Search Results (19,301)

Environmental pollution indicated by the presence of $P{M}_{2.5}$ particulate matter varies based on prevailing atmospheric conditions described by certain meteorological variables. Consequently, it is important to understand atmospheric behavior in areas such as the Aburrá Valley, which experiences recurrent pollution events twice a year. This study examines the behavior of specific meteorological variables and $P{M}_{2.5}$ particulate matter in the Aburrá Valley. By using statistical analysis tools such as correlation coefficients, principal component analysis (PCA), and multiple linear regression models, the research identifies relationships between $P{M}_{2.5}$ and daily cycles of temperature, rainfall, radiation, and wind speed and direction. Datasets were analyzed considering periods before and after the COVID-19 lockdown (pre-pandemic and pandemic, respectively), and specific pollution events were also analyzed. Furthermore, this work considers the relationships between $P{M}_{2.5}$ and meteorological variables, contrasting the pre-pandemic and pandemic periods. This study characterizes diurnal cycles of meteorological variables and their relationship with $P{M}_{2.5}$. There are consistent patterns among temperature, atmospheric boundary layer (ABL) height, and solar radiation, whereas precipitation and relative humidity show the opposite behavior. $P{M}_{2.5}$ exhibits similar relative frequency functions during both daytime and nighttime, regardless of rainfall. An inverse relationship is noted between $P{M}_{2.5}$ levels and ABL height at different times of the day. Moreover, the PCA results show that the first principal component explains around 60% of the total variance in the hydrometeorological data. The second PC explains 10%, and the rest of the variance is distributed among the other three to eight PCs. In this sense, there is no significant difference between the two PCAs with hydrometeorological data from a pre-pandemic period and a COVID-19 pandemic period. Multiple regression analysis indicates a significant and consistent dependence of $P{M}_{2.5}$ on temperature and solar radiation across both analyzed periods. The application of Generalized Additive Models (GAMs) to our dataset yielded promising results, reflecting the complex relationship between meteorological variables and $P{M}_{2.5}$ concentrations. The metrics obtained from the GAM were as follows: Mean Squared Error (MSE) of 98.04, Root Mean Squared Error (RMSE) of 9.90, R-squared (R²) of 0.24, Akaike Information Criterion (AIC) of 110,051.34, and Bayesian Information Criterion (BIC) of 110,140.63. In comparison, the linear regression model exhibited slightly higher MSE (100.49), RMSE (10.02), and lower R-squared (0.22), with AIC and BIC values of 110,407.45 and 110,460.67, respectively. Although the improvement in performance metrics from GAM over the linear model is not conclusive, they indicate a better fit for the complexity of atmospheric dynamics influencing $P{M}_{2.5}$ levels. These findings underscore the intricate interplay of meteorological factors and particulate matter concentration, reinforcing the necessity for advanced modeling techniques in environmental studies. This work presents new insights that enhance the diagnosis, understanding, and modeling of environmental pollution, thereby supporting informed decision-making and strengthening management efforts. Full article

In survival analysis, interval-censored data and panel count data represent two prevalent types of incomplete data. Given that, within certain research contexts, the events of interest may simultaneously involve both data types, it is imperative to perform a joint analysis of these data to fully comprehend the occurrence process of the events being studied. In this paper, a novel semiparametric joint regression analysis framework is proposed for the analysis of interval-censored data and panel count data. It is hypothesized that the failure time follows an additive–multiplicative hazards model, while the recurrent events follow a nonhomogeneous Poisson process. Additionally, a gamma-distributed frailty is introduced to describe the correlation between the failure time and the count process of recurrent events. To estimate the model parameters, a sieve maximum likelihood estimation method based on Bernstein polynomials is proposed. The performance of this estimation method under finite sample conditions is evaluated through a series of simulation studies, and an empirical study is illustrated. Full article

