Search Results (152)

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

Background: Primary plasma cell leukemia (pPCL) represents the most aggressive plasma cell dyscrasia with a poor prognosis and survival of <3 years. The International Myeloma Working Group (IMWG) adopted more inclusive diagnostic criteria for pPCL in 2021, including patients with 5% or more circulating plasma cells (down from 20%). Most published studies of pPCL do not include patients who meet the criteria for pPCL based on the newer diagnostic guidelines, and the data on the optimal treatment of pPCL is scarce. In our multi-center retrospective analysis, we report data on treatment regimens used in 67 pPCL patients to characterize outcomes in this population. Methods: We included patients with newly diagnosed pPCL between 2010 and 2023 based on the 2021 IMWG definition at one of three academic centers. Results: Our results suggest significant improvement in overall response rate (ORR) and progression-free survival (PFS) with the use of autologous stem cell transplant, but without additional benefit for a tandem transplant. The presence of high-risk cytogenetics was an independent risk factor for progression in the cohort. Conclusions: Our dataset represents one of the largest cohorts to date using the expanded definition of pPCL adopted by the IMWG in 2021 and stresses the importance of taking pPCL patients to transplant. Unfortunately, our study was not powered to determine the efficacy of individual induction and maintenance regimens, and many patients diagnosed with pPCL are ineligible for transplant based on end-organ damage at diagnosis or from disease that is refractory to induction therapy, underscoring the need for early diagnosis and treatment in hopes of preserving transplant eligibility. Full article

A growing body of recent work suggests the possibility of heterogeneous ribosomal composition. We recently observed subtype-specific mRNA and copy number variation signatures of human ribosomal proteins (RPs) in cancers from human adults, but whether such subtype-specific RP mRNA signatures are also present in human pediatric cancers is currently unknown. In this study, we analyzed mRNA expression data from multiple large pediatric cancer datasets to test for heterogeneity in RP mRNA signatures. We found that different pediatric cancer types have different RP mRNA signatures, sometimes multiple RP mRNA signatures within the same pediatric cancer type, which can be subgroup/subtype-specific (e.g., in Medulloblastoma) or cell-of-origin-specific (e.g., in Acute Lymphoblastic Leukemia (ALL)). In B-cell ALL, we found two RP mRNA subtypes with significantly different prognoses. Consistent with our recent findings in adult cancers, the RP mRNA signature in pediatric cancer is heterogeneous and subtype-specific and may have clinical relevance. Full article

Artificial neural networks in drug discovery have shown remarkable potential in various areas, including molecular similarity assessment and virtual screening. This study presents a novel multimodal Siamese neural network architecture. The aim was to join molecular electrostatic potential (MEP) images with the texture features derived from reduced density gradient (RDG) diagrams for enhanced molecular similarity prediction. On one side, the proposed model is combined with a convolutional neural network (CNN) for processing MEP visual information. This data is added to the multilayer perceptron (MLP) that extracts texture features from gray-level co-occurrence matrices (GLCM) computed from RDG diagrams. Both representations converge through a multimodal projector into a shared embedding space, which was trained using triplet loss to learn similarity and dissimilarity patterns. Limitations associated with the use of purely structural descriptors were overcome by incorporating non-covalent interaction information through RDG profiles, which enables the identification of bioisosteric relationships needed for rational drug design. Three datasets were used to evaluate the performance of the developed model: tyrosine kinase inhibitors (TKIs) targeting the mutant T315I BCR-ABL receptor for the treatment of chronic myeloid leukemia, acetylcholinesterase inhibitors (AChEIs) for Alzheimer’s disease therapy, and heterodimeric AChEI candidates for cross-validation. The visual and texture features of the Siamese architecture help in the capture of molecular similarities based on electrostatic and non-covalent interaction profiles. Therefore, the developed protocol offers a suitable approach in computational drug discovery, being a promising framework for virtual screening, drug repositioning, and the identification of novel therapeutic candidates. Full article

