Search Results (65)

In this paper, we present a mathematical model, $M_{siqr}$, to analyze the dynamics of mobile malware propagation in smartphone networks. The model segments the mobile device population into susceptible, exposed, infected, quarantined, and recovered compartments, integrating critical control parameters such as infection and quarantine rates. The analytical results include the derivation of the basic reproduction number, $R_0$, along with equilibrium and stability analyses that provide insights into long-term system behavior. A focused scenario analysis compares the baseline dynamics with a more aggressive malware variant and a more effective quarantine response. The results show that increased infectivity sharply escalates the peak of infection, while enhanced quarantine measures effectively suppress it. This highlights the importance of prompt containment strategies even under more virulent conditions. The sensitivity analysis identifies the infection rate as the most influential parameter driving peak infection, while the quarantine rate exerts the most significant dampening effect. Monte Carlo simulations of parameter uncertainty reveal a consistently high epidemic potential across varied conditions. A parameter sweep across the infection–quarantine space further maps out the conditions under which malware outbreaks can be mitigated or prevented. Overall, the model demonstrates that mobile malware poses sustained epidemic risk under uncertainty, but effective control parameters—particularly quarantine—can drastically alter outbreak trajectories. Full article

Currently, there is no standard treatment for renal cell carcinoma (RCC) that is free of side effects and resistance. Additionally, limited information exists on how curcumin affects the gene expression profiles of patients with translocation renal cell carcinoma (tRCC) and papillary renal cell carcinoma (pRCC). The pathways responsible for metastasis in tRCC are still not well understood, and there is no established treatment or reliable biomarker to predict outcomes for metastatic tRCC. Primary clinical data from patients were retrieved from the TCGA database and analyzed using cBioPortal, stitch, string, R and Python. Various analyses were performed, including differential gene expression, protein-protein interaction (PPI) network analysis, drug-targeted gene analysis, gene ontology (GO), enrichment analyses, and systematic searches to assess the impact of curcumin on the transcriptomic profiles of tRCC, pRCC, and clear cell renal cell carcinoma (ccRCC). No significant impact of sensitive genes on survival in KIRC and KIRP was found, though a trend suggested they may delay disease progression. The combination of curcumin with sunitinib showed promise in overcoming drug resistance in ccRCC by inducing ferroptosis, reducing iron, and increasing ADAMTS18 expression. This study, leveraging data from the TCGA database and other databases explored the impact of curcumin on transcriptomic profiles in tRCC, pRCC, and clear cell RCC (ccRCC). Gene analysis revealed immune and metabolic differences, with KIRC showing a stronger immune response. This study is the first to propose that future research into the miR-148/ADAMTS18 genes and the ferroptosis pathway in tRCC and pRCC could lead to the development of new therapies and the identification of novel therapeutic targets, potentially overcoming drug resistance and metastasis. Full article

Background: Differentiating histologic subtypes of fat-poor small renal masses using conventional imaging remains difficult due to their overlapping radiologic characteristics. We aimed to develop a machine learning-based diagnostic model using CT-derived radiomic features to classify the five most common renal tumor subtypes: clear cell RCC (ccRCC), papillary RCC (pRCC), chromophobe RCC (chRCC), angiomyolipoma (AML), and oncocytoma. Methods: A total of 499 patients with pathologically confirmed renal tumors who underwent preoperative contrast-enhanced CT and nephrectomy were retrospectively analyzed. Results: We extracted and analyzed radiomic features from 1548 multi-phase CT scans from 499 patients, focusing on fat-poor tumors. Five machine learning classifiers including Linear SVM, Rbf SVM, Random Forest, and XGBoost were involved. Among the models, XGBoost showed the best classification performance, with an average AU-PRC: mean = 0.757, standard error = 0.033 and a renal angiomyolipoma-specific AU-ROC: mean = 0.824, standard error = 0.023. These results outperformed other single-phase CT radiomic feature-based machine learning models trained with 20% of principal components. Conclusions: This study demonstrates the effectiveness of radiomics-based machine learning in classifying renal tumor subtypes and highlights the potential of AI in medical imaging. The findings, particularly the utility of single-phase CT and feature optimization, offer valuable insights for future precision medicine approaches. Such methods may support more personalized diagnosis and treatment planning in renal oncology. Full article

