Search Results (311)

Background: Acute coronary syndrome (ACS) remains a leading cause of morbidity and mortality worldwide, and improved risk stratification beyond conventional biomarkers is needed. Novel laboratory-derived indices reflecting systemic inflammation and immunonutritional status including the inflammatory prognostic index (IPI), prognostic nutritional index (PNI), and aggregate index of systemic inflammation (AISI) may provide integrated prognostic information in ACS. Methods: In this cohort study, 2400 participants were included: 800 controls, 800 patients with non-ST-elevation myocardial infarction (NSTEMI), and 800 with ST-elevation myocardial infarction (STEMI). Results: Compared with controls, NSTEMI and STEMI patients were younger and exhibited higher body mass index, blood pressure, heart rate, and progressively worse glycemic indices (fasting glucose and HbA1c; all p < 0.001). Lipid parameters were significantly higher in ACS groups versus controls (p < 0.001). Cardiac biomarkers were markedly elevated in ACS, with significantly higher troponin I and CK-MB levels in STEMI than NSTEMI and controls (both p < 0.001). Inflammatory and renal parameters (CRP, fibrinogen, urea, creatinine) were increased in ACS, most prominently in STEMI. Composite indices demonstrated strong inter-correlations, including a strong positive correlation between AISI and IPI (r ≈ 0.91, p < 0.001), while PNI correlated inversely with CONUT score (r ≈ −0.70, p < 0.001). The Gensini score differed significantly among groups and was highest in NSTEMI (p < 0.001). Survival was significantly lower in STEMI than NSTEMI (log-rank p = 0.005), with RMST of 315.5 days in NSTEMI versus 299.4 days in STEMI. In multivariable Cox regression, STEMI presentation independently predicted higher mortality risk (HR 1.26, 95% CI 1.04–1.53; p = 0.018), and higher Gensini score was also independently associated with mortality (HR 1.01 per point; 95% CI 1.00–1.02; p = 0.036). Higher PNI was independently protective (HR 0.997; 95% CI 0.993–1.000; p = 0.045), whereas age and CONUT score were not significant in the adjusted model. Conclusions: Novel laboratory-derived systemic inflammatory and nutrition-related indices particularly IPI and AISI as markers of inflammatory burden and PNI as a marker of immunonutritional balance provide clinically relevant prognostic information in ACS. STEMI presentation is associated with shorter survival, and all-cause mortality is independently related to STEMI status, greater angiographic severity (higher Gensini score), and lower PNI. These readily available indices may offer incremental value for risk stratification in NSTEMI and STEMI when integrated with conventional clinical and angiographic assessment. Full article

Stirling numbers are among the most classical objects in enumerative combinatorics, counting set partitions and permutations. In this paper, we study their $(p,q)$-analogues in type B from a rook-theoretic point of view. We introduce a type B Ferrers board and establish a bijection between signed restricted growth functions and type B rook placements. In addition, we defined the weighted statistics ${\mathrm{levLB}}_B\left(w\right)$ and ${\mathrm{levLS}}_{\mathrm{B}}\left(\mathrm{w}\right)$ over the set of signed restricted growth functions. The associated statistics yield a weighted enumeration that recovers the $(p,q)$-Stirling polynomials of type B, their recurrence relations and generating functions. We then introduce type B Laguerre boards and prove that their rook numbers coincide with the Lah numbers of type B. Full article

