Search Results (16)

The QR distribution, recently introduced for modeling lifetime data under Type-II censoring, offers a flexible framework for survival and reliability analysis. This study provides the first comprehensive evaluation of multiple modern estimation techniques for the QR distribution under Type-II censoring. We systematically compare classical maximum likelihood estimation with stochastic gradient descent variants (Momentum and Adam), Bayesian approaches including Maximum A Posteriori estimation, Markov Chain Monte Carlo, and Variational Inference, as well as machine learning-integrated methods such as amortized neural network inference. Using both synthetic and the real Veterans’ Administration Lung Cancer dataset, we evaluate these methods in terms of parameter estimation accuracy, computational efficiency, and convergence behavior. The results demonstrate the strengths of optimization-based, Bayesian, and neural approaches, highlighting their practical utility in handling complex censored survival data. This research validates the distribution’s effectiveness in capturing survival dynamics, offering valuable insights for clinical applications and highlighting areas for methodological improvement. Full article

A detailed picture of how an aerosol is transported and deposited in the self-affine bronchial tree structure of patients is fundamental to design and optimize orally inhaled drug products. This work describes a Monte Carlo-based statistical deposition model able to simulate aerosol transport and deposition in a 3D human bronchial tree. The model enables working with complex and realistic inhalation maneuvers including breath-holding and exhalation. It can run on fully stochastically generated bronchial trees as well as on those whose proximal airways are extracted from patient chest scans. However, at present, a mechanical breathing model is not explicitly included in our trees; their ventilation can be controlled by means of heuristic airflow splitting rules at bifurcations and by an alveolation index controlling the distal lung volume. Our formulation allows us to introduce different types of pathologies on the trees, both those altering their morphology (e.g., bronchiectasis and chronic obstructive pulmonary disease) and those impairing their function (e.g., interstitial lung diseases and emphysema). In this initial activity we describe deposition and ventilation models as well as the stochastic tree construction algorithm, and we validate them against total, regional, lobar, and sub-lobar deposition for healthy subjects. Full article

Background/Objectives: Dry powder inhalation is an attractive research area for development. Therefore, this work aimed to develop inhalable co-spray-dried theophylline (TN) microparticles, utilizing raffinose-amino acid fine carriers intended for asthma therapy. The study addressed enhancing TN’s physicochemical and aerodynamic properties to ensure efficient lung deposition. Methods: The process involves spray-drying each formulation’s solution using a mini spray drier. A rigorous assessment was conducted on particle size distribution, structural and thermal analysis, morphology study, in vitro and in silico aerodynamic investigation, and aerodynamic particle counter in addition to the solubility, in vitro dissolution, and diffusion of TN. Results: The carriers containing leucine and glycine revealed superior characteristics (mass median aerodynamic diameter (MMAD): 4.6–5 µm, fine particle fraction (FPF): 30.6–35.1%, and amorphous spherical structure) as candidates for further development of TN-DPIs, while arginine was excluded due to intensive aggregation and hygroscopicity, which led to poor aerodynamic performance. TN co-spray-dried samples demonstrated fine micronized particles (D [0.5]: 3.99–5.96 µm) with predominantly amorphous structure (crystallinity index: 24.1–45.2%) and significant solubility enhancement (~19-fold). Formulations containing leucine and leucine-glycine revealed the highest FPF (45.7–47.8%) and in silico lung deposition (39.3–40.1%), rapid in vitro drug release (~100% within 10 min), and improved in vitro diffusion (2.29–2.43-fold), respectively. Moreover, the aerodynamic counter confirmed the development of fine microparticles (mean number particle size = 2.3–2.02 µm). Conclusions: This innovative formulation possesses enhanced physicochemical, morphological, and aerodynamic characteristics of low-dose TN for local asthma treatment and could be applied as a promising carrier for dry powder inhaler development. Full article

Background: Cystic fibrosis is a hereditary disease, which causes the accumulation of dense mucus in the lungs accompanied by frequent local inflammation. The non-steroidal anti-inflammatory drug ibuprofen (IBU) and the mucolytic mannitol (MAN) can treat these symptoms. Compared to per os administration, a lower dose of these drugs is sufficient to achieve the desired effect by delivering them in a pulmonary manner. However, it is still a challenge to administer high drug doses to the lungs. We aim to develop two inhaled powder formulations, a single-drug product of MAN and a combined formulation containing IBU and MAN. Methods: MAN was dissolved in an aqueous solution of Poloxamer-188 (POL). In the case of the combined formulation, a suspension was first prepared in a planetary mill via wet milling in POL medium. After the addition of leucine (LEU), the formulations were spray-dried. The prepared DPI samples were analyzed by using laser diffraction, scanning electron microscopy, powder X-ray diffraction, differential scanning calorimetry, density tests, in vitro aerodynamic studies (Andersen Cascade Impactor, Spraytec^® device), in vitro dissolution tests in artificial lung fluid, and in silico tests with stochastic lung model. Results: The DPIs showed suitability for inhalation with low-density spherical particles of appropriate size. The LEU-containing systems were characterized by high lung deposition and adequate aerodynamic diameter. The amorphization during the procedures resulted in rapid drug release. Conclusions: We have successfully produced a single-drug formulation and an innovative combination formulation, which could provide complex treatment for patients with cystic fibrosis to improve their quality of life. Full article

