Search Results (17)

Non-melanoma skin cancer (NMSC), comprising basal cell carcinoma (BCC) and cutaneous squamous cell carcinoma (cSCC), represents the most common type of cancer worldwide, particularly among Caucasians. While BCC is locally invasive with minimal metastatic potential, cSCC is a highly aggressive tumor with a significant potential for metastasis, particularly in elderly populations. Tumor development and progression and the metastasis of cSCC are influenced by a complex interplay between tumor cells and the tumor microenvironment. Recent research highlights the importance of various immune cell subsets, including T cells, tumor-associated macrophages (TAMs), and dendritic cells, in influencing tumor progression, immune evasion, and treatment resistance. This review outlines key regulatory mechanisms in the immune tumor microenvironment (TME) of cSCC and explores the role of cytokines, immune checkpoints, and stromal interactions. We further discuss the relevance of three-dimensional (3D) in vitro models such as spheroids, organoids, and tumor-on-chip systems as tools to mimic immune–tumor interactions with higher physiological relevance, such as macrophage activation and polarization against cSCC cells. Globally, 3D models offer new opportunities for immunotherapy screening and mechanistic studies. Understanding the immune landscape in cSCC through advanced modeling techniques holds strong clinical potential for improving diagnostic and therapeutic strategies. Full article

Continuous improvement (CI) is vital for Saudi manufacturing firms to remain competitive in the global market. However, cultural factors significantly influence CI adoption. This qualitative study, involving 28 interviews and focus groups with employees from five local manufacturing firms, explored these factors. Seven key cultural themes emerged, including communication, employee wellbeing, talent management, ethics, top management support, organisational learning, and compliance. A conceptual framework was developed to assess a firm’s cultural proximity to an ideal CI state. This framework integrates a diagnostic tool to guide firms in evaluating their cultural landscape and implementing targeted interventions for successful CI adoption. Future research should explore the long-term impacts of cultural shifts on performance and competitiveness. Full article

Promoting the intensive utilization of arable land is a critical strategy for addressing the scarcity problem of arable land resources and thus ensuring food security. However, public emergencies pose significant challenges to the intensive utilization of arable land. Based on the pressure-state response (PSR) model and taking Sichuan Province, known as China’s “Heavenly Granary”, as an example, this study constructs a suitable evaluation system and analyzes the variation trend of the intensive utilization of arable land from the perspective of public emergencies. Key factors constraining the intensive utilization of arable land are further analyzed using the obstacle diagnostic model. The findings of this study are as follows: (1) Despite the shocks of public emergencies, the intensive utilization level of arable land in Sichuan Province in China shows an overall upward trend, indicating a high level of resilience and adaptability. (2) The pressure to utilize arable land intensively in Sichuan exhibits periodic fluctuations, yet the state remains generally stable. The whole system shows positive adaptive responses to external pressures and contemporary conditions during the mid-to-late stages of the research period. Nevertheless, coordination among subsystems within the PSR framework remains suboptimal, and a dynamic equilibrium across the subsystems has not yet been achieved. (3) Obstacle factors constraining the intensive arable land utilization in Sichuan exhibit notable temporal variations. Early-period constraints centered on multiple cropping indexes, grain yield per unit area, and irrigation index, reflecting limitations of traditional agricultural production modes. In the later stages, key obstacles shifted to factors including per capita cultivated land, population density, and pesticide/fertilizer input index, highlighting the impediment effects caused by evolving socio-demographic dynamics influenced by public emergencies. The findings of this study reveal critical pathways for local governments to achieve sustainable arable land management amidst global uncertainties. Full article