Background/Objectives: Early treatment assessment in metastatic renal cell carcinoma (mRCC) remains challenging due to the limited accuracy of current imaging methods. Given prostate-specific membrane antigen (PSMA) overexpression in mRCC, PSMA PET is a promising approach. Despite numerous studies on PSMA imaging in mRCC, data on PSMA uptake changes during systemic therapy are scarce. We analyzed PSMA uptake on PET after treatment initiation in mRCC patients. Methods: A retrospective single-center analysis of mRCC patients who underwent [¹⁸F]PSMA-1007 PET/CT before (PET₁) and at a mean of 9.5 weeks after (PET₂) starting systemic therapy was conducted. PSMA uptake in metastatic lesions was compared by region and RCC subtype. Uptake differences between PET₁ and PET₂ were analyzed using an unpaired t-test. Results: This study included 25 patients (mean age 65.2 ± 14.7 years; 20 male) with mRCC. A total of 113 (PET₁) and 48 (PET₂) metastases were assessed. Lymph node metastases showed stable PSMA uptake (median SUV_max) after treatment (7.8 vs. 7.7, p = 0.77), while uptake by bone (6.4 vs. 12.4, p = 0.03) and lung metastases (4.5 vs. 8.1, p = 0.004) increased significantly. SUV stability in lymph nodes was independent of RCC subtype (ccRCC: p = 0.48, pRCC: p > 0.99). Bone (6.6 vs. 15.9, p = 0.008) and lung metastases (4.8 vs. 8.1, p = 0.02) had higher PSMA uptake in ccRCC, unlike pRCC (bone: 6.2 vs. 6.0, p = 0.86). Conclusions: Alterations of PSMA-radioligand uptake are seen in bone and pulmonary metastases but not in lymph node metastases after initiation of systemic treatment in patients with mRCC. ccRCC has a higher PSMA uptake than other RCC subtypes. Full article

Background/Objectives: RNA-modifying proteins play a crucial role in the progression of cancer. The fat mass and obesity-associated protein (FTO) and alkB homolog 5 RNA demethylase (ALKBH5) are RNA-demethylating proteins that have contrasting effects in renal cell carcinoma (RCC) among different populations. This research investigates the genotype and expression levels of FTO and ALKBH5 in RCC patients from the Middle East and Northern Africa (MENA) region. Methods: Formalin-fixed paraffin-embedded samples from the kidney biopsies of RCC patients and controls were examined using targeted DNA sequencing, whole transcriptome profiling, and immunohistochemistry. Results: Our findings show that the rs11075995T variant in FTO is associated with a heightened risk of clear-cell RCC (ccRCC). ALKBH5 and FTO protein expression were significantly lower in ccRCC and chromophobe RCC (chRCC) patients but not in papillary RCC (pRCC) patients. In ccRCC, transcriptomic data revealed a significant downregulation of FTO (log₂FC = −5.2, q < 0.001) and ALKBH5 (log₂FC = −4.7, q < 0.001) compared to controls. A significant negative correlation was found in ccRCC between FTO expression and T allele frequency in rs11075995, suggesting that FTO expression is affected. Conclusions: This is the first demonstration of the association of the dysregulated expression of FTO and ALKBH5 in ccRCC and chRCC patients from the MENA region. FTO variant rs11075995T increased the risk of ccRCC and was negatively associated with FTO protein expression. Full article

Artificial intelligence (AI) plays a crucial role in modern healthcare by enhancing disease modeling and outbreak prediction. In this study, we develop an epidemiological model for the Marburg virus, integrating vaccination and treatment strategies while considering vaccine efficacy and treatment failure. The model exhibits mathematical symmetry in its equilibrium analysis, ensuring a balanced assessment of disease dynamics across human and bat reservoir populations. We compute the Marburg-free and endemic equilibrium points, derive the secondary infection threshold, and conduct sensitivity analysis using the PRCC method to identify key disease transmission parameters that are important for disease control. To validate the theory, we optimized a deep neural network (DNN) via grid search and employed it for dynamic analysis, which also validates the cutting-edge application of AI in healthcare. We also compare AI-based predictions with traditional numerical solutions for reproduction number for humans $R_{0h}>1$ and $R_{0h}<1$ for validation and efficacy of the AI approach. The results demonstrate the model’s stability, efficacy, and predictive power, emphasizing the synergy between AI and mathematical epidemiology. This study provides valuable insights for public health interventions and effective disease control strategies by leveraging AI-driven simulations, highlighting AI’s potential to revolutionize and enhance early detection and tailor treatment strategies. Full article