Chronic myeloid leukemia (CML) is a myeloproliferative neoplasm characterized by uncontrolled proliferation of myeloid cells. MicroRNAs (miRNAs), small noncoding RNAs, regulate post-transcriptional gene expression by degrading target mRNAs or repressing translation. Dysregulated miRNA expression has been implicated in various malignancies, including CML, where they can function as oncogenes or tumor suppressors. This study aimed to investigate the relationship between miR-122-5p and cell division cycle 25A (CDC25A) in CML and to elucidate the regulatory mechanisms of miR-122-5p. This study integrates bioinformatics analysis with in vitro RT-qPCR validation in K562 chronic myeloid leukemia cells to explore the potential regulatory relationship between miR-122-5p and CDC25A. mRNA expression profiles were retrieved from the GSE100026 dataset in the Gene Expression Omnibus (GEO), and differentially expressed genes were identified using GEO2R. Quantitative real-time PCR (RT-qPCR) was performed to measure miR-122-5p, CDC25A, and cyclin-dependent kinase 4 (CDK4) expression levels. Bioinformatics analyses (miRNeT, miRDIP, TargetScan, BioGPS, GeneMANIA, STRING) were applied to predict molecular interactions and functional pathways. Public RNA-seq datasets and in silico tools were used to prioritize candidates; RT-qPCR in a single CML cell line (K562) provided in vitro expression validation. In K562 cells, miR-122-5p expression was significantly reduced, while CDC25A and CDK4 were markedly upregulated. Bioinformatics tools confirmed CDC25A as a potential miR-122-5p target. Functional enrichment indicated CDC25A involvement in cell cycle regulation and apoptosis. These findings suggest that miR-122-5p functions as a tumor suppressor in CML by targeting CDC25A. Modulating the miR-122-5p/CDC25A axis may provide potential molecular targets for inhibiting CML progression through regulation of cell cycle pathways. Findings are exploratory and based on bioinformatics with limited in vitro expression confirmation; functional studies are required to establish causality. Full article

Modern healthcare and engineering both rely on robust reliability models, where handling censored data effectively translates into longer-lasting devices, improved therapies, and safer environments for society. To address this, we develop a novel inferential framework for the ZLindley (ZL) distribution under the improved adaptive progressive Type-II censoring strategy. The proposed approach unifies the flexibility of the ZL model—capable of representing monotonically increasing hazards—with the efficiency of an adaptive censoring strategy that guarantees experiment termination within pre-specified limits. Both classical and Bayesian methodologies are investigated: Maximum likelihood and log-transformed likelihood estimators are derived alongside their asymptotic confidence intervals, while Bayesian estimation is conducted via gamma priors and Markov chain Monte Carlo methods, yielding Bayes point estimates, credible intervals, and highest posterior density regions. Extensive Monte Carlo simulations are employed to evaluate estimator performance in terms of bias, efficiency, coverage probability, and interval length across diverse censoring designs. Results demonstrate the superiority of Bayesian inference, particularly under informative priors, and highlight the robustness of HPD intervals over traditional asymptotic approaches. To emphasize practical utility, the methodology is applied to real-world reliability datasets from clinical trials on leukemia patients and hydrological measurements from River Styx floods, demonstrating the model’s ability to capture heterogeneity, over-dispersion, and increasing risk profiles. The empirical investigations reveal that the ZLindley distribution consistently provides a better fit than well-known competitors—including Lindley, Weibull, and Gamma models—when applied to real-world case studies from clinical leukemia trials and hydrological systems, highlighting its unmatched flexibility, robustness, and predictive utility for practical reliability modeling. Full article

Artificial intelligence applications in histopathological diagnostics are rapidly expanding, with particular promise in complex hematological malignancies where diagnostic accuracy remains challenging and subjective. This study undertakes a scoping review to systematically map the extent of research on artificial intelligence applications in histopathological diagnostics of leukemias, examine geographic distribution and methodological approaches, and assess the current state of AI model performance and clinical readiness. A comprehensive search was conducted in the Scopus database covering publications from 2018 to 2025 (as of 12 July 2025), using five targeted search strategies combining AI, histopathology, and leukemia-related terms. Following a three-stage screening protocol, 418 publications were selected from an initial pool of over 75,000 records across multiple countries and research domains. The analysis revealed a marked increase in research output, peaking in 2024 with substantial contributions from India (26.3%), China (17.9%), USA (13.8%), and Saudi Arabia (11.1%). Among 43 documented datasets ranging from 80 to 42,386 images, studies predominantly utilized convolutional neural networks and deep learning approaches. AI models demonstrated high diagnostic accuracy, with 25 end-to-end models achieving an average accuracy of 97.72% compared to 96.34% for 20 classical machine learning approaches. Most studies focused on acute lymphoblastic leukemia detection and subtype classification using blood smear and bone marrow specimens. Despite promising diagnostic performance, significant gaps remain in clinical translation, standardization, and regulatory approval, with none of the reviewed AI systems currently FDA-approved for routine leukemia diagnostics. Future research should prioritize clinical validation studies, standardized datasets, and integration with existing diagnostic workflows to realize the potential of AI in hematopathological practice. Full article