Background: Helicobacter pylori is a globally prevalent pathogen and a major contributor to gastric diseases, including chronic gastritis, peptic ulcer disease, and gastric cancer. This study investigates the prevalence, distribution, and clinical relevance of its key virulence genes, vacA and cagA, in a Peruvian patient cohort. Materials and Methods: Fifty-one gastric biopsies were collected from patients with a presumptive diagnosis of H. pylori-induced gastritis at Hospital Carlos Alberto Seguín Escobedo in Arequipa, Peru, in March 2024. Two biopsies per patient—one from the antrum and one from the gastric body—were obtained during endoscopy. DNA extraction was performed using the Quick-DNA Fungal/Bacterial Kit (Zymo Research, USA). Molecular identification of H. pylori was conducted via PCR targeting the glmM gene, while the vacA and cagA virulence genes were detected using specific primers. Statistical analyses, including Pearson’s chi-square and Mann–Whitney tests, were applied to assess associations between virulence gene presence and clinical or histopathological variables. Results: Among the gastric biopsies, the vacA gene was detected in 37.3% of samples, while cagA was present in 17.6%. Statistical analysis revealed significant associations between vacA and specific clinical and endoscopic features, including erythematous gastropathy, nodular gastritis, and emetic syndrome, suggesting its localized role in disease pathogenesis. Additionally, the presence of cagA was significantly linked to moderate inflammatory intensity in gastric body biopsies, indicating its association with more severe histopathological outcomes. Chronic gastritis was the most common histopathological finding, with moderate intensity correlating strongly with the presence of virulence genes. Conclusions: These findings highlight substantial regional variability in the distribution and pathogenicity of H. pylori genotypes. This study underscores the importance of incorporating molecular diagnostics into routine clinical practice to improve diagnostic accuracy and inform region-specific therapeutic strategies. This is particularly crucial in endemic regions like Peru, where unique environmental and genetic factors may influence infection dynamics and disease outcomes. Full article

Colic is a leading cause of mortality in horses, demanding precise and timely interventions. This study integrates machine learning and explainable artificial intelligence (XAI) to predict survival outcomes in horses with colic, using clinical, procedural, and diagnostic data. Random forest and XGBoost emerged as top-performing models, achieving F1 scores of 85.9% and 86.1%, respectively. SHAP (Shapley additive explanations) was employed to provide interpretable insights, offering both global and local explanations for model predictions. The analysis revealed that key features, such as pulse rate, lesion type, and total protein levels, significantly influenced survival likelihood. Local interpretations highlighted the unique contribution of clinical factors to individual cases, enabling personalized insights that guide targeted treatment strategies. These tailored predictions empower veterinarians to prioritize interventions based on the specific conditions of each horse, moving beyond generalized care protocols. By combining predictive accuracy with interpretability, this study advances precision veterinary medicine, enhancing outcomes for equine colic cases and setting a benchmark for future applications of AI in animal health. Full article

This study analyzed 2102 conjunctival lesions excised between 1981 and 2003 at a single tertiary center in Serbia, with the aim of evaluating their histopathological characteristics, anatomical localization, and demographic distribution. Of the total cases recorded, 55.1% were male, indicating a slight male predominance. The bulbar conjunctiva was the most commonly affected site (34.5%), with 39.3% of tumors extended to multiple regions of the conjunctiva, including areas such as the plica and caruncula. The most common benign lesion was compound conjunctival nevus (16.7%), while squamous cell carcinoma (SCC) (11.4%) and melanoma (11.3%) were the most prevalent malignant tumors. Tumor incidence peaked in the 61–70 and 51–60 year age groups, with malignant tumors such as SCC being more frequent in males. Comparisons with similar global studies reveal that our findings align with worldwide trends, such as the predominance of SCC, which has been linked to UV exposure, and the frequency of melanoma in fair-skinned populations. However, the lower prevalence of fibrodegenerative lesions like pterygia and pinguecula in our cohort likely reflects Serbia’s cooler climate compared to regions with higher UV exposure. These findings underscore the diverse nature of conjunctival tumors, the critical role of histopathological examination for diagnosis, and the influence of environmental factors. This study provides valuable insights into the epidemiology of conjunctival tumors, contributing to global understanding and guiding future diagnostic and therapeutic approaches. Full article

