Search Results (1,411)

Background/Objective: Understanding how chronic conditions such as knee osteoarthritis (OA) and chronic obstructive pulmonary disease (COPD) influence long-term physical activity (PA) is essential for developing condition-specific rehabilitation strategies. This study aimed to examine whether baseline diagnoses of knee OA and COPD are independently associated with the trajectories of PA decline over five years in U.S. adults, informed by the disablement process model. Methods: We analyzed data from 855 adults aged ≥45 years enrolled in the Osteoarthritis Initiative (OAI). The participants were categorized into three baseline groups, control (n = 122), knee OA (n = 646), and COPD (n = 87), based on self-reports and prior clinical assessments. PA was measured annually for five years using the Physical Activity Scale for the Elderly (PASE). General linear mixed models assessed changes in PA over time, adjusting for demographic, behavioral, and clinical covariates. Results: Compared to the controls, participants with knee OA had a significant decline in PA over time (β = −6.62; 95% CI: −15.4 to −2.19; p = 0.014). Those with COPD experienced an even greater decline compared to the knee OA group (β = −11.2; 95% CI: −21.7 to −0.67; p = 0.037). These associations persisted after adjusting for age, sex, body mass index, comorbidities, and smoking. Conclusions: Baseline knee OA and COPD were independently associated with long-term reductions in PA. These findings underscore the importance of early, tailored rehabilitation strategies, particularly pulmonary rehabilitation, in preserving functional independence among older adults with chronic conditions. Full article

Unmanned Aerial Vehicles (UAVs) have rapidly grown into different essential applications, including surveillance, disaster response, agriculture, and urban monitoring. However, for UAVS to steer safely and autonomously, the ability to detect obstacles and nearby aircraft remains crucial, especially under hard environmental conditions. This study comprehensively analyzes the recent landscape of obstacle and aircraft detection techniques tailored for UAVs acting in difficult scenarios such as fog, rain, smoke, low light, motion blur, and disorderly environments. It starts with a detailed discussion of key detection challenges and continues with an evaluation of different sensor types, from RGB and infrared cameras to LiDAR, radar, sonar, and event-based vision sensors. Both classical computer vision methods and deep learning-based detection techniques are examined in particular, highlighting their performance strengths and limitations under degraded sensing conditions. The paper additionally offers an overview of suitable UAV-specific datasets and the evaluation metrics generally used to evaluate detection systems. Finally, the paper examines open problems and coming research directions, emphasising the demand for lightweight, adaptive, and weather-resilient detection systems appropriate for real-time onboard processing. This study aims to guide students and engineers towards developing stronger and intelligent detection systems for next-generation UAV operations. Full article

Background: This study presents an innovative approach to cardiovascular disease (CVD) risk prediction based on a comprehensive analysis of clinical, immunological and biochemical markers using mathematical modelling and machine learning methods. Baseline data include indices of humoral and cellular immunity (CD59, CD16, IL-10, CD14, CD19, CD8, CD4, etc.), cytokines and markers of cardiovascular disease, inflammatory markers (TNF, GM-CSF, CRP), growth and angiogenesis factors (VEGF, PGF), proteins involved in apoptosis and cytotoxicity (perforin, CD95), as well as indices of liver function, kidney function, oxidative stress and heart failure (albumin, cystatin C, N-terminal pro B-type natriuretic peptide (NT-proBNP), superoxide dismutase (SOD), C-reactive protein (CRP), cholinesterase (ChE), cholesterol, and glomerular filtration rate (GFR)). Clinical and behavioural risk factors were also considered: arterial hypertension (AH), previous myocardial infarction (PICS), aortocoronary bypass surgery (CABG) and/or stenting, coronary heart disease (CHD), atrial fibrillation (AF), atrioventricular block (AB block), and diabetes mellitus (DM), as well as lifestyle (smoking, alcohol consumption, physical activity level), education, and body mass index (BMI). Methods: The study included 52 patients aged 65 years and older. Based on the clinical, biochemical and immunological data obtained, a model for predicting the risk of premature cardiovascular aging was developed using mathematical modelling and machine learning methods. The aim of the study was to develop a predictive model allowing for the early detection of predisposition to the development of CVDs and their complications. Numerical methods of mathematical modelling, including Runge–Kutta, Adams–Bashforth and backward-directed Euler methods, were used to solve the prediction problem, which made it possible to describe the dynamics of changes in biomarkers and patients’ condition over time with high accuracy. Results: HLA-DR (50%), CD14 (41%) and CD16 (38%) showed the highest association with aging processes. BMI was correlated with placental growth factor (37%). The glomerular filtration rate was positively associated with physical activity (47%), whereas SOD activity was negatively correlated with it (48%), reflecting a decline in antioxidant defence. Conclusions: The obtained results allow for improving the accuracy of cardiovascular risk prediction, and form personalised recommendations for the prevention and correction of its development. Full article