Accumulating scientific evidence highlights the pivotal role of miRNA–disease association research in elucidating disease pathogenesis and developing innovative diagnostics. Consequently, accurately identifying disease-associated miRNAs has emerged as a prominent research topic in bioinformatics. Advances in graph neural networks (GNNs) have catalyzed methodological breakthroughs in this field. However, existing methods are often plagued by data noise and struggle to effectively integrate local and global information, which hinders their predictive performance. To address this, we introduce HGTMDA, an innovative hypergraph learning framework that incorporates random walk with restart-based association masking and an enhanced GCN-Transformer model to infer miRNA–disease associations. HGTMDA starts by constructing multiple homogeneous similarity networks. A novel enhancement of our approach is the introduction of a restart-based random walk association masking strategy. By stochastically masking a subset of association data and integrating it with a GCN enhanced by an attention mechanism, this strategy enables better capture of key information, leading to improved information utilization and reduced impact of noisy data. Next, we build an miRNA–disease heterogeneous hypergraph and adopt an improved GCN-Transformer encoder to effectively solve the effective extraction of local and global information. Lastly, we utilize a combined Dice cross-entropy (DCE) loss function to guide the model training and optimize its performance. To evaluate the performance of HGTMDA, comprehensive comparisons were conducted with state-of-the-art methods. Additionally, in-depth case studies on lung cancer and colorectal cancer were performed. The results demonstrate HGTMDA’s outstanding performance across various metrics and its exceptional effectiveness in real-world application scenarios, highlighting the advantages and value of this method. Full article

Accurate detection of respiratory system damage including COVID-19 is considered one of the crucial applications of deep learning (DL) models using CT images. However, the main shortcoming of the published works has been unreliable reported accuracy and the lack of repeatability with new datasets, mainly due to slice-wise splits of the data, creating dependency between training and test sets due to shared data across the sets. We introduce a new dataset of CT images (ISFCT Dataset) with labels indicating the subject-wise split to train and test our DL algorithms in an unbiased manner. We also use this dataset to validate the real performance of the published works in a subject-wise data split. Another key feature provides more specific labels (eight characteristic lung features) rather than being limited to COVID-19 and healthy labels. We show that the reported high accuracy of the existing models on current slice-wise splits is not repeatable for subject-wise splits, and distribution differences between data splits are demonstrated using t-distribution stochastic neighbor embedding. We indicate that, by examining subject-wise data splitting, less complicated models show competitive results compared to the exiting complicated models, demonstrating that complex models do not necessarily generate accurate and repeatable results. Full article

The health effects of particles are directly related to their deposition patterns (deposition site and amount) in human airways. However, estimating the particle trajectory in a large-scale human lung airway model is still a challenge. In this work, a truncated single-path, large-scale human airway model (G3–G10) with a stochastically coupled boundary method were employed to investigate the particle trajectory and the roles of their deposition mechanisms. The deposition patterns of particles with diameters (dp) of 1–10 μm are investigated under various inlet Reynolds numbers (Re = 100–2000). Inertial impaction, gravitational sedimentation, and combined mechanism were considered. With the increasing airway generations, the deposition of smaller particles (dp < 4 μm) increased due to gravitational sedimentation, while that of larger particles decreased due to inertial impaction. The obtained formulas of Stokes number and Re can predict the deposition efficiency due to the combined mechanism in the present model, and the prediction can be used to assess the dose-effect of atmospheric aerosols on the human body. Diseases in deeper generations are mainly attributed to the deposition of smaller particles under lower inhalation rates, while diseases at the proximal generations mainly result from the deposition of larger particles under higher inhalation rates. Full article

