments—are used to estimate the distribution’s parameters and are evaluated across three simulation cases. Moreover, the effectiveness of the proposed distribution is evaluated using four datasets from medicine and education. Overall, the results demonstrated the superiority of the proposed distribution fitting the examined data relative to some existing discrete models. Full article

Background/Objectives: Human milk is a complex biological fluid containing not only macro- and micronutrients but also diverse bioactive molecules, including extracellular RNAs. Although RNA has been detected in milk for decades, only a subset of RNA species has been characterized in detail, and abundant families such as tRNA-, yRNA-, and rRNA-derived fragments remain underexplored. This study aimed to define the composition, fragmentation patterns, stability, and exploratory functional activity of these highly abundant RNAs in human milk. Methods: We performed small RNA sequencing on skim milk samples and analyzed the resulting profiles in comparison with publicly available milk and biofluid datasets. RNA stability assays, Northern blotting, and RT-qPCR were conducted to validate RNA abundance and degradation kinetics. Extracellular vesicles (EVs) and non-vesicular fractions were analyzed to determine the subcellular distribution of RNA species. Exploratory functional assays using synthetic RNA fragments were carried out to assess their ability to modulate cellular responses in vitro. Results: Human milk was found to be highly enriched in small RNA fragments derived from tRNA, yRNA, and rRNA, dominated by a limited set of discrete sequences. These profiles were highly reproducible across independent datasets and distinct biofluids. Orthologal validation assays confirmed their abundance and stability, with RNA levels exceeding those of serum by over two orders of magnitude. Full-length transcripts were enriched in EVs, whereas shorter fragments predominated in the non-vesicular fraction. Synthetic milk-derived exRNAs showed detectable pro-survival activity under stress conditions in vitro. Conclusions: This study reveals that human milk carries a limited set of highly abundant stable sRNA molecules, primarily derived from tRNAs, yRNAs, and rRNAs. These findings provide new insights into the RNA cargo of human milk and offer preliminary evidence that selected sRNA fragments can modulate cellular stress responses in in vitro models. Full article

Recent developments in discrete probability models play a crucial role in reliability and survival analysis when lifetimes are recorded as counts. Motivated by this need, we introduce the discrete ZLindley (DZL) distribution, a novel discretization of the continuous ZL law. Constructed using a survival-function approach, the DZL retains the analytical tractability of its continuous parent while simultaneously exhibiting a monotonically decreasing probability mass function and a strictly increasing hazard rate—properties that are rarely achieved together in existing discrete models. We derive key statistical properties of the proposed distribution, including moments, quantiles, order statistics, and reliability indices such as stress–strength reliability and the mean residual life. These results demonstrate the DZL’s flexibility in modeling skewness, over-dispersion, and heavy-tailed behavior. For statistical inference, we develop maximum likelihood and symmetric Bayesian estimation procedures under censored sampling schemes, supported by asymptotic approximations, bootstrap methods, and Markov chain Monte Carlo techniques. Monte Carlo simulation studies confirm the robustness and efficiency of the Bayesian estimators, particularly under informative prior specifications. The practical applicability of the DZL is illustrated using two real datasets: failure times (in hours) of 18 electronic systems and remission durations (in weeks) of 20 leukemia patients. In both cases, the DZL provides substantially better fits than nine established discrete distributions. By combining structural simplicity, inferential flexibility, and strong empirical performance, the DZL distribution advances discrete reliability theory and offers a versatile tool for contemporary statistical modeling. Full article