To enhance the fire safety protection level of deeply buried metro stations, this study conducted full-scale fire experiments based on Wulichong Station of Guiyang Metro Line 3. It systematically investigated the laws of smoke movement and temperature distribution under the coupled effects of different fire source powers and smoke extraction system states. Through the set up of multiple sets of comparative test conditions, the study focused on analyzing the influence mechanism of the operation (on/off) of the smoke extraction system on smoke spread characteristics and temperature field distribution. The results indicate that under the condition where the smoke extraction system is turned off, the smoke exhibits typical stratified spread characteristics driven by thermal buoyancy, with the temperature rising significantly as the vertical height increases. When the smoke extraction system is activated, the horizontal airflow generated by mechanical smoke extraction significantly alters the flame morphology (with an inclination angle exceeding 45°), effectively extracting and discharging the hot smoke and leading to a more uniform temperature distribution within the space. Full article

Background and Clinical Significance: Antisynthetase syndrome (ASyS) is a rare autoimmune entity defined by the presence of anti-aminoacyl-t ribonucleic acid (RNA) synthetase autoantibodies and classically associated with a triad of interstitial lung disease (ILD), inflammatory myopathy, and arthritis. Additional clinical features may include Raynaud’s phenomenon and “mechanic’s hands”. Among antisynthetase antibodies, anti-PL-12 is notably associated with predominant or isolated ILD and may occur in the absence of clinically evident myositis, thereby complicating timely diagnosis. Case Presentation: We are presenting a 45-year-old non-smoking female patient with a prior diagnosis of seronegative rheumatoid arthritis (RA) who developed progressive dyspnea, dry cough, and sicca symptoms. High-resolution computed tomography revealed a nonspecific interstitial pneumonia (NSIP) pattern. Despite normal creatine kinase and lactate dehydrogenase levels, serological work-up revealed positive anti-PL-12 and anti-Ro52 antibodies, supporting a diagnosis of antisynthetase syndrome without myositis, fulfilling the diagnostic criteria for ASyS per Connors and Solomon. Treatment with corticosteroids and cyclophosphamide induced clinical and functional respiratory improvement, while azathioprine was initiated for maintenance. Conclusions: This case underscores the clinical heterogeneity of antisynthetase syndrome and highlights the diagnostic challenge posed by anti-PL-12–associated ILD in the absence of myositis. Importantly, it demonstrates that in patients with pre-existing rheumatologic diagnoses, the emergence of atypical pulmonary manifestations warrants repeat serologic evaluation to assess ASyS and other autoimmune conditions. Early diagnosis and immunosuppressive treatment are essential to optimize outcomes. Full article

Background/Objectives: Misperception of body weight might be associated with the likelihood of being in a pre-contemplation phase and with a limited intention to initiate a healthy behavioral change toward weight management. The current study investigates factors associated with the misperception of body weight, body weight satisfaction, and intentions for body weight modifications. Methods: This study utilized a cross-sectional design to reach adults from Jazan, in the southwest of Saudi Arabia. The data collection tool measured demographics, the participants’ latest height and weight, body weight perception, satisfaction, and intentions concerning body weight modification. Chi-squared tests were used to compare the demographic characteristics between those who had correct perception and those who had a wrong one. Results: A sample of 685 adult participants was included in the current analysis. The mean age of the participants was 31.3 years (standard deviation: 11.1). The proportion of female participants was 52%. Fifty-four percent of the participants had a wrong perception of their body weight. Gender, employment, smoking, khat chewing, having a diagnosed condition, and BMI levels were statistically associated with the perception of body weight (p-values < 0.05). Conclusions: The detected misestimating, especially underestimation, might be associated with the likelihood of participants being in a precontemplation phase and with a limited intention to initiate a healthy behavioral change toward weight management. The practical implications of these findings indicate the importance of incorporating the assessment of weight perception alongside actual BMI measurement in clinical settings. Full article