Background and Clinical Significance: Diffuse Large B-Cell Lymphoma (DLBCL) is a morphologically and molecularly heterogeneous lymphoproliferative disorder that originates from a clonal B-cell ancestor. Patients usually present with rapidly enlarging lymph nodes or mass(es) at single or multiple sites. Generally, 18F-Fluorodeoxyglucose (18F-FDG) positron emission tomography with computed tomography (PET-CT) is performed post-treatment to evaluate remission status, especially in radiologically residual tumors. Myofibroblastoma (MFB) is a benign mesenchymal tumor of the mammary stroma composed of fibroblasts and myofibroblasts. These entities do not often present concurrently. Case presentation: The patient was an 80-year-old man with a history of stage IV-BS Diffuse Large B-Cell Lymphoma (DLBCL) with a high-risk International Prognostic Index (IPI). The patient underwent treatment with a six-cycle R-CHOP regimen. Immediately after the last cycle, an 18F-Fluorodeoxyglucose (18F-FDG) positron emission tomography with computed tomography (PET-CT) scan revealed a nodular solid lesion with a faintly increased metabolic standardized uptake value (SUVmax) of 3 in the upper outer quadrant of his left breast. A biopsy of the breast lesion was performed, and it revealed a benign mesenchymal tumor, specifically a Myofibroblastoma. The patient has not presented any symptoms or complications since surgery (12 months) and remains in complete remission (CR). Conclusions: Given the potential diagnostic pitfalls and therapeutic implications of residual tumors in the context of DLBCL, a conscientious evaluation by a multidisciplinary team (MDT) is highly recommended. Full article

Background/Objectives: Diffuse large B-cell lymphoma (DLBCL) is the most common subtype of non-Hodgkin lymphoma and remains incurable in approximately 30–40% of patients despite advances in immunochemotherapy. Although gene expression profiling has improved risk stratification, there is an ongoing need for non-invasive, cost-effective, and clinically practical biomarkers to identify patients at high risk of treatment resistance or relapse (R/R). Systemic inflammation plays a pivotal role in DLBCL pathogenesis, impacting both tumor progression and treatment response. The C-reactive protein–albumin–lymphocyte (CALLY) index, integrating markers of inflammation, nutritional status, and immune competence, has demonstrated prognostic relevance in solid tumors; however, its relevance in hematologic malignancies remains unexplored. Methods: We retrospectively analyzed 180 adults with newly diagnosed DLBCL, NOS (not otherwise specified) who received frontline rituximab-based immunochemotherapy (R-CHOP or CHOP-like regimens) between January 2014 and December 2019 at three tertiary centers in Serbia. The median age was 67 years (IQR 59–73), and 56.1% were female. Receiver operating characteristic (ROC) analysis determined 6.5 as the optimal CALLY index cut-off (AUC 0.744, 95% CI 0.670–0.817; p < 0.001). Results: A low CALLY index (<6.5) was significantly associated with adverse clinical features, including anemia, elevated lactate dehydrogenase and β2-microglobulin, poor ECOG performance status, bulky disease, advanced stage, and unfavorable IPI, R-IPI, and NCCN-IPI scores (all p < 0.001). In contrast, no associations were observed with tumor subtype, immunophenotype, or comorbidities. Furthermore, patients with CALLY <6.5 showed lower overall response rates to treatment (59.6% vs. 85.5%, p < 0.001) and higher relapse rates (21.0% vs. 6.2%, p = 0.014). They also experienced reduced 3- and 5-year overall survival (OS) and event-free survival (EFS) (all p < 0.001). In multivariate analysis, a low CALLY index independently predicted poorer OS (HR 2.04, 95% CI 1.13–3.67; p = 0.017) and EFS (HR 1.89, 95% CI 1.13–3.14; p = 0.015). In addition, it independently identified patients at risk of relapsed/refractory (R/R) disease (OR 2.50, 95% CI 1.02–10.10; p = 0.04), outperforming standard prognostic indices. Conclusions: The CALLY index is a simple, low-cost, and widely accessible biomarker that independently predicts prognosis in DLBCL, NOS. It outperforms standard indices in identifying R/R cases. The CALLY index may enhance risk stratification and guide individualized treatment strategies. Full article