Current medical limitations in predicting cancer survival status and time necessitate advancements beyond traditional methods and physical indicators. This research introduces a novel two-stage prognostic framework for renal cell carcinoma, addressing the inadequacies of existing diagnostic approaches. In the first stage, the framework accurately predicts the survival status (alive or deceased) with metrics Accuracy, Precision, Recall, and F1 score to evaluate the effects of the classification results, while the second stage focuses on forecasting the future survival time of deceased patients with Root Mean Square Error and Mean Absolute Error to evaluate the regression results. Leveraging popular machine learning models, such as Adaptive Boosting, Extra Trees, Gradient Boosting, Random Forest, and Extreme Gradient Boosting, along with fusion models like Voting, Stacking, and Blending, our approach significantly improves prognostic accuracy as shown in our experiments. The novelty of our research lies in the integration of a logistic regression meta-model for interpreting the blending model’s predictions, enhancing transparency. By the SHapley Additive exPlanations’ interpretability, we provide insights into variable contributions, aiding understanding at both global and local levels. Through modal segmentation and multimodal fusion applied to raw data from the Surveillance, Epidemiology, and End Results program, we enhance the precision of renal cell carcinoma prognosis. Our proposed model provides an interpretable analysis of model predictions, highlighting key variables influencing classification and regression decisions in the two-stage renal cell carcinoma prognosis framework. By addressing the black-box problem inherent in machine learning, our proposed model helps healthcare practitioners with a more reliable and transparent basis for applying machine learning in cancer prognostication. Full article

This study introduces Multi-Threshold Recurrence Rate Plots (MTRRP), a novel methodology for analyzing dynamic patterns in complex systems, such as those influenced by neurodegenerative diseases in brain activity. MTRRP characterizes how recurrence rates evolve with increasing recurrence thresholds. A key innovation of our approach, Recurrence Complexity, captures structural complexity by integrating local randomness and global structural features through the product of Recurrence Rate Gradient and Recurrence Hurst, both derived from MTRRP. We applied this technique to resting-state EEG data from patients diagnosed with Alzheimer’s Disease (AD), Frontotemporal Dementia (FTD), and age-matched healthy controls. The results revealed significantly higher recurrence complexity in the occipital areas of AD and FTD patients, particularly pronounced in the Alpha and Beta frequency bands. Furthermore, EEG features derived from MTRRP were evaluated using a Support Vector Machine with leave-one-out cross-validation, achieving a classification accuracy of 87.7%. These findings not only underscore the utility of MTRRP in detecting distinct neurophysiological patterns associated with neurodegenerative diseases but also highlight its broader applicability in time series analysis, providing a substantial tool for advancing medical diagnostics and research. Full article

Understanding the relationship between the formation, structure, and functionality of catalyst layers is crucial for designing catalyst layers with specific high-current-density operations. In this study, we investigated the impact of the ionomer-to-carbon (I/C) ratio and solid content on transport properties. We conducted fuel cell performance and diagnostic measurements to demonstrate the combined effects of the I/C ratio and solid content on the mass transport, particularly oxygen transport. To elucidate the roles of the I/C ratio and solid content in catalyst layer formation, we utilized dynamic light scattering and rheological measurements. By analyzing the local and global structure of ionomer-Pt/C assemblages in the catalyst inks, we observed that the I/C ratio and solid content influence the competition between homo-aggregation and hetero-aggregation, the strengths of inter- and intra-cluster bonds, and the rigidity and connectivity of the particulate structure. Additionally, high-shear-application simulations tend to reduce the connectivity of the particulate network and induce cluster densification, unless the global structure is mechanically stable and resilient. Based on this understanding, we established the formation–structure–functionality relationship for catalyst layers, thereby providing fundamental insights for designing catalyst layers tailored to specific functionalities. Full article

Recently, with more portable diagnostic devices being moved to people anywhere, point-of-care (PoC) imaging has become more convenient and more popular than the traditional “bed imaging”. Instant image segmentation, as an important technology of computer vision, is receiving more and more attention in PoC diagnosis. However, the image distortion caused by image preprocessing and the low resolution of medical images extracted by PoC devices are urgent problems that need to be solved. Moreover, more efficient feature representation is necessary in the design of instant image segmentation. In this paper, a new feature representation considering the relationships among local features with minimal parameters and a lower computational complexity is proposed. Since a feature window sliding along a diagonal can capture more pluralistic features, a Diagonal-Axial Multi-Layer Perceptron is designed to obtain the global correlation among local features for a more comprehensive feature representation. Additionally, a new multi-scale feature fusion is proposed to integrate nonlinear features with linear ones to obtain a more precise feature representation. Richer features are figured out. In order to improve the generalization of the models, a dynamic residual spatial pyramid pooling based on various receptive fields is constructed according to different sizes of images, which alleviates the influence of image distortion. The experimental results show that the proposed strategy has better performance on instant image segmentation. Notably, it yields an average improvement of 1.31% in Dice than existing strategies on the BUSI, ISIC2018 and MoNuSeg datasets. Full article