Creativity and the production of artwork can have an impact on the course and treatment of comorbid severe mental illness and neurodegeneration. We report on a 70-year-old male patient with highly original artistic behavior, who suffered from lifelong recurrent major depression and subsequently developed symptoms of progressive bulbar palsy (PBP). In the context of a systematic literature review, we detail the patient’s personal and artistic biographies and portray artwork from his artistic portfolio together with his disease history, clinical examination, psychopathological and neuropsychological evaluations, blood and cerebrospinal fluid analyses, neuroimaging, neurophysiological testing, and psychotherapeutic treatment. The patient’s 1–2-year history of primarily bulbar motor symptoms and signs aligned with electromyography, showing widespread signs of continuing denervation/chronic neurogenic changes. Slight impairments in semantic fluency, executive control, and visuoconstructive abilities were observed in neuropsychological testing, in conjunction with right-sided medial temporal lobe atrophy in an MRI. He was prescribed medication, including extended-release venlafaxine, trazodone, pramipexole, and zolpidem, and took his medication regularly, usually at high doses. For most of his life, the patient had attributed professional “success” and artistic output to, at times, excessive alcohol consumption. Later, however, his interest in creative work continued despite alcohol reduction and cessation. Psychotherapy grounded him in reality via goal-centered behaviors, making him realize that his physical and mental ailments rather hindered his creative output. In summary, creative behavior can be utilized in the treatment of patients with psychiatric conditions (affective or addictive disorders) and/or neurodegenerative diseases. In the reported case, specific psychopharmacology and psychotherapy that address goal-directed self-efficacy experiences of reality were critical to the patient’s treatment. Full article

Background/Objectives: Faecal microbiota transplantation (FMT) is considered a safe and effective therapy for recurrent Clostridioides difficile infection. It is the only current clinical indication for this technique, although numerous clinical research studies and trials propose its potential usefulness for treating other pathologies. Donor selection is a very rigorous process, based on a personal lifestyle interview and the absence of known pathogens in faeces and serum, leading to only a few volunteers finally achieving the corresponding certification. However, despite the high amount of data generated from the ongoing research studies relating microbiota and health, there is not yet a consensus defining what is a “healthy” microbiota. To date, knowledge of the composition of the microbiota is not a requirement to be a faecal donor. The aim of this work was to evaluate whether the analysis of the composition of the microbiota by massive sequencing of 16S rDNA could be useful in the selection of the faecal donors. Methods: Samples from 10 certified donors from Mikrobiomik Healthcare Company were collected and sequenced using 16S rDNA in a MiSeq (Illumina) platform. Alpha (Chao1 and Shannon indices) and beta diversity (Bray–Curtis) were performed using the bioinformatic web server Microbiome Analyst. The differences in microbial composition at the genera and phyla levels among the donors were evaluated. Results: The microbial diversity metric by alpha diversity indexes showed that most donors exhibited a similar microbial diversity and richness, whereas beta diversity by 16S rDNA sequencing revealed significant inter-donor differences, with a more stable microbial composition over time in some donors. The phyla Bacillota and Bacteroidota were predominant in all donors, while the density of other phyla, such as Actinomycota and Pseudomonota, varied among individuals. Each donor exhibited a characteristic genera distribution pattern; however, it was possible to define a microbiome core consisting of the genera Agathobacter, Eubacterium, Bacteroides, Clostridia UCG-014 and Akkermansia. Conclusions: The results suggest that donor certification does not need to rely exclusively on their microbiota composition, as it is unique to each donor. While one donor showed greater microbial diversity and richness, clear criteria for microbial normality and health have yet to be established. Therefore, donor certification should focus more on clinical and lifestyle aspects. Full article

Endometrial cancer is one of the most common gynecological malignancies, and while early-stage cases are highly treatable, recurrent or advanced EC remains challenging to manage. Immunotherapy, particularly immune checkpoint inhibitors, has revolutionized treatment approaches in oncology, and its application in EC has shown promising results. Key to immunotherapy efficacy in EC is the tumor’s mismatch repair status, with MMR-deficient tumors demonstrating a higher tumor mutational burden and increased PD-L1 expression, making them more susceptible to immune checkpoint inhibitors (ICIs) such as pembrolizumab, durvalumab, and dostarlimab. However, not all mismatch repair-deficient (MMRd) tumors respond to ICIs, particularly those with a “cold” tumor microenvironment (TME) characterized by poor immune infiltration. In contrast, some MMR-proficient tumors with a “hot” TME respond well to ICIs, underscoring the complex interplay between MMR status, tumor mutational burden (TMB), and TME. To overcome resistance in cold tumors, novel therapies, including Chimeric Antigen Receptor (CAR) T cells and tumor-infiltrating lymphocytes are being explored, offering targeted immune-based strategies to enhance treatment efficacy. This review discusses the current understanding of immunotherapy in EC, emphasizing the prognostic and therapeutic implications of MMR status, TME composition, and emerging cell-based therapies. Full article