Bone marrow involvement in B-cell non-Hodgkin lymphoma is an adverse prognostic factor; therefore, its detection is necessary at the time of diagnosis and during follow-up. This study evaluates the concordance between flow cytometry (FC) and bone marrow biopsy (BMB) in detecting bone marrow involvement in B-cell non-Hodgkin lymphomas as well as their prognostic relevance in a Chilean cohort. A total of 202 samples from 172 patients with diffuse large B-cell (DLBCL), follicular (FL), marginal-zone (MZL), and mantle cell (MCL) lymphoma were retrospectively analyzed; all patients underwent simultaneous BMB and FC. Bone marrow involvement was identified in 29% of samples via BMB and in 40% via FC, with an overall concordance of 89% (kappa: 0.75), which was lower in mantle cell lymphoma. Eleven percent of cases showed BMB-FC+ discordance, generally associated with low tumor burden. In survival analyses, the BMB+/FC+ group exhibited shorter overall and progression-free survival, and concordant involvement was associated with a higher risk of mortality and progression, particularly among patients with an intermediate or high IPI. Involvement detected exclusively by FC did not have a significant prognostic impact. These findings support the role of FC as a complementary or alternative diagnostic tool in settings with limited resources, improving sensitivity for detecting bone marrow involvement without compromising clinical relevance. Full article

Background: The Inflammatory Prognostic Index (IPI) is a novel biomarker integrating C-reactive protein (CRP), albumin, and white blood cell-derived ratios, reflecting systemic inflammation and nutritional status. Inflammation is central to in-stent restenosis (ISR). This study investigated the prognostic value of IPI in predicting ISR after drug-eluting stent (DES) implantation. Methods: We retrospectively analyzed 571 patients who underwent DES implantation and follow-up angiography at a median of 12 months (IQR 12–24) for recurrent angina or ischemia. Patients were grouped as ISR (+) (n = 218) and ISR (−) (n = 353). IPI was calculated as (CRP × neutrophil-to-lymphocyte ratio)/albumin. Logistic regression and ROC analyses assessed the predictive role of IPI. Results: ISR occurred in 38.1% of patients. The ISR (+) group showed a higher prevalence of hypertension and active smoking, as well as higher CRP, glucose, and neutrophil levels, but lower albumin and lymphocytes (all p < 0.05). Elevated IPI independently predicted ISR (OR = 2.90; 95% CI = 2.35–3.57; p < 0.001). ROC analysis showed an optimal cutoff of 1.275 (sensitivity 84.4%, specificity 74.5%). Conclusions: IPI, derived from routine laboratory tests, independently predicts ISR after DES implantation and may serve as a simple, inexpensive biomarker for coronary artery disease risk stratification. Full article

Introduction: Central nervous system (CNS) involvement is reported to represent 5% of extranodal diffuse large B cell lymphoma (DLBCL) at diagnosis. To stratify patients’ risk of CNS involvement, the CNS-International Prognostic Index (CNS-IPI) score was developed. Serum uric acid level has not been incorporated into IPI or CNS-IPI score and its correlation with CNS-IPI risk groups and the CNS relapse has not been studied to date. Therefore, we aimed to retrospectively analyze the relationship between uric acid levels and CNS relapse risk in patients with newly diagnosed adult DLBCL. Methods: Ninety-four adult newly diagnosed DLBCL patients were retrospectively assessed via electronic records and patient files. Demographic data, IPI and CNS-IPI scores, hematologic parameters, serum lactate dehydrogenase, beta-2 microglobulin, serum uric acid and creatinine levels at diagnosis were recorded for analysis. Uric acid levels were compared between CNS-IPI low-intermediate and high groups and cumulative incidence of CNS relapses were analyzed between normal and elevated uric acid levels. Results: Uric acid levels were found to be significantly higher in CNS-IPI high-risk patients (p = 0.008). Elevated serum uric acid levels at diagnosis were significantly associated with high CNS-IPI risk in the logistic regression analysis (OR 1.34, 95% CI 1.05–1.78, p = 0.047). Uric acid also higher than 5.39 mg/dL showed a discriminatory ability in ROC analyses (AUC 0.633, 95% CI 0.495–0.771, p = 0.05). In the competing risks regression analysis, accounting for non-CNS-related death and progression as the competing events, CNS-IPI subgroups and uric acid levels were not significantly associated with the cumulative incidence of CNS relapse (SHR 0.81, 95% CI 0.12–5.59; Fine–Gray, p = 0.834), (SHR 0.55, 95% CI 0.09–3.47; Fine–Gray, p = 0.526), respectively. Conclusions: Even though uric acid levels are significantly higher and showed a discriminatory ability to detect the CNS-IPI high subgroup, elevated uric acid levels could not predict the CNS relapses. Full article