Purpose: With the addition of palbociclib to endocrine therapy, many hormone receptor-positive (HR+) metastatic breast cancer (mBC) patients experience toxicities that can lead to dose reductions and cycle delays. We examined the actual doses of palbociclib received by patients and their treatment responses. These dose adjustments, made at the physician’s discretion, are not always consistent with pharmaceutical company recommendations. The aim of this study was to assess the influence of dose adjustments on dose intensity and treatment response in our patients. Methods: Records of patients with HR+ mBC treated with palbociclib between December 2016 and January 2019 at the Sainte-Catherine Institute were retrospectively reviewed. Dose intensity was defined as the total dose of palbociclib received by each patient during the first six months of treatment. Anticipated dose reductions and extended cycle delays were recorded. Treatment response at six months and survival were assessed using statistical analyses. Results: A total of 131 women were included; the median age was 67 years. Forty-six patients (35%) experienced an anticipated dose reduction or an extended cycle delay during the first six months of treatment. Logistic regression analysis showed that factors correlated with six-month treatment response included anticipated dose reduction or extended cycle delay (OR = 14.6, 95% CI 3.74–97.4, p < 0.001), cycle delay > 4 weeks (OR = 5.94, 95% CI 1.58–21, p = 0.01), initial dosage < 125 mg (OR = 4.09, 95% CI 1.13–13.7, p = 0.034), and six-month dose intensity < 14,250 mg (OR = 26.0, 95% CI 4.91–481, p < 0.001). Conclusions: In this real-world assessment of clinical outcomes in French patients with HR+ mBC treated with palbociclib, a palbociclib dose intensity lower than recommended—particularly due to cycle delays longer than four weeks—was associated with an increased risk of six-month disease progression. Full article

In recent years, there has been growing interest in discrete probability distributions due to their ability to model the complex behavior of real-world count data. In this paper, a new discrete mixture distribution based on two Weibull components is introduced, constructed using the general discretization approach. Several important statistical properties of the proposed distribution, including the survival function, hazard rate function, alternative hazard rate function, moments, quantile function, and order statistics are derived. It was concluded from the descriptive measures that the discrete mixture of two Weibull distributions transitions from being positively skewed with heavy tails to a more symmetric and light-tailed form. This demonstrates the high flexibility of the discrete mixture of two Weibull distributions in capturing a wide range of shapes as its parameter values vary. Estimation of the parameters is performed via maximum likelihood under Type II censoring scheme. A simulation study assesses the performance of the maximum likelihood estimators. Furthermore, the applicability of the proposed distribution is demonstrated using two real-life datasets. In summary, this paper constructs the discrete mixture of two Weibull distributions, investigates its statistical characteristics, and estimates its parameters, demonstrating its flexibility and practical applicability. These results highlight its potential as a powerful tool for modeling complex discrete data. Full article

Objectives: This phase Ib study assessed the safety and compatibility of indirect oregovomab immunization and Toll-like-receptor-3 (TLR3) stimulation with immune adjuvant Hiltonol^® (poly-ICLC) and induced clinically relevant CA125-specific anti-tumor immunity in heavily pretreated patients with progressive platinum-resistant ovarian cancer (PROC). Methods: Patients with elevated serum CA125 level >50 U/mL received four intravenous infusions with 2 mg oregovomab followed by 2 mg Hiltonol^® intramuscular 30 min and 48 h post-oregovomab at weeks 0, 3, 6, and 9. At week 12, imaging was performed, and salvage chemotherapy was allowed post-progression per the investigator’s discretion. The Fifth/final oregovomab with Hiltonol^® infusion was given at week 16. Results: Fifteen enrolled patients were analyzed for safety and efficacy. Thirteen (87%) patients completed at least three Hiltonol^® infusions with oregovomab, specifically, two cycles (n = 2), three cycles (n = 2), four cycles (n = 3), and five cycles (n = 8). Adverse events included mild fatigue, flu-like symptoms, chills, axillary pain, and injection site discomfort in 13 (87%) patients. Serious adverse events were reported in seven (47%) patients, including Grade 3 hypertension (n = 2), thrombocytopenia (n = 1), and Grade 3 events attributed to underlying disease (n = 4). Ten (67%) patients had disease progression, three (20%) had stable disease, and two were unevaluable. Early humoral response by week 6 was observed in seven of nine (77%) patients, median progression-free survival was 2.7 months (95% confidence interval [CI]: 2.2, 3.3), and median overall survival was 15.0 months (95% CI: 8.2–23.9). Conclusions: The safety and compatibility of combining oregovomab with Hiltonol^® have been demonstrated in this study. The potential to enhance activity of chemotherapy using oregovomab indirect immunization and Hiltonol^® stimulation is proposed. Full article