Predicting streamflow is essential for managing water resources, especially in basins and watersheds where snowmelt plays a major role in river discharge. This study evaluates the advanced deep learning models for accurate monthly and peak streamflow forecasting in the Gilgit River Basin. The models utilized were LSTM, BiLSTM, GRU, CNN, and their hybrid combinations (CNN-LSTM, CNN-BiLSTM, CNN-GRU, and CNN-BiGRU). Our research measured the model’s accuracy through root mean square error (RMSE), mean absolute error (MAE), Nash–Sutcliffe efficiency (NSE), and the coefficient of determination (R²). The findings indicated that the hybrid models, especially CNN-BiGRU and CNN-BiLSTM, achieved much better performance than traditional models like LSTM and GRU. For instance, CNN-BiGRU achieved the lowest RMSE (71.6 in training and 95.7 in testing) and the highest R² (0.962 in training and 0.929 in testing). A novel aspect of this research was the integration of MODIS-derived snow-covered area (SCA) data, which enhanced model accuracy substantially. When SCA data were included, the CNN-BiLSTM model’s RMSE improved from 83.6 to 71.6 during training and from 108.6 to 95.7 during testing. In peak streamflow prediction, CNN-BiGRU outperformed other models with the lowest absolute error (108.4), followed by CNN-BiLSTM (144.1). This study’s results reinforce the notion that combining CNN’s spatial feature extraction capabilities with the temporal dependencies captured by LSTM or GRU significantly enhances model accuracy. The demonstrated improvements in prediction accuracy, especially for extreme events, highlight the potential for these models to support more informed decision-making in flood risk management and water allocation. Full article

Li-ion batteries are important in modern technology, especially for drones, due to their high energy density, long cycle life, and lightweight properties. Predicting their performance is crucial for enhancing drone flight safety, optimizing operations, and reducing costs. This involves using advanced techniques like machine learning (e.g., Convolutional Neural Network-CNNs, Recurrent Neural Network-RNNs), statistical modeling (e.g., Kalman Filtering), and explainable AI (e.g., SHAP, LIME, PDP) to forecast battery behavior, extend battery life, and improve drone efficiency. The study aims to develop a CNN-RNN-based ensemble model, enhanced with explainable AI, to predict key battery metrics during drone flights. The model’s predictions will aid in enhancing battery performance via continuous, data-driven monitoring, improve drone safety, optimize operations, and reduce greenhouse gas emissions through advanced recycling methods. In the present study, comparisons are made for the behaviors of two different drone Li-ion batteries, numbered 92 and 129. The ensemble model in Drone 92 showed the best performance with MAE (0.00032), RMSE (0.00067), and R² (0.98665) scores. Similarly, the ensemble model in Drone 129 showed the best performance with MAE (0.00030), RMSE (0.00044), and R² (0.98094) performance metrics. Similar performance results are obtained in the two predictions. However, drone 129 has a minimally lower error rate. When the Partial Dependence Plots results, which are one of the explainable AI (XAI) techniques, are interpreted with the decision tree algorithm, the effect of the Current (A) value on the model estimations in both drone flights is quite evident. When the current value is around −4, the model is more sensitive and shows more changes. This study will establish benchmarks for future research and foster advancements in drone and battery technologies through extensive testing. Full article