Thyroid cancer (TC) incidence is rising, necessitating a refined understanding of its complex biology, particularly for advanced forms. This review synthesizes the state-of-the-art knowledge, guided by the WHO 5th Classification (2022), which incorporates molecular findings and introduces categories like Differentiated High-Grade Thyroid Carcinoma (DHGTC) to better stratify prognosis. The review summarizes the molecular changes in thyroid cancer (TC) by establishing a clear link between specific oncogenic alterations and the resulting tumor phenotype, prognosis, risk stratification and therapeutic vulnerabilities. The central importance of the review lies in its comprehensive integration of these molecular changes with the resulting immunological microenvironment and the rationale for novel, personalized therapies. Moreover, high-level genomic instability within aggressive thyroid malignancies promotes an immunosuppressive tumor microenvironment via the selection and recruitment of suppressive immune components, contributing to immune evasion and poor prognosis. This characteristic immunosuppression identifies the aggressive tumors as prime candidates for targeted immunotherapies. The review implicitly argues that understanding the molecular drivers of this immunosuppression is essential for designing effective clinical trials using these novel agents. Diagnostic advancements, including molecular testing for high-risk mutations (BRAF, TERT) and the integration of Artificial Intelligence (AI) for refined risk stratification, are enabling personalized treatment. The evolving molecular and clinical understanding allows for a paradigm shift toward individualized therapies that balance optimal disease control with minimizing morbidity, especially in the context of high-risk disease. Full article

Background: The coordinated improvement of yield, quality and resistance is a primary goal in rice breeding. Gene editing technology is a novel method for precise multiplex gene improvement. Methods: In this study, we constructed a multiplex CRISPR/Cas9 vector targeting yield-related genes (GS3, OsPIL15, Gn1a), fragrance gene (OsBADH2) and rice blast resistance gene (Pi21) to pyramid traits for enhanced yield, quality, and disease resistance in rice. A tRNA-assisted CRISPR/Cas9 multiplex gene editing vector, M601-OsPIL15/GS3/Gn1a/OsBADH2/Pi21-gRNA, was constructed. Genetic transformation was performed using the Agrobacterium-mediated method with the japonica rice variety Xin Dao 53 as the recipient. Mutation editing efficiency was detected in T₀ transgenic plants. Grain length, grain number per panicle, thousand-grain weight, 2-acetyl-1-pyrroline (2-AP) content, and rice blast resistance of homozygous lines were measured in the T₃ generations. Results: Effectively edited plants were obtained in the T₀ generation. The simultaneous editing efficiency for all five genes reached 9.38%. The individual gene editing efficiencies for Pi21, GS3, OsBADH2, Gn1a, and OsPIL15 were 78%, 63%, 56%, 54%, and 13%, respectively. Five five-gene homozygous edited lines with two genotypes were selected in the T₂ generation. In the T₃ generation, compared with the wild-type (WT), the edited homozygous lines showed increased grain number per panicle (14.60–25.61%), increased grain length (7.39–11.16%), increased grain length–width ratio (8.37–13.02%), increased thousand-grain weight (3.79–9.15%), a 42–64 folds increase in the fragrant substance 2-AP content, and significantly enhanced rice blast resistance. Meanwhile, there were no significant changes in other agronomic traits. Conclusions: CRISPR/Cas9-mediated multiplex gene editing technology enabled the simultaneous editing of genes related to rice yield, quality, and disease resistance. This provides an effective approach for obtaining new japonica rice germplasm with blast resistance, long grains, and fragrance. Full article

This paper presents new aggregation operators for $p,q,r$-fractional fuzzy sets based on the Frank t-norm and t-conorm. We introduce the $p,q,r$-fractional fuzzy Frank weighted average and $p,q,r$-fractional fuzzy Frank weighted geometric operators and discuss their algebraic properties, including closure, boundedness, idempotency, and monotonicity. Based on new operations, we develop a multi-criteria group decision-making framework that integrates the evaluations of multiple experts via the proposed Frank operators and ranks the alternatives under $p,q,r$-fractional fuzzy information. The model is applied to a cryptocurrency stability assessment problem, where four coins are evaluated with respect to six criteria. The results show that both aggregation operators yield consistent rankings with good discriminatory power among the alternatives. A sensitivity analysis is conducted to check the stability of the model under parameter variations. A comparative study further demonstrates the compatibility and advantages of the proposed method over several existing decision-making approaches. The proposed framework is well suited to decision-making scenarios in which multiple experts’ opinions must be integrated within a complex fuzzy information environment. Full article