Food addiction (FA) is an emerging psychiatric condition that presents behavioral and neurobiological similarities with other addictions, and its early identification is essential to prevent the development of more severe disorders. The aim of the present study was to determine the ability of anthropometric measures, eating habits, symptoms related to eating disorders (ED), and lifestyle features to predict the symptoms of food addiction. Methodology: A cross-sectional study was conducted in a sample of 702 university students (77.3% women; age: 22 ± 6 years). The Food Frequency Questionnaire (FFQ), the Yale Food Addiction Scale 2.0 (YFAS 2.0), the Eating Attitudes Test (EAT-26), anthropometric measurements, and a set of self-report questions on substance use, physical activity level, and other questions were administered. A total of 6.4% of participants presented symptoms compatible with food addiction, and 8.1% were at risk for ED. Additionally, 26.5% reported daily smoking, 70.6% consumed alcohol, 2.9% used illicit drugs, and 29.4% took medication; 35.3% did not engage in physical activity. Individuals with food addiction had higher BMI (p = 0.010), waist circumference (p = 0.001), and body fat (p < 0.001) values, and a higher risk of eating disorders (p = 0.010) compared to those without this condition. In the multivariate logistic model, non-dairy beverage consumption (such as coffee or alcohol), vitamin D deficiency, and waist circumference predicted food addiction symptoms (R²_Nagelkerke = 0.349). Indeed, the machine learning approaches confirmed the influence of these variables. Conclusions: The prediction models allowed an accurate prediction of FA in the university students; moreover, the individualized approach improved the identification of people with FA, involving complex dimensions of eating behavior, body composition, and potential nutritional deficits not previously studied. Full article

This study describes a quite special and interesting atmospheric event characterized by the simultaneous presence of fresh and aged smoke layers. These peculiar conditions occurred on 16 July 2024 at the CNR-IMAA atmospheric observatory (CIAO) in Potenza (Italy), and represent an ideal case for the evaluation of the impact of aging and transport mechanisms on both the optical and microphysical properties of biomass burning aerosol. The fresh smoke was originated by a local wildfire about 2 km from the measurement site and observed about one hour after its ignition. The other smoke layer was due to a wide wildfire occurring in Canada that, according to backward trajectory analysis, traveled for about 5–6 days before reaching the observatory. Synergetic use of lidar, ceilometer, radar, and microwave radiometer measurements revealed that particles from the local wildfire, located at about 3 km a.s.l., acted as condensation nuclei for cloud formation as a result of high humidity concentrations at this altitude range. Optical characterization of the fresh smoke layer based on Raman lidar measurements provided lidar ratio (LR) values of 46 ± 4 sr and 34 ± 3 sr, at 355 and 532 nm, respectively. The particle linear depolarization ratio (PLDR) at 532 nm was 0.067 ± 0.002, while backscatter-related Ångström exponent (AEβ) values were 1.21 ± 0.03, 1.23 ± 0.03, and 1.22 ± 0.04 in the spectral ranges of 355–532 nm, 355–1064 nm and 532–1064 nm, respectively. Microphysical inversion caused by these intensive optical parameters indicates a low contribution of black carbon (BC) and, despite their small size, particles remained outside the ultrafine range. Moreover, a combined use of CIAO remote sensing and in situ instrumentation shows that the particle properties are affected by humidity variations, thus suggesting a marked particle hygroscopic behavior. In contrast, the smoke plume from the Canadian wildfire traveled at altitudes between 6 and 8 km a.s.l., remaining unaffected by local humidity. Absorption in this case was higher, and, as observed in other aged wildfires, the LR at 532 nm was larger than that at 355 nm. Specifically, the LR at 355 nm was 55 ± 2 sr, while at 532 nm it was 82 ± 3 sr. The AEβ values were 1.77 ± 0.13 and 1.41 ± 0.07 at 355–532 nm and 532–1064 nm, respectively and the PLDR at 532 nm was 0.040 ± 0.003. Microphysical analysis suggests the presence of larger, yet much more absorbent particles. This analysis indicates that both optical and microphysical properties of smoke can vary significantly depending on its origin, persistence, and transport in the atmosphere. These factors that must be carefully incorporated into future climate models, especially considering the frequent occurrences of fire events worldwide. Full article