(1) Background: Lung cancer causes a substantial epidemiological burden in Greece. Yet, no formal national lung cancer screening program has been introduced to date. This study modeled the impact on lung cancer life years (LCLY) of a hypothetical scenario of comprehensive screening for lung cancer with low-dose computed tomography (LDCT) of the high-risk population in Greece, as defined by the US Preventive Services Taskforce, would be screened and linked to care (SLTC) for lung cancer versus the current scenario of background (opportunistic) screening only; (2) Methods: A stochastic model was built to monitor a hypothetical cohort of 100,000 high-risk men and women as they transitioned between health states (without cancer, with cancer, alive, dead) over 5 years. Transition probabilities were based on clinical expert opinion. Cancer cases, cancer-related deaths, and LCLYs lost were modeled in current and hypothetical scenarios. The difference in outcomes between the two scenarios was calculated. 150 iterations of simulation scenarios were conducted for 100,000 persons; (3) Results: Increasing SLTC to a hypothetical 100% of eligible high-risk people in Greece leads to a statistically significant reduction in deaths and in total years lost due to lung cancer, when compared with the current SLTC paradigm. Over 5 years, the model predicted a difference of 339 deaths and 944 lost years between the hypothetical and current scenario. More specifically, the hypothetical scenario led to fewer deaths (−24.56%, p < 0.001) and fewer life years lost (−31.01%, p < 0.001). It also led to a shift to lower-stage cancers at the time of diagnosis; (4) Conclusions: Our study suggests that applying a 100% screening strategy amongst high-risk adults aged 50–80, would result in additional averted deaths and LCLYs gained over 5 years in Greece. Full article

Since the outbreak of COVID-19, the coronavirus has caused a massive threat to people’s lives. With the development of artificial intelligence technology, identifying key features in medical images through deep learning, infection cases can be screened quickly and accurately. This paper uses deep-learning-based approaches to classify COVID-19 and normal (healthy) chest X-ray images. To effectively extract medical X-ray image features and improve the detection accuracy of COVID-19 images, this paper extracts the texture features of X-ray images based on the gray level co-occurrence matrix and then realizes feature selection by principal components analysis (PCA) and t-distributed stochastic neighbor embedding (T-SNE) algorithms. To improve the accuracy of X-ray image detection, this paper designs a COVID-19 X-ray image detection model based on the multi-head self-attention mechanism and residual neural network. It applies the multi-head self-attention mechanism to the residual network bottleneck layer. The experimental results show that the multi-head self-attention residual network (MHSA-ResNet) detection model has an accuracy of 95.52% and a precision of 96.02%. It has a good detection effect and can realize the three classifications of COVID-19 pneumonia, common pneumonia, and normal lungs, proving the method’s effectiveness and practicability in this paper. Full article

The estimation of the dose of inhaled nanomaterials is of fundamental importance in occupational and environmental health. Indeed, the toxicology and risk assessment of inhaled NMs depends on deposition rates in various parts of the lung, coupled with clearance/retention rates that depend on processes such as physical removal by ciliary clearance, macrophage-mediated clearance and lymphatic clearance, together with dissolution and disintegration. A number of lung dosimetry models have been designed to estimate the deposition and retention of inhaled particles, including empirical models, deterministic models, stochastic statistical models and mechanistic multiple-path models. Various assumptions are used in these models, including use of a symmetrical or asymmetrical lung, which affects the performance of these models. This study presents the most recent developments of in vivo dosimetry in nanotoxicology, with a focus on the design and modelling approach, and the required input data used, together with verification and validation status of the model. Widely implemented models in nanotoxicology were identified and analyzed, i.e., the Multiple Path Particle Dosimetry (MPPD) model, International Commission on Radiological Protection (ICRP) models, the National Council on Radiation Protection and Measurement (NCRP) model, the Exposure Dose Model (ExDoM) and the Integrated Exposure and Dose Modeling and Analysis System (EDMAS). Full article

A better understanding of the process of melanoma metastasis is required to underpin the development of novel therapies that will improve patient outcomes. The use of appropriate animal models is indispensable for investigating the mechanisms of melanoma metastasis. However, reliable and practicable quantification of metastases in experimental mice remains a challenge, particularly if the metastatic burden is low. Here, we describe a qRT-PCR-based protocol that employs the melanocytic marker Trp-1 for the sensitive quantification of melanoma metastases in the murine lung. Using this protocol, we were able to detect the presence of as few as 100 disseminated melanoma cells in lung tissue. This allowed us to quantify metastatic burden in a spontaneous syngeneic B16-F10 metastasis model, even in the absence of visible metastases, as well as in the autochthonous Tg(Grm1)/Cyld^−/− melanoma model. Importantly, we also observed an uneven distribution of disseminated melanoma cells amongst the five lobes of the murine lung, which varied considerably from animal to animal. Together, our findings demonstrate that the qRT-PCR-based detection of Trp-1 allows the quantification of low pulmonary metastatic burden in both transplantable and autochthonous murine melanoma models, and show that the analysis of lung metastasis in such models needs to take into account the stochastic distribution of metastatic lesions amongst the lung lobes. Full article