Background/Objectives: The anti-GD2 monoclonal antibody dinutuximab beta has become standard of care maintenance therapy for high-risk neuroblastoma (HR-NB) in the first-line setting and is also approved in the relapsed/refractory setting. We present a retrospective review of 37 children with HR-NB included in the Hungarian Childhood Cancer Registry who received dinutuximab beta (first-line maintenance therapy, n = 31; relapsed/refractory, n = 6). Methods: All patients received dinutuximab beta continuously over the first 10 days of each 35-day cycle, with dosing based on body surface area/weight. Five cycles were planned, with further cycles administered at the treating physician’s discretion. Results: At data cutoff, the overall disease control rate was 54.1% (20/37) (complete response, 51.4% (19/37); partial response, 0.0% (0/37), stable disease, 2.7% [1/37]); two patients (5.4%) had progressive disease, and 15 patients (40.5%) had died. The 5-year overall survival (OS) and event-free survival (EFS) rates in the overall population were 63.3% (95% confidence interval, 49.1−81.7) and 56.2% (95% confidence interval, 42.1−75.0), respectively. Grade 3 or 4 adverse events (including blood and lymphatic system disorders, hypoxia, hypotension, and capillary leak syndrome) were generally consistent with dinutuximab beta’s known safety profile. Conclusions: Dinutuximab beta was an effective immunotherapy for patients with HR-NB in routine clinical practice, with a generally manageable side effect profile. Full article

Background and Objectives: Severe combined immunodeficiency (SCID) represents a group of rare and potentially fatal monogenic disorders arising from pathogenic variants in a broad spectrum of genes. Diagnostic delays beyond the first few months of life have been associated with poor overall survival and hematopoietic stem cell transplantation (HSCT) outcomes. Therefore, the aim of our study was to apply an NGS assay enabling the rapid and reliable diagnosis of SCID. Materials and Methods: We developed a targeted NGS panel of 30 genes implicated in the pathogenesis of most SCID cases and we applied it to three Greek infants with suspected SCID. Results: Each patient displayed a distinct immunophenotype—T⁻B⁻NK⁻, T⁻B⁻NK⁺ and T⁻B⁺NK⁻, respectively—and was found to harbor pathogenic or likely pathogenic variants in the analyzed SCID-related genes. In particular, patient 1 carried two heterozygous ADA variants (c.58G>A, p.Gly20Arg and c.956_960del, p.Glu319Glyfs); patient 2 harbored two discrete pathogenic variants in the DCLRE1C gene (a large deletion of exons 1–3 and the nonsense mutation c.241C>T, p.Arg81*), causing Artemis deficiency; and patient 3 carried a hemizygous IL2RG missense variant (c.437T>C, p.Leu146Pro), associated with X-linked SCID. All variants were confirmed by Sanger sequencing. Conclusions: Our method successfully identified the underlying genetic defects in all patients, thereby establishing a molecular diagnosis of SCID. These findings highlight the potential of targeted NGS assays for achieving rapid and accurate molecular diagnosis of SCID, which is crucial for the timely treatment of life-threatening conditions in affected children. Full article