Despite recent advances in deep learning for fire detection, much of the current research prioritizes model-centric metrics over dataset fidelity, particularly from a fire safety engineering perspective. Commonly used datasets are often dominated by fully developed flames, mislabel smoke-only frames as non-fire, or lack intra-video diversity due to redundant frames from limited sources. Some works treat smoke detection alone as early-stage detection, even though many fires (e.g., electrical or chemical) begin with visible flames and no smoke. Additionally, attempts to improve model applicability through mixed-context datasets—combining indoor, outdoor, and wildland scenes—often overlook the unique false alarm sources and detection challenges specific to each environment. To address these limitations, we curated a new video dataset comprising 1108 annotated fire and non-fire clips captured via indoor surveillance cameras. Unlike existing datasets, ours emphasizes early-stage fire dynamics (pre-flashover) and includes varied fire sources (e.g., sofa, cupboard, and attic fires), realistic false alarm triggers (e.g., flame-colored objects, artificial lighting), and a wide range of spatial layouts and illumination conditions. This collection enables robust training and benchmarking for early indoor fire detection. Using this dataset, we developed a spatiotemporal fire detection model based on the mixed convolutions ResNets (MC3_18) architecture, augmented with Convolutional Block Attention Modules (CBAM). The proposed model achieved 86.11% accuracy, 88.76% precision, and 84.04% recall, along with low false positive (11.63%) and false negative (15.96%) rates. Compared to its CBAM-free baseline, the model exhibits notable improvements in F1-score and interpretability, as confirmed by Grad-CAM++ visualizations highlighting attention to semantically meaningful fire features. These results demonstrate that effective early fire detection is inseparable from high-quality, context-specific datasets. Our work introduces a scalable, safety-driven approach that advances the development of reliable, interpretable, and deployment-ready fire detection systems for residential environments. Full article

Background: Prematurely born newborns with low birth weight constitute a group of patients who require special care from the first days of life. Prematurity and low birth weight affect about 13.4 million infants. Risk factors include placental disorders but also factors related to the mother, such as smoking, alcohol drinking, drug use, malnutrition, or certain diseases. It is imperative to educate women of reproductive age (15–49) about the basic factors influencing embryonic development, such as oral health, diet, medicine intake, and harmful habits. Even though most women are aware of the negative impact of harmful habits on the fetus, still too little attention is paid to oral health in pregnant women. Poor oral health may influence the well-being of the future mother, as well as of the child. Therefore, women of reproductive age and those who are pregnant must have adequate knowledge on this subject. The aim of this study was to assess the knowledge of Polish women of reproductive age (15–49) regarding oral health during pregnancy, including the impact of dental treatment, oral hygiene, and maternal oral conditions on pregnancy outcomes and the health of the newborn. Materials and Methods: This was a cross-sectional study of 508 women, in the reproductive age, whose age ranged from 18 to 49 years old. The surveys were conducted from April 2020 to November 2020. The questionnaire was originally developed based on the available literature and consisted of seven sections: basic information, general health and habits, pregnancy status and dental care, knowledge of treatment options during pregnancy, oral health status and its association with the risk of preterm birth, prematurity and the child’s oral health, and breastfeeding and oral development. Results: After excluding incomplete questionnaires, a total of 499 questionnaires were included in the analysis. Women participating in the study had a fairly good understanding of the impact of oral health on the fetus and the role of breastfeeding in the development of the stomatognathic system (from 50% to 70% correct answers). However, even though most respondents had completed higher education (344/68.94%), their knowledge of oral health, preterm birth, and low birth weight was very limited (including the impact of inflammation on the intrauterine development of the child or bacteria and transfer across the placenta). In these sections, the percentage of correct answers ranged from less than 20% to 50%. When analyzing knowledge by age, education, number of births, and place of residence, the highest levels of knowledge were observed among respondents with higher education, particularly those aged 27–32. Conclusions: Respondents had a fairly good understanding of the general impact of oral health during pregnancy and recognition of the importance of breastfeeding for infants. However, their knowledge about the impact of bacteria and inflammation in the mother’s oral cavity on prematurity and low birth weight was limited. Therefore, educating women of reproductive age and pregnant women on this topic is essential, as it may help reduce the adverse consequences of prematurity. Full article