Background and Objective: High-flow pancreaticoduodenal artery (PDA) aneurysms secondary to celiac trunk occlusion or stenosis have a high risk of rupture. Embolization offers a less invasive alternative to surgery. We evaluated the effectiveness and safety of retrograde embolization via the superior mesenteric artery of high-flow PDA aneurysms without celiac trunk revascularization. Methods: This retrospective bicentric study included patients who underwent embolization of high-flow PDA aneurysms due to significant celiac trunk stenosis or occlusion. All patients underwent pre-interventional dynamic contrast-enhanced computed tomography. Retrograde embolization was performed using microcoils and/or liquid agents without celiac trunk revascularization. Follow up involved clinical and radiological assessment at one month. Technical and clinical success were evaluated, and complications were categorized as minor or major. Results: Twenty-three patients (mean age 65 ± 14 years; 52% male) were included. Emergency embolization was required in 12 patients (52%). The technical success rate was 100%. Patients were monitored for a median of 16 months. Clinical success was 87%. No hemorrhagic recurrences were observed. Minor complications occurred in two cases. One major complication involved splenic infarction due to glue migration, requiring splenectomy and intensive unit care admission. Conclusions: Retrograde embolization of high-flow PDA aneurysms is effective and safe without needing celiac trunk revascularization. Full article

Background and Clinical Significance: Voriconazole is a commonly prescribed second-generation azole used for the prevention and treatment of fungal infections. This report seeks to elucidate the relationship between certain intravenous infusion parameters and the causality and severity of potential visual adverse events associated with voriconazole administration, despite existing reports of visual disturbances such as hallucinations and altered visual perception, the underlying causes of which remain inadequately understood. Case Presentation: This case report describes a 32-year-old female patient who experienced sudden hallucinations and visual impairments after receiving an initial dose of intravenous voriconazole for the treatment of recurrent severe vulvovaginitis caused by Candida glabrata. The symptoms quickly disappeared when the dosage and infusion rate were reduced as per the recommendations of the clinical pharmacist. Conclusions: This example emphasizes the possible negative drug responses linked to voriconazole, especially those provoked by its irrational use described as an inappropriate infusion rate, and the crucial role of clinical pharmacists in recognizing and handling these reactions. Full article

Rectal prolapse is a common condition in laboratory-housed macaques, usually associated with recurrent gastroenteritis and stress. However, evidence for both statements is lacking. Therefore, the prevalence of rectal prolapses and their risk factors in laboratory-housed macaques have yet to be assessed. In addition, a standard of care pharmacological treatment remains to be recognized and documented. Thus, a retrospective study involving 816 laboratory-housed macaques was conducted, in which the prevalence, risk factors, and treatment success of rectal prolapses was assessed. The prevalence was shown to be 3.92%, and all cases could be either directly or indirectly linked to stress factors. By eliminating the suggested stressor, most rectal prolapses reverted naturally (69%). Moreover, we discuss the advantage of the ancillary administration of hyoscine butylbromide and metamizole as a treatment modality. Full article

Background and Objectives: Recurrent pregnancy loss (RPL) affects numerous couples worldwide and has traditionally been attributed mainly to maternal factors. However, recent evidence highlights significant paternal influences on pregnancy viability and outcomes. This review aims to comprehensively examine male contributions to pregnancy loss, focusing on underlying mechanisms, novel biomarkers, and integrated strategies for improved reproductive success. Materials and Methods: A comprehensive narrative review was conducted by searching databases including PubMed and Embase for the literature published from January 2004 to October 2024. Studies focusing on paternal influences in RPL—encompassing oxidative stress, genetic and epigenetic mechanisms, health conditions, lifestyle factors, environmental exposures, and advancements in sperm proteomics—were included. Inclusion criteria were peer-reviewed articles in English that directly addressed paternal factors in RPL; studies not meeting these criteria were excluded. Results: The review identified that paternal factors such as advanced age, metabolic and cardiovascular health issues, chronic diseases, lifestyle habits (e.g., smoking, alcohol consumption, poor diet), and environmental exposures significantly affect sperm integrity through mechanisms like oxidative stress, DNA fragmentation, and epigenetic alterations. Advanced paternal age and poor health conditions are associated with increased risks of miscarriage and adverse pregnancy outcomes. Novel sperm proteomic biomarkers have been identified, offering potential for enhanced diagnostics and personalized interventions. Integrated approaches involving multidisciplinary assessments, preventive strategies, and genetic counseling are essential for effectively addressing RPL. Conclusions: Integrating paternal factors into clinical evaluations is crucial for effectively addressing recurrent pregnancy loss. Recognizing and modifying paternal risk factors through lifestyle changes, medical interventions, and environmental management can improve pregnancy outcomes. The findings underscore the need for incorporating paternal assessments into standard care and highlight the importance of future research focusing on standardizing diagnostic protocols, expanding studies on paternal contributions, and integrating proteomic biomarkers into clinical practice to facilitate personalized treatment strategies. Full article