Staining-based assays are widely used for cell analysis but are invasive, alter physiology, and prevent longitudinal monitoring. Label-free, morphology-based approaches could enable real-time, non-invasive drug testing, yet detection of subtle and dynamic changes has remained difficult. We developed a deep learning framework for stain-free monitoring of leukemia cell cultures using automated bright-field microscopy in a semi-automated culture system (AICE3, LABMaiTE, Augsburg, Germany). YOLOv8 models were trained on images from K562, HL-60, and Kasumi-1 cells, using an NVIDIA DGX A100 GPU for training and tested on GPU and CPU environments for real-time performance. Comparative benchmarking with RT-DETR and interpretability analyses using Eigen-CAM and radiomics (RedTell) was performed. YOLOv8 achieved high accuracy (mAP@0.5 > 98%, precision/sensitivity > 97%), with reproducibility confirmed on an independent dataset from a second laboratory and an AICE3 setup. The model distinguished between morphologically similar leukemia lines and reliably classified untreated versus differentiated K562 cells (hemin-induced erythroid and PMA-induced megakaryocytic; >95% accuracy). Incorporation of decitabine-treated cells demonstrated applicability to drug testing, revealing treatment-specific and intermediate phenotypes. Longitudinal monitoring captured culture- and time-dependent drift, enabling separation of temporal from drug-induced changes. Radiomics highlighted interpretable features such as size, elongation, and texture, but with lower accuracy than the deep learning approach. To our knowledge, this is the first demonstration that deep learning resolves subtle, drug-induced, and time-dependent morphological changes in unstained leukemia cells in real time. This approach provides a robust, accessible framework for label-free longitudinal drug testing and establishes a foundation for future autonomous, feedback-driven platforms in precision oncology. Ultimately, this approach may also contribute to more precise and adaptive clinical decision-making, advancing the field of personalized medicine. Full article

Background: Acute myeloid leukemia (AML) is an aggressive and biologically diverse hematologic cancer that disproportionately affects older individuals. Despite advances in molecular profiling and therapy, long-term outcomes remain unsatisfactory. This nationwide registry was established to provide real-world insights into clinical characteristics, treatment strategies, and survival among adult AML patients in Turkey. Methods: The Turkish AML Registry Project (ClinicalTrials.gov Identifier: NCT05979675) combines retrospective and prospective data from 23 tertiary hematology centers. Adult patients diagnosed between January 2008 and July 2023 were included. Baseline demographics, European LeukemiaNet (ELN) 2017 risk groups, Eastern Cooperative Oncology Group (ECOG) performance status, treatment intensity, and targeted therapy use were analyzed. Response and survival outcomes were assessed using Kaplan–Meier methods. Results: The interim dataset included 891 patients (median age 58 years, 45.5% ≥60). Intensive chemotherapy, most commonly 7 + 3, was applied in 74.1%, while 25.9% received lower-intensity regimens. Targeted agents, mainly venetoclax, were incorporated more frequently into low-intensity therapies (19.1% vs. 3.4%, p < 0.001). Complete remission occurred in 70.2% after intensive and 35.9% after low-intensity therapy, improving to 51.4% with targeted agents. Median overall survival (OS) was 27.2 months, with 1-year OS rates of 54.1%, 28.9%, and 17.6% for favorable, intermediate, and adverse ELN groups (p < 0.001). ECOG 0–1 predicted superior survival (1-year OS 70.3% vs. 47.0%). Conclusions: Nationwide real-world evidence underscores the prognostic relevance of ELN risk and functional status in AML. While intensive chemotherapy remains central, combining targeted agents with low-intensity regimens improves outcomes in less fit patients and supports personalized treatment approaches. Full article