Background/Objectives: Weight loss and its subsequent regain pose significant challenges for those dealing with overweight and obesity. This study explores weight loss strategies among adults in Saudi Arabia and evaluates factors linked to weight regain. Methods: This cross-sectional study focused on adults residing in Jazan, located in southwest Saudi Arabia. Data collection was conducted using a self-administered questionnaire that assessed participants’ demographics, medical history, perceptions of body weight, weight loss methods, and the incidence of weight regain. Logistic regression was used to determine whether there were statistically significant differences related to the occurrence of weight regain. Results: A total of 368 participants reported efforts to lose weight over the past 3 years. The average age of these participants was 32.7 years (standard deviation: 11.3), and the gender distribution was almost equal. The majority of the sample (65%) voiced dissatisfaction with their body weight. Some participants employed a combination of weight loss methods, with exercise, reduced food intake, and intermittent fasting being the most frequently mentioned. The findings also indicate that a minority sought professional help, whether from a physician or a nutritionist. Over 90% claimed to have successfully lost weight at least once during their attempts, but more than half (139 individuals) experienced weight regain following their weight loss efforts. Within the univariate logistic regression, higher odds ratios of weight regain were detected among men, older participants, those living in rural areas, individuals with higher levels of education, employed persons or business owners, those with higher monthly incomes, smokers, khat chewers, and those diagnosed with a chronic condition (p values < 0.05). However, the multivariate logistic regression revealed that only residence, monthly income, smoking status, and being diagnosed with a chronic disease remained statistically significant as predictors of weight regain after adjusting for other variables (p values < 0.05). Conclusions: These findings highlight the significance of incorporating weight regain prevention into body weight management for individuals dealing with overweight and obesity. Further research is needed to evaluate specific dietary, physical activity, and psychological factors that may increase the risk of weight regain in certain participants. Full article

Lung cancer is one of the most common and deadly forms of cancer worldwide, despite sustained efforts to encourage smoking cessation and raise awareness of the risk factors. In Romania, lung cancer is a significant health challenge, being the leading cause of death caused by cancer, especially amongst men. The incidence of lung cancer in connective tissue disease (CTD) varies in different studies from 4.5% in rheumatoid arthritis (RA), to 4.4% in polymyositis or dermatomyositis, and up to 11.1% in systemic sclerosis. However, older studies have shown an increased risk of cancer in patients with rheumatoid arthritis (RA), ranging from 10% to 30% compared to the general population, particularly in those undergoing methotrexate therapy. Rheumatoid arthritis affects approximately 40 per 100,000 people annually worldwide, with a three- to four-fold higher incidence in women. Non-small cell lung cancer (NSCLC), the most common lung cancer subtype, has been linked to RA, yet the association remains poorly defined, with limited insight into the underlying molecular mechanisms. We present the case of a 61-year-old male with a 49-pack-year smoking history and a known diagnosis of rheumatoid arthritis, currently managed with methotrexate therapy. He was admitted for evaluation due to a progressive decline in general condition, characterized by worsening dyspnea and chest pain, symptoms that had been longstanding but had markedly exacerbated over the past two weeks. Based on a chest CT performed prior to the patient’s admission to our clinic, subsequent diagnostic investigations established the diagnosis of pulmonary adenocarcinoma. The diagnostic process proved to be particularly challenging due to the presence of multiple comorbidities, which significantly impacted both the diagnostic approach and the overall clinical trajectory. Full article