Background: Lung transplantation is a specialized procedure used to treat chronic end-stage respiratory diseases. Due to the scarcity of lung donors, constructing fair and equitable lung transplant allocation methods is an issue that has been addressed with different strategies worldwide. This work aims to describe how Italy’s “national protocol for the management of surplus organs in all transplant programs” functions through an online app to allocate lung transplants. We have developed two probability models to describe the allocation process among the various transplant centers. An online app was then created. The first model considers conditional probabilities based on a protocol flowchart to compute the probability for each area and transplant center to receive each n-th organ in the period considered. The second probability model is based on the generalization of the binomial distribution to correlated binary variables, which is based on Bahadur’s representation, to compute the cumulative probability for each transplant center to receive at least nth organs. Our results show that the impact of the allocation of a surplus organ depends mostly on the region where the organ was donated. The discrepancies shown by our model may be explained by a discrepancy between the northern and southern regions in relation to the number of organs donated. Full article

Cancer is the leading cause of death in Taiwan. According to the Cancer Registration Report of Taiwan’s Ministry of Health and Welfare, a total of 13,488 people suffered from lung cancer in 2016, making it the second-most common cancer and the leading cancer in men. Compared with other types of cancer, the incidence of lung cancer is high. In this study, the National Health Insurance Research Database (NHIRDB) was used to determine the diseases and symptoms associated with lung cancer, and a 10-year probability deep neural network prediction model for lung cancer was developed. The proposed model could allow patients with a high risk of lung cancer to receive an earlier diagnosis and support the physicians’ clinical decision-making. The study was designed as a cohort study. The subjects were patients who were diagnosed with lung cancer between 2000 and 2009, and the patients’ disease histories were back-tracked for a period, extending to ten years before the diagnosis of lung cancer. As a result, a total of 13 diseases were selected as the predicting factors. A nine layers deep neural network model was created to predict the probability of lung cancer, depending on the different pre-diagnosed diseases, and to benefit the earlier detection of lung cancer in potential patients. The model is trained 1000 times, the batch size is set to 100, the SGD (Stochastic gradient descent) optimizer is used, the learning rate is set to 0.1, and the momentum is set to 0.1. The proposed model showed an accuracy of 85.4%, a sensitivity of 72.4% and a specificity of 85%, as well as an 87.4% area under ROC (AUROC) (95%, 0.8604–0.8885) model precision. Based on data analysis and deep learning, our prediction model discovered some features that had not been previously identified by clinical knowledge. This study tracks a decade of clinical diagnostic records to identify possible symptoms and comorbidities of lung cancer, allows early prediction of the disease, and assists more patients with early diagnosis. Full article

Pulmonary delivery has high bioavailability, a large surface area for absorption, and limited drug degradation. Particle engineering is important to develop inhalable formulations to improve the therapeutic effect. In our work, the poorly water-soluble meloxicam (MX) was used as an active ingredient, which could be useful for the treatment of non-small cell lung cancer, cystic fibrosis, and chronic obstructive pulmonary disease. We aimed to produce inhalable “nano-in-micro” dry powder inhalers (DPIs) containing MX and additives (poly-vinyl-alcohol, leucine). We targeted the respiratory zone with the microcomposites and reached a higher drug concentration with the nanonized active ingredient. We did the following investigations: particle size analysis, morphology, density, interparticular interactions, crystallinity, in vitro dissolution, in vitro permeability, in vitro aerodynamics (Andersen cascade impactor), and in silico aerodynamics (stochastic lung model). We worked out a preparation method by combining wet milling and spray-drying. We produced spherical, 3–4 µm sized particles built up by MX nanoparticles. The increased surface area and amorphization improved the dissolution and diffusion of the MX. The formulations showed appropriate aerodynamical properties: 1.5–2.4 µm MMAD and 72–76% fine particle fraction (FPF) values. The in silico measurements proved the deposition in the deeper airways. The samples were suitable for the treatment of local lung diseases. Full article

This review updates the scientific literature concerning asbestos and lung cancer, emphasizing cumulative exposure and synergism between asbestos exposure and tobacco smoke, and proposes an evidence-based and equitable approach to compensation for asbestos-related lung cancer cases. This update is based on several earlier reviews written by the second and third authors on asbestos and lung cancer since 1995. We reevaluated the peer-reviewed epidemiologic studies. In addition, selected in vivo and in vitro animal studies and molecular and cellular studies in humans were included. We conclude that the mechanism of lung cancer causation induced by the interdependent coaction of asbestos fibers and tobacco smoke at a biological level is a multistage stochastic process with both agents acting conjointly at all times. The new knowledge gained through this review provides the evidence for synergism between asbestos exposure and tobacco smoke in lung cancer causation at a biological level. The evaluated statistical data conform best to a multiplicative model for the interaction effects of asbestos and smoking on the lung cancer risk, with no requirement for asbestosis. Any asbestos exposure, even in a heavy smoker, contributes to causation. Based on this information, we propose criteria for the attribution of lung cancer to asbestos in smokers and non-smokers. Full article