Malignant tumors can evade immune surveillance and elimination through multiple mechanisms, with the induction of immune cell dysfunction serving as a crucial strategy. Mounting evidence indicates that T cell senescence constitutes the primary mechanism underlying T cell dysfunction in acute myeloid leukemia (AML) and represents one of the potential causes of immunotherapy failure. AML usually progresses rapidly and is highly susceptible to drug resistance, thereby resulting in recurrence and patient mortality. Hence, disrupting the immune interface within the bone marrow microenvironment of AML has emerged as a critical objective for synergistically enhancing tumor immunotherapy. In this review, we summarize the general characteristics, distinctive phenotypes, and regulatory signaling networks of senescent T cells and highlight their potential clinical significance in the bone marrow microenvironment of AML. Additionally, we discuss potential therapeutic strategies for alleviating and reversing T cell senescence. Full article

This paper addresses the challenge of degradation prediction in proton-exchange membrane fuel cells (PEMFCs). Traditional methods often struggle to balance accuracy and complexity, particularly under dynamic operational conditions. To overcome these limitations, this study proposes a data-driven approach based on the gated recurrent unit (GRU) neural network, optimized by the grey wolf optimizer (GWO). The integration of the GWO automates the hyperparameter tuning process, enhancing the predictive performance of the GRU network. The proposed GWO-GRU method was validated utilizing actual PEMFC data under dynamic load conditions. The results demonstrate that the GWO-GRU method achieves superior accuracy compared to other standard methods. The method offers a practical solution for online PEMFC degradation prediction, providing stable and accurate forecasting for PEMFC systems in dynamic environments. Full article

Bladder cancer (BC) represents a wide spectrum of diseases, ranging from recurrent non-invasive tumors to advanced stages that require intensive treatments. BC accounts for an estimated 500,000 new cases and 200,000 deaths worldwide every year. Understanding the biology of BC has changed how this disease is diagnosed and treated. Bladder cancer is highly immunogenic, involving innate and adaptive components of the immune system. Although little is still known of how immune cells respond to BC, immunotherapy with bacillus Calmette–Guérin (BCG) remains the gold standard in high-risk non-muscle invasive BC. For muscle-invasive BC and metastatic stages, immune checkpoint inhibitors targeting CTLA-4, PD-1, and PD-L1 have emerged as potent therapies, enhancing immune surveillance and tumor cell elimination. This review aims to unravel the immune responses involving innate and adaptive immune cells in BC that will contribute to establishing new and promising therapeutic options, while reviewing the immunotherapies currently in use in bladder cancer. Full article

Background: Dermatofibrosarcoma protuberans (DFSP) is a rare proliferative condition representing skin sarcomas known to locally recur yet very rarely known to metastasize. A typical characteristic is trunk localization, making vulvar occurrences an exception. Complete resection is the primary treatment. Case Report: We present an unusual case of twice-recurrent vulvar DFSP in a 92-year-old woman. Wide local excision with tumor-negative margins (R0 resection) of a grapefruit-sized mass was followed by a rhomboid skin flap transplantation to address a substantial skin deficit at the post excision site. At 14-month follow-up, no signs of recurrence were present, all wounds were healed, and the esthetic results were assessed as highly adequate. Conclusions: This study is an argument for the role of reconstructive surgery in patients of advanced age undergoing oncological procedures; skin transplantations should be considered regardless of the patient’s age. We also provide an updated review on vulvar DFSP. Full article