Objectives: To describe the US, CEUS, CT, and MRI features of papillary renal cell carcinoma (PRCC) and to underline the imaging characteristics that are helpful in the differential diagnosis. Methods: Patients with histologically proven papillary renal cell carcinoma who underwent at least two imaging examinations (US, CEUS, CT, and MRI) were included in the study. Tumor size, homogeneity, morphology, perilesional stranding, contrast enhancement locoregional extension were assessed. A comparison and the characteristics of the imaging features for each imaging modality were analyzed. Results: A total of 27 patients with an histologically confirmed diagnosis of PRCC were included in the study. US was highly accurate in distinguishing solid masses from cystic masses, supporting the differential diagnosis of PRCC, as well as in patients with a poor representation of the solid component. CEUS significantly increased diagnostic accuracy in delineating the solid intralesional component. Furthermore, when using CEUS, in the arterial phase, PRCC exhibited hypo-enhancement, and in the late phase it showed an inhomogeneous and delayed wash-out compared with the surrounding renal parenchyma. At MRI, PRCC showed a marked restiction of DWI and was hypointense in the T2-weighted compared to the renal parenchyma. Conclusions: In our study, the characteristic hypodensity and hypoenhancement of PRCC make CT the weakest method of their recognition, while US/CEUS and MRI are necessary to reach a definitive diagnosis. Knowledge of the appearance of PRCC can support an early diagnosis and prompt management, and radiologists should be aware that PRCC, when detected using CT, may resemble spurious non-septate renal cyst. Full article

In this paper, a stochastic continuous-time Markov chain (CTMC) model is developed and analyzed to explore the dynamics of cholera. The multitype branching process is used to compute a stochastic threshold for the CTMC model. Latin hypercube sampling/partial rank correlation coefficient (LHS/PRCC) sensitivity analysis methods are implemented to derive sensitivity indices of model parameters. The results show that the natural death rate $\left({\mu }_v\right)$ of a vector is the most sensitive parameter for controlling disease outbreaks. Numerical simulations indicate that the solutions of the CTMC stochastic model are relatively close to the solutions of the deterministic model. Numerical simulations estimate the probability of both disease extinction and outbreak. The probability of cholera extinction is high when it emerges from bacterial concentrations in non-contaminated/safe water in comparison to when it emerges from all infected groups. Thus, any intervention that focuses on reducing the number of infections at the beginning of a cholera outbreak is essential for reducing its transmission. Full article

Cloth-changing Person Re-Identification (CC-ReID) poses a significant challenge in tracking pedestrians across cameras while accounting for changes in clothing appearance. Despite recent progress in CC-ReID, existing methods predominantly focus on learning the unique biological features of pedestrians, often overlooking constraints that promote the learning of cloth-agnostic features. Addressing this limitation, we propose a Robust Fine-grained Learning Network (RFLNet) to effectively learn robust cloth-agnostic features by leveraging fine-grained semantic constraints. Specifically, we introduce a four-body-part attention module to enhance the learning of detailed pedestrian semantic features. To further strengthen the model’s robustness to clothing variations, we employ a random erasing algorithm, encouraging the network to concentrate on cloth-irrelevant attributes. Additionally, we design a fine-grained semantic loss to guide the model in learning identity-related, detailed semantic features, thereby improving its focus on cloth-agnostic regions. Comprehensive experiments on widely used CC-ReID benchmarks demonstrate the effectiveness of RFLNet. Our method achieves state-of-the-art performance, including a 0.7% increase in mAP on PRCC and a 1.6% improvement in rank-1 accuracy on DeepChange. Full article

TFE3-rearranged Renal Cell Carcinoma (TFE3-RCC) is an aggressive subtype of RCC characterized by Xp11.2 rearrangement, leading to TFE3 fusion proteins with oncogenic potential. Despite advances in understanding its molecular biology, effective therapies for advanced cases remain elusive. This study investigates the role of ARID2, a component of the SWI/SNF chromatin remodeling complex, in TFE3-RCC. Through a series of in vitro and in vivo experiments, we confirmed that ARID2 acts as a tumor suppressor in TFE3-RCC. ARID2 knockout (KO) enhanced TFE3-RCC cell migration, proliferation, and tumor growth. Transcriptomic analysis revealed ERBB3 as a key target gene regulated by both PRCC-TFE3 and ARID2. Chromatin immunoprecipitation (ChIP) assays demonstrated that PRCC-TFE3 directly binds to and upregulates ERBB3 expression, with ARID2 KO further enhancing this effect. TFE3-RCC ARID2 KO cells exhibited significant gene expression enrichment in MAPK and ERBB3 signaling pathways. These cells also showed increased activation of ERBB3, EGFR, and selective activation of SRC and MAPK. TFE3-RCC ARID2 KO cells demonstrated heightened sensitivity to the ERBB3 inhibitor AZD8931 compared to their wild-type counterparts, exhibiting significantly reduced migration and proliferation rates. These findings suggest that the PRCC-TFE3-ARID2-ERBB3 axis plays a critical role in TFE3-RCC pathogenesis and highlights the potential of targeting ERBB3 in ARID2-deficient TFE3-RCC as a therapeutic strategy. This study provides new insights into the molecular mechanisms of TFE3-RCC and suggests avenues for precision treatment of this aggressive cancer. Full article