The Maxwell–Boltzmann (MB) distribution is fundamental in statistical physics, providing an exact description of particle speed or energy distributions. In this study, a discrete formulation derived via the survival function discretization technique extends the MB model’s theoretical strengths to realistically handle lifetime and reliability data recorded in integer form, enabling accurate modeling under inherently discrete or censored observation schemes. The proposed discrete MB (DMB) model preserves the continuous MB’s flexibility in capturing diverse hazard rate shapes, while directly addressing the discrete and often censored nature of real-world lifetime and reliability data. Its formulation accommodates right-skewed, left-skewed, and symmetric probability mass functions with an inherently increasing hazard rate, enabling robust modeling of negatively skewed and monotonic-failure processes where competing discrete models underperform. We establish a comprehensive suite of distributional properties, including closed-form expressions for the probability mass, cumulative distribution, hazard functions, quantiles, raw moments, dispersion indices, and order statistics. For parameter estimation under Type-II censoring, we develop maximum likelihood, Bayesian, and bootstrap-based approaches and propose six distinct interval estimation methods encompassing frequentist, resampling, and Bayesian paradigms. Extensive Monte Carlo simulations systematically compare estimator performance across varying sample sizes, censoring levels, and prior structures, revealing the superiority of Bayesian–MCMC estimators with highest posterior density intervals in small- to moderate-sample regimes. Two genuine datasets—spanning engineering reliability and clinical survival contexts—demonstrate the DMB model’s superior goodness-of-fit and predictive accuracy over eleven competing discrete lifetime models. Full article

Glioblastoma multiforme (GBM) is the most aggressive and common primary brain tumor. Magnetic resonance imaging (MRI) provides detailed visualization of tumor morphology, edema, and necrosis. However, manually segmenting GBM from MRI scans is time-consuming, subjective, and prone to inter-observer variability. Therefore, automated and reliable segmentation methods are crucial for improving diagnostic accuracy. This study employs an image semantic segmentation model to segment brain tumors in MRI scans of GBM patients. The MRI recall images include T1-weighted imaging (T1WI) and fluid-attenuated inversion recovery (FLAIR) sequences. To enhance the performance of the semantic segmentation model, image preprocessing techniques were applied before analyzing and comparing commonly used segmentation models. Additionally, a survival model was constructed using discrete genotype attributes of GBM patients. The results indicate that the DeepLabV3+ model achieved the highest accuracy for semantic segmentation, with an accuracy of 77.9% on T1WI image sequences, while the U-Net model achieved 80.1% accuracy on FLAIR image sequences. Furthermore, in constructing the survival model using a discrete attribute dataset, the dataset was divided into three subsets based on different missing value handling strategies. This study found that replacing missing values with 1 resulted in the highest accuracy, with the Bernoulli Bayesian model and the multinomial Bayesian model achieving an accuracy of 94.74%. This study integrates image preprocessing techniques and semantic segmentation models to improve the accuracy and efficiency of brain tumor segmentation while also developing a highly accurate survival model. The findings aim to assist physicians in saving time and facilitating preliminary diagnosis and analysis. Full article

The proximal femur represents the most frequent site in the appendicular skeleton for metastatic bone disease (MBD) to occur, with a high risk for pathologic fracture. While surgical stabilization is typically used to manage fractures, reconstruction approaches are gaining popularity due to improved survival. Previous studies have focused on clinical outcomes, but patient-centered outcomes remain underexplored. This study aims to develop a patient-centered primary outcome for the Proximal FEmur Reconstruction or Internal Fixation fOR Metastases (PERFORM) Randomized Controlled Trial, employing a mixed-methods approach. First, a focus group with advanced cancer patients and caregivers identified relevant outcomes. Next, a discrete choice experiment (DCE) assessed the importance of these outcomes among stakeholders, including surgeons, patients and caregivers. The most important components for the primary outcome were identified: mortality within twelve months, physical function assessed at four months using the PROMIS^® Global Physical Function score, and the number of days at home within twelve months. The DCE further confirmed that survival and physical function were most prioritized. The PERFORM trial’s primary outcome, developed through extensive stakeholder engagement, will guide the evaluation of surgical approaches for MBD of the proximal femur and has the potential to influence patient-centered practice. Full article