Personalized drug selection is crucial for treating complex diseases such as Acute Myeloid Leukemia, where maximizing therapeutic efficacy is essential. Although precision medicine aims to tailor treatments to individual molecular profiles, existing machine learning models often fall short in selecting the best drug from multiple candidates. We present SEATS (Systematic Efficacy Assignment with Treatment Seats), which adapts conventional models like Random Forest and XGBoost for multiclass drug assignment by allocating probabilistic “treatment seats” to drugs based on efficacy. This approach helps models learn clinically relevant distinctions. Additionally, we assess an interpretable Optimal Decision Tree (ODT) model designed specifically for drug assignment. Trained on the BeatAML2 cohort and validated on the GDSC AML cell line dataset, integrating SEATS with Random Forest and XGBoost improved prediction accuracy and consistency. The ODT model offered competitive performance with clear, interpretable decision paths and minimal feature requirements, facilitating clinical use. SEATS reorients standard models towards personalized drug selection. Combined with the ODT framework it provides effective, interpretable strategies for precision oncology, underscoring the potential of tailored machine learning solutions in supporting real-world treatment decisions. Full article

This paper introduces BCenetTucker, a novel bootstrap-enhanced extension of the CenetTucker model designed to address the instability of sparse support recovery in high-dimensional tensor settings. By integrating mode-specific resampling directly into the penalized tensor decomposition process, BCenetTucker improves the reliability and reproducibility of latent structure estimation without compromising the model′s interpretability. The proposed method is systematically benchmarked against classical CenetTucker, Stability Selection, and Bolasso, using real-world gene expression data from the GSE13159 leukemia dataset. Across multiple stability metrics—including support-size deviation, average Jaccard index, inclusion frequency, proportion of stable support, and Stable Selection Index (SSI)—BCenetTucker consistently demonstrates superior robustness and structural coherence relative to competing approaches. In the real data application, BCenetTucker preserved all essential signals originally identified by CenetTucker while uncovering additional marginal yet reproducible features. The method achieved high reproducibility (Jaccard index = 0.975; support-size deviation = 1.7 genes), confirming its sensitivity to weak but stable signals. The protocol was implemented in the GSparseBoot R library, enabling reproducibility, transparency, and applicability to diverse domains involving structured high-dimensional data. Altogether, these results establish BCenetTucker as a powerful and extensible framework for achieving stable sparse decompositions in modern tensor analytics. Full article

Male infertility is a global health concern, and many cases are idiopathic in nature. The development and differentiation of germ cells (Gcs) are supported by Sertoli cells (Scs). Differentiated Scs support the development of Gcs into sperm, and hence, male fertility. We previously reported on a developmental switch in Scs around 12 days of age onwards in rats. During the process of the differentiation of Scs, the differential expression of mitophagy-related genes and its role in male fertility are poorly understood. To address this gap, we evaluated the microarray dataset GSE48795 to identify 12 mitophagy-related hub genes, including B-Cell Leukemia/Lymphoma 2 (Bcl2) and FBJ murine osteosarcoma viral oncogene homolog (Fos). We identify Neuron-derived orphan receptor 1 (Nor1) as a potential mitophagy-related gene of interest due to its strong regulatory association with two hub genes, Bcl2 and Fos, which were differentially expressed during Sc maturation. To validate this finding, we generated a transgenic rat model with the Sc-specific knockdown of Nor1 during puberty. A functional analysis showed impaired spermatogenesis with reduced fertility in these transgenic rats. Our findings suggest that Nor1 may be an important mitophagy-related gene regulating the function of Scs and thereby regulating male fertility. Full article