Objective: Renal cell carcinoma (RCC) is a rare but severe and aggressive pediatric malignancy. While incidence is uncommon, survival is relatively low with respect to acute lymphocytic leukemia (ALL), AML, lymphoma, ependymoma, glioblastoma, and Wilms Tumor. The pediatric renal cell carcinoma (pRCC) incidence, cumulative incidence (period prevalence), and mortality vary by health disparities’ indicators, namely sex, race, ethnicity, age at tumor diagnosis, and social determinants of health (SDHs) as well as Epigenomic Determinants of Health (EDHs). However, studies are unavailable on some pRCC risk determinants, such as area of residence and socio-economic status (SES). The current study aimed at assessing the temporal trends, cumulative incidence, household median income, urbanity, mortality, and pRCC survival differentials. Materials and Methods: A retrospective cohort design was utilized to examine the event-free survival of children (0–19) with RCC using the Surveillance Epidemiology and End Result Data, 1973–2015. While the time-dependent variable, namely survival months, was utilized, we assessed the predictors of pRCC survival, mainly sex, age at diagnosis, education, insurance status, income, and tumor grade, as prognostic factors. In examining the joint effect of area of residence and race, as an exposure function with time in survival, we utilized the Cox proportional hazard model, while the annual percent change was assessed using a generalized linear model, implying a weighted average. Results: Between 1973 and 2015, there were 174 cases of pRCC, of whom 49 experienced mortality (28.2%). The pRCC cumulative incidence tends to increase with advancing age. A significant survival differential was observed between black/AA children with RCC and their white counterparts. Compared with white children, black/AA children were almost three times as likely to die, hazard ratio (HR) = 2.90, 95% CI = 1.56–5.31, p = 0.001. A survival differential was observed in sex, with males presenting with a 21% increased likelihood of dying, HR = 1.21; 95% CI, 0.69–2.11. In the metropolitan area, the risk of dying was almost three times as likely among black/AA children compared to their white counterparts, HR = 2.78; 95% CI, 1.45–5.43, while in the urban area, the risk of dying was almost four times as likely among black/AA children compared to their white counterparts, HR = 4.18; 95% CI, 0.84–20.80. After controlling for age, sex, education, and insurance, the risk of dying increased amongst black/AA children in metropolitan areas, adjusted HR (aHR) = 3.37, 99% CI = 1.35–8.44. In the urban area, after adjustment for age, sex, and insurance, there was an increased risk of dying for black/AA children, compared with their white counterparts with pRCC, aHR = 8.87, 99% CI = 2.77–28.10. Conclusion: pRCC indicates an increased trend in males and age at diagnosis between 10 and 14, as well as a survival disadvantage among black/AA children, compared with their white counterparts. Additionally, urbanity significantly influences the racial differences in survival. These data are suggestive of the conjoined effect of environment and race in pRCC survival, indicative of further assessment of gene–environment interaction (epigenomics) in incidence, mortality, and survival in pRCC. Full article