The volatility of commodity prices has obligated primary metal producers to continuously seek ways of cutting costs in mineral processing units. Improving the wear characteristics and reducing the probability of grinding media fracture can potentially reduce production costs. Characterisation of the impact-loading environment and stress induced into the grinding media in SAG mills aids manufacturers in developing grinding media with superior mechanical properties. Such grinding media development emanates from a firm understanding of the SAG process supported by computer modelling tools and well-established engineering designs. The discrete element method (DEM) is a numerical technique for evaluating collision behaviour in particulate systems. This paper discusses the application of the DEM to estimate survivability and stress, induced into grinding media in a SAG mill. Full article

Survival analysis is a crucial statistical technique used to estimate the anticipated duration until a specific event occurs. However, current methods often involve discretizing the time scale and struggle with managing absent features within the data. This becomes especially pertinent since events can transpire at any given point, rendering event analysis a continuous concern. Additionally, the presence of missing attributes within tabular data is widespread. By leveraging recent developments of Transformer and Self-Supervised Learning (SSL), we introduce SSL-SurvFormer. This entails a continuously monotonic Transformer network, empowered by SSL pre-training, that is designed to address the challenges presented by continuous events and absent features in survival prediction. Our proposed continuously monotonic Transformer model facilitates accurate estimation of survival probabilities, thereby bypassing the need for temporal discretization. Additionally, our SSL pre-training strategy incorporates data transformation to adeptly manage missing information. The SSL pre-training encompasses two tasks: mask prediction, which identifies positions of absent features, and reconstruction, which endeavors to recover absent elements based on observed ones. Our empirical evaluations conducted across a variety of datasets, including FLCHAIN, METABRIC, and SUPPORT, consistently highlight the superior performance of SSL-SurvFormer in comparison to existing methods. Additionally, SSL-SurvFormer demonstrates effectiveness in handling missing values, a critical aspect often encountered in real-world datasets. Full article

The Hjorth distribution is more flexible in modeling various hazard rate shapes, including increasing, decreasing, and bathtub shapes. This makes it highly useful in reliability analysis and survival studies, where different failure rate behaviors must be captured effectively. In some practical experiments, the observed data may appear to be continuous, but their intrinsic discreteness requires the development of specialized techniques for constructing discrete counterparts to continuous distributions. This study extends this methodology by discretizing the Hjorth distribution using the survival function approach. The proposed discrete Hjorth distribution preserves the essential statistical characteristics of its continuous counterpart, such as percentiles and quantiles, making it a valuable tool for modeling lifetime data. The complexity of the transformation requires numerical techniques to ensure accurate estimations and analysis. A key feature of this study is the incorporation of Type-II censored samples. We also derive key statistical properties, including the quantile function and order statistics, and then employ maximum likelihood and Bayesian inference methods. A comparative analysis of these estimation techniques is conducted through simulation studies. Furthermore, the proposed model is validated using two real-world datasets, including electronic device failure times and ball-bearing failure analysis, by applying goodness-of-fit tests against alternative discrete models. The findings emphasize the versatility and applicability of the discrete Hjorth distribution in reliability studies, engineering, and survival analysis, offering a robust framework for modeling discrete data in practical scenarios. To our knowledge, no prior research has explored the use of censored data in analyzing discrete Hjorth-distributed data. This study fills this gap, providing new insights into discrete reliability modeling and broadening the application of the Hjorth distribution in real-world scenarios. Full article

The aim of this work is to propose a proportional log survival model (PLSM) as a discrete alternative to the proportional hazards (PH) model. This paper presents the formulation of PLSM as well as the procedures for verifying its assumption. The parameters of the PLSM are inferred using the maximum likelihood method, and a simulation study was carried out to investigate the usual asymptotic properties of the estimators. The PLSM was illustrated using data on the survival time of leukemia patients, and it was shown to be a viable alternative for modeling discrete survival data in the presence of covariates. Full article