The celebrated question of whether $P=NP$ continues to define the boundary between the feasible and the intractable in computer science. In this paper, we revisit the problem from two complementary angles: Time-Relative Description Complexity and automated discovery, adopting an epistemic rather than ontological perspective. Even if polynomial-time algorithms for NP-complete problems do exist, their minimal descriptions may have very high Kolmogorov complexity. This creates what we call an epistemic barrier, making such algorithms effectively undiscoverable by unaided human reasoning. A series of structural results—relativization, Natural Proofs, and the Probabilistically Checkable Proofs (PCPs) theorem—already indicate that classical proof techniques are unlikely to resolve the question, which motivates a more pragmatic shift in emphasis. We therefore ask a different, more practical question: what can systematic computational search achieve within these limits? We propose a certificate-first workflow for algorithmic discovery, in which candidate algorithms are considered scientifically credible only when accompanied by machine-checkable evidence. Examples include Deletion/Resolution Asymmetric Tautology (DRAT)/Flexible RAT (FRAT) proof logs for SAT, Linear Programming (LP)/Semidefinite Programming (SDP) dual bounds for optimization, and other forms of independently verifiable certificates. Within this framework, high-capacity search and learning systems can explore algorithmic spaces far beyond manual (human) design, yet still produce artifacts that are auditable and reproducible. Empirical motivation comes from large language models and other scalable learning systems, where increasing capacity often yields new emergent behaviors even though internal representations remain opaque. This paper is best described as a position and expository essay that synthesizes insights from complexity theory, Kolmogorov complexity, and automated algorithm discovery, using Time-Relative Description Complexity as an organising lens and outlining a pragmatic research direction grounded in verifiable computation. We argue for a shift in emphasis from the elusive search for polynomial-time solutions to the constructive pursuit of high-performance heuristics and approximation methods grounded in verifiable evidence. The overarching message is that capacity plus certification offers a principled path toward better algorithms and clearer scientific limits without presuming a final resolution of $P\stackrel{?}{=}NP$. Full article

Chronic lymphocytic leukemia (CLL) is a heterogenous disease, with varied clinical outcomes. Multiplex assays used to measure soluble immune checkpoints offer a less laborious method of monitoring patients with CLL, but none of these panels have been validated. The aim of the study was to assess soluble immune checkpoint profiles in patients with CLL and to correlate these with independent prognostic markers such as β2-microglobulin (B2M), Rai stage, fluorescence in situ hybridization (FISH) status, and the International Prognostic Index for Chronic Lymphocytic Leukemia (CLL-IPI). We measured plasma levels of soluble interleukin-2 receptor alpha (sCD25), T cell immunoglobulin and mucin domain-containing protein 3 (TIM-3), galectin-9, programmed cell death 1 (PD-1), programmed death-ligand 1 (PD-L1), and cytotoxic T-lymphocyte associated protein 4 (CTLA-4) using cytometric bead array-based assays. We further measured plasma levels of B2M using an enzyme-linked immunosorbent assay (ELISA) kit. Soluble immune checkpoints were correlated with prognostic markers. The plasma levels of sCD25, TIM-3, galectin-9, PD-1, and PD-L1 were significantly increased in patients with CLL compared to the control group, p < 0.0001. Galectin-9 plasma levels were directly associated with B2M levels (β = 0.65, p = 0.012). Our findings suggest that galectin-9 may provide valuable prognostic significance for patients with CLL. Full article