Introduction: Type 2 diabetes mellitus is a major global health concern, but interpreting machine learning models for diagnosis remains challenging. This study investigates combining association rule mining with advanced natural language processing to improve both diagnostic accuracy and interpretability. This novel approach has not been explored before in using pretrained transformers for diabetes classification on tabular data. Methods: The study used the Pima Indians Diabetes dataset to investigate Type 2 diabetes mellitus. Python and Jupyter Notebook were employed for analysis, with the NiaARM framework for association rule mining. LightGBM and the dalex package were used for performance comparison and feature importance analysis, respectively. SHAP was used for local interpretability. OpenAI GPT version 3.5 was utilized for outcome prediction and interpretation. The source code is available on GitHub. Results: NiaARM generated 350 rules to predict diabetes. LightGBM performed better than the GPT-based model. A comparison of GPT and NiaARM rules showed disparities, prompting a similarity score analysis. LightGBM’s decision making leaned heavily on glucose, age, and BMI, as highlighted in feature importance rankings. Beeswarm plots demonstrated how feature values correlate with their influence on diagnosis outcomes. Discussion: Combining association rule mining with GPT for Type 2 diabetes mellitus classification yields limited effectiveness. Enhancements like preprocessing and hyperparameter tuning are required. Interpretation challenges and GPT’s dependency on provided rules indicate the necessity for prompt engineering and similarity score methods. Variations in feature importance rankings underscore the complexity of T2DM. Concerns regarding GPT’s reliability emphasize the importance of iterative approaches for improving prediction accuracy. Full article

Background: Infections by multidrug-resistant (MDR) microorganisms are associated with increased morbidity and mortality in burn patients. This study aimed to analyze the evolution of MDR bacteria over a five-year period at Coimbra Burns Unit (CBU) in Portugal, seeking to assess the possible associations of specific bacteria with presumed risk factors. Methods: The data obtained consisted of identified bacteria present in any microbiological sample from each patient (including blood, central venous catheter, urine, tracheal aspirate and/or wound exudate). Univariate models and a multivariate model were constructed for each of the MDR bacteria species that infected at least 50 patients or that had five or more MDR strains. Statistical hypothesis tests with a p-value less than 0.05 were considered significant. Results: Of a total of 341 samples obtained, 107 were MDR, corresponding to 10 species. Globally, there was no significant variation in MDR bacteria frequency over the period under analysis. Some risk factors and/or trends were identified for some species, but none was linked to all of them. Conclusions: The risks for the development of MDR in bacteria in burn patients are multifactorial, mainly linked to longer hospital stays, the use of invasive devices and inadequate antimicrobial treatment. However, the influence of these risks regarding specific bacterial species is not straightforward and may rely on individual characteristics, type of treatment and/or local prevalent flora. Due to the severity of multidrug-resistant infections, continued microbiological surveillance with the aid of rapid diagnostic tests and prompt institution of appropriate antimicrobial therapy are crucial to improving outcomes for burn patients. Full article