Background: Renal cell carcinoma (RCC) is a highly aggressive malignancy accounting for the majority of kidney cancers. Despite recent advancements in therapeutic options, the prognosis for advanced-stage RCC remains poor. Niemann–Pick C1-Like 1 (NPC1L1) plays a crucial role in cholesterol absorption and has been implicated in cancer progression across various cancers. However, its expression patterns and prognostic significance in RCC remain unclear. Methods: In this study, NPC1L1 expression in normal and RCC tissues, including subtypes, was compared using TCGA, GEPIA2, and The Human Protein Atlas. Clinical correlations were assessed, and the impact of NPC1L1 on overall survival (OS) and progression-free survival (PFS) was evaluated. Gene effect scores were analyzed using the DepMap tool to determine the involvement of NPC1L1 in RCC progression. Results: NPC1L1 expression was significantly lower in RCC tissues compared to normal tissues, particularly in the clear cell RCC (ccRCC), papillary RCC (pRCC), and chromophobe RCC (chRCC) subtypes, but increased in advanced tumor stages. Higher NPC1L1 expression was associated with worse OS and PFS in RCC patients. Multivariable Cox regression confirmed NPC1L1 as an independent prognostic marker. Additionally, gene effect scores showed that NPC1L1 is essential for the survival of specific RCC cell lines. Conclusions: This study determines NPC1L1 as an independent prognostic indicator in RCC, with higher expression associated with poor survival outcomes. These findings suggest that NPC1L1 could serve as a valuable marker for identifying high-risk RCC patients. Further research is required to investigate the molecular mechanisms underlying the role of NPC1L1 in RCC progression. Full article

Dengue fever, a mosquito-borne disease caused by the dengue virus, imposes a substantial disease burden on the world. Wolbachia not only manipulates the reproductive processes of mosquitoes through maternal inheritance and cytoplasmic incompatibility (CI) but also restrain the replication of dengue viruses within mosquitoes, becoming a novel approach for biologically combating dengue fever. A combined use of Wolbachia and insecticides may help to prevent pesky mosquito bites and dengue transmission. A model with impulsive spraying insecticide is introduced to examine the spread of Wolbachia in wild mosquitoes. We prove the stability and permanence results of periodic solutions in the system. Partial rank correlation coefficients (PRCCs) can determine the importance of the contribution of input parameters on the value of the outcome variable. PRCCs are used to analyze the influence of input parameters on the threshold condition of the population replacement strategy. We then explore the impacts of mosquito-killing rates and pulse periods on both population eradication and replacement strategies. To further investigate the effects of memory intensity on the two control strategies, we developed a Caputo fractional-order impulsive mosquito population model with integrated control measures. Simulation results show that for the low fecundity scenario of individuals, as memory intensity increases, the mosquito eradication strategy will occur at a slower speed, potentially even leading to the mosquito replacement strategy with low female numbers. For the high fecundity scenario of individuals, with increasing memory intensity, the mosquito replacement strategy will be achieved more quickly, with lower mosquito population amplitudes and overall numbers. It indicates that although memory factors are not conducive to implementing a mosquito eradication strategy, achieving the replacement strategy with a lower mosquito amount is helpful. This work will be advantageous for developing efficient integrated control strategies to curb dengue transmission. Full article

Background: Identical translocations involving the TFE3 gene and various partners have been found in both renal and soft tissue tumors, like alveolar soft part sarcoma (ASPSCR1), ossifying fibromyxoid tumor (PHF1), epithelioid hemangioendothelioma, and the clear cell stromal tumor of the lung (YAP1). Methods: Herein, we review in detail the clinicopathologic and molecular data of TFE3-rearranged renal tumors and propose our perspective, which may shed light on this emerging conundrum. Results: Among the kidney tumors carrying TFE3 translocations, most are morphologically heterogeneous carcinomas labeling for the tubular marker PAX8. The others are mesenchymal neoplasms known as PEComas, characterized by epithelioid cells co-expressing smooth muscle actin, cathepsin-K, melanogenesis markers, and sometimes melanin pigment deposition. Over the past 30 years, numerous TFE3 fusion partners have been identified, with ASPL/ASPSCR1, PRCC, SFPQ/PSF, and NONO being the most frequent. Conclusions: It is not well understood why similar gene fusions can give rise to renal tumors with different morpho-immunophenotypes, which may contribute to the recent disagreement regarding their classification. However, as these two entities, respectively, epithelial and mesenchymal in nature, are widely recognized by the pathology community and their clinicopathologic features well established, we overall believe it is still better to retain the names TFE3-rearranged renal cell carcinoma and TFE3-rearranged PEComa. Full article

This paper presents a novel approach to studying divorce dynamics and elimination strategies using nonlinear differential equations. A mathematical model is formulated to capture the key factors influencing divorce rates. The model undergoes a rigorous theoretical analysis, including parameter estimation, solution existence/uniqueness, positivity, boundedness, and invariant regions. A qualitative analysis explores equilibria, stability conditions, and a sensitivity analysis. Numerical simulations and discussions are presented to validate the model and shed light on divorce dynamics. Finally, conclusions and future research directions are outlined. This work offers valuable insights for understanding and potentially mitigating divorce rates through targeted interventions. Full article