Background: Cardiovascular disease (CVD) is very widespread in countries with a Western-style diet, representing one of the major causes of morbidity. Genetic factors, obesity, diabetes, dyslipidemia, smoking, and ageing are risk factors for CVD outcomes. From a pathogenic point of view, the condition of low-grade inflammation of the arteries leads to endothelial damage and atherosclerosis development. Nowadays, a broad range of drugs is available to treat CVD, but many of them are associated with side effects. Therefore, alternative therapeutic remedies need to be discovered in combination with conventional drugs. A balanced diet rich in fruits and vegetables, e.g., the Mediterranean diet, has been shown to lower the incidence of CVD. Plant-derived polyphenols are ingested in food, and these compounds can exert beneficial effects on human health, such as antioxidant and anti-inflammatory activities. Objective: In the present review, the cellular and molecular bases of the beneficial effects of polyphenols in the prevention and treatment of CVD will be pointed out. Methods: This review has been conducted on the basis of a literature review spanning mainly the last two decades. Results: We found that an increased dietary intake of polyphenols is associated with a parallel decrease in chronic disease incidence, including CVD. Conclusion: Despite a plethora of preclinical studies, more clinical trials are needed for a more appropriate treatment of CVD with polyphenols. Full article

Compression–ignition engines emit particulate matter (PM) (soot), prompting the widespread use of diesel particulate filters (DPFs) in the automotive sector. An alternative method for PM reduction involves the use of ultrasonic waves to disperse and modify the structure of exhaust particles. This article presents experimental results of the effects of ultrasonic emitter parameters, including the number, arrangement, and power, along with the engine speed, on the exhaust smoke density. Tests were conducted on a laboratory prototype equipped with six ultrasonic emitters spaced 0.17 m apart. The exhaust source was a diesel engine from a construction excavator, based on the MTZ-80 tractor design, delivering 80 HP and a displacement of 4750 cm³. A regression model was developed to describe the relationship between the engine speed, emitter power and spacing, and smoke density. The optimal configuration was found to involve an emitter power of 319.35 W and a spacing of 1.361 m for a given engine speed. Under the most effective conditions—an engine speed of 1500 rpm, six active emitters, and a total power of 600 W—smoke emissions were reduced by 18%. These findings support the feasibility of using ultrasonic methods as complementary or alternative exhaust gas filtration techniques for non-road diesel engines. Full article

Background: Pain is a common, often debilitating ailment that may necessitate considerable health service utilization. However, there is a need to assess the associations of pain intensity and other variables with health service utilization among United States adults who have pain. Methods: This cross-sectional database analysis made use of the Medical Expenditure Panel Survey full-year consolidated data file and included United States adults (≥18 years) who have pain. The dependent variables consisted of four health service utilization variables, which included the number of emergency room visits, inpatient discharges, office visits, and outpatient visits in 2021. The number of visits or discharges were categorized as either ≥1 or 0. The independent variable was pain intensity (extreme, quite a bit, moderate, or little pain). Other variables analyzed included age, race, ethnicity, sex, marriage, education, employment, income, insurance, chronic conditions, limitations, exercise, smoking, physical health, and mental health. Chi-squared tests compared differences between pain intensity groups, and multivariable logistic regression models assessed the associations of pain intensity and other variables with each of the four health service utilization variables. The analysis was weighted for national estimates. The significance (alpha) level was 0.05. Results: This analysis included 6280 adults, representing 89,314,769 United States adults with pain. In the multivariable analyses, there were statistically significant associations for extreme pain (odds ratio = 1.72, 95% confidence interval = 1.27–2.33), quite a bit of pain (odds ratio = 1.75, 95% confidence interval=1.37–2.24), and moderate pain (odds ratio = 1.28, 95% confidence interval = 1.02–1.60) versus little pain with emergency room visits, extreme pain (odds ratio = 2.10, 95% confidence interval = 1.44–3.08) and quite a bit of pain (odds ratio = 1.66, 95% confidence interval = 1.21–2.28) versus little pain with inpatient discharges, and quite a bit of pain (odds ratio = 1.47, 95% confidence interval = 1.03–2.11) versus little pain with office visits. There was no correlation between pain intensity levels and outpatient visits. In addition, several other variables were associated with various health service utilization variables. Conclusions: This database analysis discovered greater pain intensity levels were often correlated with increased health service utilization, including more emergency room, inpatient, and office visits. These findings may inform the development of targeted interventions for people with specific characteristics. Further work is needed to implement initiatives that optimize health service utilization and ultimately improve health outcomes for United States adults who have pain. Full article