In this study, the levels of fine particulate matter (PM_2.5), polycyclic aromatic hydrocarbons (PAHs) and nitro-PAHs (NPAHs) in PM_2.5 samples were determined from 2020 to 2021 in Singapore. For analysis convenience, the sampling period was classified according to two monsoon periods and the inter-monsoon period. Considering Singapore’s typically tropical monsoon climate, the four seasons were divided into the northeast monsoon season (NE), southwest monsoon season (SW), presouthwest monsoon season (PSW) and prenortheast monsoon season (PNE)). The PM_2.5 concentration reached 17.1 ± 8.38 μg/m³, which was slightly higher than that in 2015, and the average PAH concentration continuously declined during the sampling period compared to that reported in previous studies in 2006 and 2015. This is the first report of NPAHs in Singapore indicating a concentration of 13.1 ± 10.7 pg/m³. The seasonal variation in the PAH and NPAH concentrations in PM_2.5 did not obviously differ owing to the unique geographical location and almost uniform climate changes in Singapore. Diagnostic ratios revealed that PAHs and NPAHs mainly originated from local vehicle emissions during all seasons. 2-Nitropyrene (2-NP) and 2-nitrofluoranthene (2-NFR) in Singapore were mainly formed under the daytime OH-initiated reaction pathway. Combined with airmass backward trajectory analysis, the Indonesia air mass could have influenced Singapore’s air pollution levels in PSW. However, these survey results showed that no effect was found on the concentrations of PAHs and NPAHs in PM_2.5 in Indonesia during SW because of Indonesia’s efforts in the environment. It is worth noting that air masses from southern China could impact the PAH and NPAH concentrations according to long-range transportation during the NE. The results of the total incremental lifetime cancer risk (ILCR) via three exposure routes (ingestion, inhalation and dermal absorption) for males and females during the four seasons indicated a low long-term potential carcinogenic risk, with values ranging from 10⁻¹⁰ to 10⁻⁷. This study systematically explains the latest pollution conditions, sources, and potential health risks in Singapore, and comprehensively analyses the impact of the tropical monsoon system on air pollution in Singapore, providing a new perspective on the transmission mechanism of global air pollution. Full article

New technologies are supported by the global implementation of the internet. These improvements have deeply affected various disciplines of sciences and consequently changed services such as daily business, particularly health sectors. Innovative digital marketing strategies utilize the channels of social media and retrieved user data to analyze and improve relevant services. These multidisciplinary innovations can assist specialists, physicians and researchers in diagnostic, prophylaxis and treatment issues in the health sector. Accordingly, compared to recent decades, health decision makers are more accurate and trustful in defining new strategies. Interestingly, using social media and mobile health apps in current pandemics of SARS-CoV-2 could be an important instance of the key role of these platforms at the local and global level of health policies. These digital technologies provide platforms to connect public health sectors and health politicians for communicating and spreading relevant information. Adding influencers and campaigns to this toolbox strengthens the implementation of public health programs. In 2016, the WHO adopted a global program to eliminate viral hepatitis by 2030. Recent constructive measures that have been used in the battle against COVID-19 could be adopted for the elimination of viral hepatitis program. The presented evidence in our narrative review demonstrates that the application of digital marketing tools to create campaigns on social media, armed with professional influencers, can efficiently consolidate this program. The application of different strategies in using these popular tools will raise the public awareness about viral hepatitis. Subsequently, the availability of an effective vaccine for HBV and antiviral medication for HCV can motivate the audience to take steps towards prophylaxis and screening methods against these infectious illnesses. The encouragement of health policy makers to apply digital communication technologies and comprehensive roadmaps to implement this global program will certainly decrease the burden of viral hepatitis worldwide. Full article

According to global data, there is a male reproductive potential decrease. Pathogenesis of male infertility is often associated with autoimmunity towards sperm antigens essential for fertilization. Antisperm autoantibodies (ASAs) have immobilizing and cytotoxic properties, impairing spermatogenesis, causing sperm agglutination, altering spermatozoa motility and acrosomal reaction, and thus preventing ovum fertilization. Infertility diagnosis requires a mandatory check for the ASAs. The concept of the blood–testis barrier is currently re-formulated, with an emphasis on informational paracrine and juxtacrine effects, rather than simple anatomical separation. The etiology of male infertility includes both autoimmune and non-autoimmune diseases but equally develops through autoimmune links of pathogenesis. Varicocele commonly leads to infertility due to testicular ischemic damage, venous stasis, local hyperthermia, and hypoandrogenism. However, varicocelectomy can alter the blood–testis barrier, facilitating ASAs production as well. There are contradictory data on the role of ASAs in the pathogenesis of varicocele-related infertility. Infection and inflammation both promote ASAs production due to “danger concept” mechanisms and because of antigen mimicry. Systemic pro-autoimmune influences like hyperprolactinemia, hypoandrogenism, and hypothyroidism also facilitate ASAs production. The diagnostic value of various ASAs has not yet been clearly attributed, and their cut-levels have not been determined in sera nor in ejaculate. The assessment of the autoimmunity role in the pathogenesis of male infertility is ambiguous, so the purpose of this review is to show the effects of ASAs on the pathogenesis of male infertility. Full article