Perimidine derivatives are versatile heterocycles with growing significance in medicinal chemistry and materials sciences. However, their structural variety remains limited. This study focused on the synthesis and crystal structure characterization of a new perimidine-based molecule. A bicyclic perimidine lactone, (1S,4R)-4,7,7-trimethyl-1-(1H-perimidin-2-yl)-2-oxabicyclo[2.2.1]heptan-3-one (1), was synthesized through an intramolecular dehydration of a monoamide intermediate formed from 1,8-diaminonaphthalene and (1S)-(–)-camphanic chloride under basic conditions. The product was purified and crystallized from acetone, giving single crystals suitable for X-ray diffraction. Structural analysis revealed two stereogenic centers and crystallization in the chiral tetragonal P4₃2₁2 space group, with stabilization through N—H···O and C—H···N hydrogen bonds as well as C—H···π interactions. Full article

Background: Diffuse large B-cell lymphoma (DLBCL) is a biologically heterogeneous malignancy, with various outcomes despite significant advances in therapeutic options. Current conventional prognostic tools, e.g., the International Prognostic Index (IPI), lack sufficient precision at an individual patient level. However, artificial intelligence (AI), including machine learning (ML) and deep learning (DL), can enable specialists to navigate complex datasets, with the final aim of improving prognostic models for DLBCL. Objectives: This scoping review aims to systematically map the current literature regarding the use of AI/ML techniques in DLBCL outcome prediction and risk stratification. We categorized studies by data modality and computational approach to identify key trends, knowledge gaps, and opportunities for their translation into current practice. Methods: We conducted a structured search of the PubMed/MEDLINE, Scopus, and Cochrane Library databases through July 2025 using terms related to DLBCL, prognosis, and AI/ML. Eligible studies included original papers applying AI/ML to predict survival outcomes, classify risk groups, or identify prognostic subtypes. Studies were categorized based on input modality: clinical, positron emission tomography/computed tomography (PET/CT) imaging, histopathology, transcriptomics, genomics, circulating tumor DNA (ctDNA), and multi-omics data. Narrative synthesis was performed in line with PRISMA-ScR guidelines. Results: From the 215 records screened, 91 studies met the inclusion criteria. Group-wise we report the following categories: clinical risk features (n = 8), PET/CT imaging (n = 30), CT (n = 1), digital pathology (n = 3), conventional histopathology (n = 2), gene expression profiling (n = 19), specific mutational signatures (n = 18), ctDNA (n = 3), microRNA (n = 2), and multi-omics integration (n = 5). The most common techniques reported amongst the papers included ensemble learning, convolutional neural networks (CNNs), and LASSO-based Cox models. Several AI techniques demonstrated superior predictive performance over IPI, with area under the curve (AUC) values frequently exceeding 0.80. Multi-omics models and ctDNA-based predictors showed strong potential for clinical translation, a perspective worth considering in further studies. Conclusions: AI/ML methods are increasingly used in DLBCL to improve prognostic accuracy by leveraging data types with diverse inputs. These approaches allow an enhanced stratification, superior to traditional indices, and support the early identification of high-risk patients, earlier guidance for therapy tailoring, and early trial enrollment for flagged cases. Future investigations should focus on external validation and improvement of model interpretability, with tangible perspectives of integration into real-world workflows and translation from bench to bedside. Full article

This paper presents the design, implementation, and experimental validation of a model-free control strategy for greenhouse climate automation. The proposed approach integrates a real-time embedded controller based on a Raspberry Pi within a Cyber-Physical System (CPS) framework. A Hardware-in-the-Loop (HIL) simulation architecture was developed, where the physical controller interacts with a high-fidelity digital twin (DT) of the greenhouse modeled in MATLAB/Simulink. This configuration allows a realistic assessment of the control performance, enabling the evaluation of system dynamics, actuator limitations, and response to disturbances before actual deployment. The control strategy is based on an intelligent Proportional–Integral (iPI) model-free algorithm, which avoids reliance on explicit process modeling and is well-suited for nonlinear and time-varying environments. Experimental results demonstrate the effectiveness, robustness, and practical feasibility of the proposed approach for real-time greenhouse automation. The use of low-cost hardware and the modular nature of the system make it scalable and attractive for precision agriculture and educational applications. Full article