Background: Pediatric sepsis is a life-threatening disease that is associated with the progression of acute lymphoblastic leukemia (ALL) and the recurrence of B-cell ALL (B-ALL). Although previous studies have reported a partial association between sepsis and ALL, there is limited research on the shared genes between pediatric sepsis and relapsed B-ALL. This study aims to further elucidate the more comprehensive and novel common genetic factors and molecular pathways between the two diseases. Methods: Gene expression datasets pertaining to pediatric sepsis (GSE13904, GSE80496) and relapsed B-ALL (GSE3910, GSE28460) were retrieved from the Gene Expression Omnibus database for this retrospective analysis. The initial analysis identified differentially expressed genes common to both pediatric sepsis and relapsed B-ALL. Subsequent investigations employed three complementary approaches: protein–protein interaction networks, molecular complex detection (MCODE) clustering functions, and support vector machine recursive feature elimination model to separately identify the diagnostic biomarkers for each condition. Importantly, key common genes were identified by overlapping the diagnostic genes for pediatric sepsis and relapsed B-ALL. Further characterization involved comprehensive functional analysis through the Metascape platform, construction of transcription factor (TF)-mRNA-microRNA (miRNA) networks, drug prediction, and molecular docking to explore their biological significance and potential therapeutic targets. Results: Comparative analysis of pediatric sepsis-related and relapsed B-ALL-related datasets revealed two shared genetic markers, lactotransferrin (LTF) and matrix metallopeptidase 9 (MMP9), exhibiting diagnostic significance and consistent upregulation in both disease groups. Transcriptional regulatory network analysis identified specificity protein 1 (SP1) as the principal transcription factor capable of coregulating LTF and MMP9 expression. In addition, molecular docking demonstrated high-affinity interactions between curcumin and MMP9 (−7.18 kcal/mol) as well as reserpine and LTF (−5.4 kcal/mol), suggesting their potential therapeutic utility for clinical evaluation. Conclusions: These findings elucidate the molecular pathogenesis involving LTF and MMP9 in pediatric sepsis and relapsed B-ALL, providing novel insights for clinical diagnosis and therapeutic development. Full article

The use of JAK inhibitors (JAKi) represents a promising therapeutic approach for patients with lymphoid leukemias (Lym-L). Clinical trials are ongoing to evaluate the safety and efficacy of JAK inhibitors. Over the last years, there have been reports of preclinical Lym-L models that developed JAKi resistance, and reports of patients treated with JAKi who experienced treatment failure. Although evidence shows that there are diverse JAKi mechanisms, no review studies have been performed that summarize and discuss this information. This scoping review aimed to provide an updated overview of the mechanisms underlying JAKi molecular resistance in Lym-L. According to a scoping review PRISMA guidelines, a search was conducted in the PubMed and Europe PMC databases for studies published from 2010 to 2024. We included articles that described the molecular resistance to JAKi in Lym-L preclinical models or patients. The search was complemented by a review of laboratory-engineered resistant mutations in genomic datasets to obtain more information about their presence in patients with Lym-L. Twenty-two articles were eligible for this review, and six different mechanisms of molecular resistance were identified: (1) point mutations in the kinase domain, (2) cooperation between double-JAK mutants, (3) inactivation of phosphatases, (4) evasion of JAK inhibition due to trans-phosphorylation of JAK family proteins, (5) upregulation of pro-survival proteins, and (6) activation of kinase cross-signaling pathways. The integrated evidence enabled the identification of specific mechanisms of molecular resistance to JAKi in Lym-L, as well as promising therapeutic approaches to prevent them. These include selecting a sensitive JAKi, choosing an effective dosage regimen, and combining inhibitory molecules. Full article

Microarray gene expression data are characterized by high dimensionality and small sample sizes, which complicates cancer classification tasks. To address these challenges, this study proposes a hybrid gene selection approach that integrates a filter-based dimensionality reduction method with a metaheuristic optimizer. Specifically, the method applies the F-score statistical filter to rank and reduce gene features, followed by Nuclear Reaction Optimization (NRO) to refine the selection. This combination is referred to as the F-score-based Nuclear Reaction Optimization method or F-NRO. The performance of F-NRO was evaluated on six publicly available microarray cancer datasets (Colon, Leukemia1, Leukemia2, Lung, Lymphoma, and SRBCT) using Support Vector Machines (SVMs) and Leave-One-Out Cross-Validation (LOOCV). Comparative analysis against several existing hybrid gene selection algorithms demonstrates that F-NRO achieves high classification accuracy, including perfect accuracy on five datasets, using compact gene subsets. These results suggest that F-NRO is an effective and interpretable solution for gene selection in cancer classification tasks. Full article