Search Results (19)

Background: The mechanisms linking loneliness to frailty in older adults with diabetes remain unclear. Guided by the Loneliness–Health Outcomes Model, this study is the first to simultaneously validate the dual mechanisms (mediation and moderation) of social support in the loneliness–frailty relationship among older Chinese adults with diabetes. Methods: A cross-sectional study enrolled 442 community-dwelling adults aged ≥60 years with type 2 diabetes in Beijing. Standardized scales assessed loneliness (UCLA Loneliness Scale), frailty (Tilburg Frailty Indicator), and social support (SSRS). Analyses included Pearson’s correlations, hierarchical regression, and PROCESS macro to evaluate mediating/moderating effects, after adjusting for demographics and comorbidities. Results: The frailty prevalence was 55.2%. Loneliness was positively correlated with frailty (r = 0.327, p < 0.01), while social support showed inverse associations with both loneliness (r = −0.496) and frailty (r = −0.315) (p < 0.01). Social support partially mediated loneliness’s effect on frailty (indirect effect: 30.86%; 95% CI: 0.028–0.087) and moderated this relationship (interaction β = −0.003, p = 0.011). High-risk clusters (e.g., aged ≥80 years, widowed, and isolated individuals) exhibited combined “high loneliness–low support–high frailty” profiles. Conclusions: Social support reduces the frailty risk through dual mechanisms. These findings advocate for tiered clinical interventions: (1) targeted home-visit systems and resource allocation for high-risk subgroups (e.g., solo-living elders aged ≥80 years); and (2) the integration of social support screening into routine diabetes care to identify individuals below the protective threshold (SSRS < 45.47). These findings advance psychosocially informed strategies for diabetes management in aging populations. Full article

Background/Objectives: Digital transformation is reshaping dentistry by improving clinical efficiency, diagnostic accuracy, and patient care. However, the adoption of digital technologies in dental clinics varies widely, influenced by multiple factors, including technology readiness. This study aimed to assess the relationship between technology readiness and digital technology adoption among German dentists, focusing on the impact of clinic characteristics and professional development. Methods: A cross-sectional survey was conducted among 200 licensed German dentists. Technology readiness was measured using the validated Technology Readiness Index (TRI 2.0), encompassing four dimensions: optimism, innovativeness, discomfort, and insecurity. Data on the current use of digital technologies were collected, including digital radiography, CAD/CAM systems, AI-supported tools, and patient management solutions. Statistical analyses included correlation and quartile-based comparisons to identify patterns and significant associations. Results: Clinics with higher TRI scores demonstrated significantly greater adoption of digital technologies. Larger clinics (MVZs) showed higher levels of digital integration compared to solo practices. Younger dentists and those engaged in continuous professional development exhibited higher technology readiness and usage of advanced digital tools. No significant gender-based differences were identified in technology readiness or digital adoption. While basic technologies like digital radiography and CAD/CAM systems were widely used, AI-based diagnostics and 3D printing remained underutilized. Key barriers included financial constraints and limited training opportunities. Conclusions: Technology readiness plays a critical role in shaping digital adoption in dental clinics. The findings highlight the need for targeted support, especially for smaller clinics, through professional training and investment in digital infrastructure. This study contributes to a better understanding of digital transformation in dentistry and supports strategies aligned with global health goals to improve access to digital care. Full article

Background: Diabetic retinopathy (DR) is a prevalent microvascular complication of diabetes mellitus, and early detection is crucial for effective management. Metabolomics profiling has emerged as a promising approach for identifying potential biomarkers associated with DR progression. This study aimed to develop a hybrid explainable artificial intelligence (XAI) model for targeted metabolomics analysis of patients with DR, utilizing a focused approach to identify specific metabolites exhibiting varying concentrations among individuals without DR (NDR), those with non-proliferative DR (NPDR), and individuals with proliferative DR (PDR) who have type 2 diabetes mellitus (T2DM). Methods: A total of 317 T2DM patients, including 143 NDR, 123 NPDR, and 51 PDR cases, were included in the study. Serum samples underwent targeted metabolomics analysis using liquid chromatography and mass spectrometry. Several machine learning models, including Support Vector Machines (SVC), Random Forest (RF), Decision Tree (DT), Logistic Regression (LR), and Multilayer Perceptrons (MLP), were implemented as solo models and in a two-stage ensemble hybrid approach. The models were trained and validated using 10-fold cross-validation. SHapley Additive exPlanations (SHAP) were employed to interpret the contributions of each feature to the model predictions. Statistical analyses were conducted using the Shapiro–Wilk test for normality, the Kruskal–Wallis H test for group differences, and the Mann–Whitney U test with Bonferroni correction for post-hoc comparisons. Results: The hybrid SVC + MLP model achieved the highest performance, with an accuracy of 89.58%, a precision of 87.18%, an F1-score of 88.20%, and an F-beta score of 87.55%. SHAP analysis revealed that glucose, glycine, and age were consistently important features across all DR classes, while creatinine and various phosphatidylcholines exhibited higher importance in the PDR class, suggesting their potential as biomarkers for severe DR. Conclusion: The hybrid XAI models, particularly the SVC + MLP ensemble, demonstrated superior performance in predicting DR progression compared to solo models. The application of SHAP facilitates the interpretation of feature importance, providing valuable insights into the metabolic and physiological markers associated with different stages of DR. These findings highlight the potential of hybrid XAI models combined with explainable techniques for early detection, targeted interventions, and personalized treatment strategies in DR management. Full article

Surveys of the Italian population typically assess general sexual behaviors (e.g., oral, vaginal and anal intercourse). However, little is known about other sexual behavior such as rough sexual behaviors, choking and slapping. Thus, an online cross-sectional survey of 4618 Italian participants was conducted. In the past month, the most prevalent sexual behaviors were solo masturbation (93.6%), partner masturbation (80.0%), oral sex (71.4% received, 75.9% performed), penile–vaginal intercourse (75.7%) and anal intercourse (12.5% received, 7.1% performed). Regarding rough sexual behaviors, the most common behaviors performed were spanking (23.3% received, 55.5% performed), choking (13.2% receive, 60.0% performed), slapping (30.1% received, 20.9% performed) and name calling (44.5% received, 37.0% performed). Our results suggest a gender difference where men and transgender/non-binary individuals perform more rough sexual behaviors compared to women. Moreover, regarding the role of consent in behaviors such as choking and slapping, our results highlight the importance of sexual and affective education to implement sexual assertiveness. In conclusion, this study adds knowledge to the limited literature on this topic, especially with respect to the Italian population. Full article

Depression is increasingly prevalent, leading to higher suicide risk. Depression detection and sentimental analysis of text inputs in cross-domain frameworks are challenging. Solo deep learning (SDL) and ensemble deep learning (EDL) models are not robust enough. Recently, attention mechanisms have been introduced in SDL. We hypothesize that attention-enabled EDL (aeEDL) architectures are superior compared to attention-not-enabled SDL (aneSDL) or aeSDL models. We designed EDL-based architectures with attention blocks to build eleven kinds of SDL model and five kinds of EDL model on four domain-specific datasets. We scientifically validated our models by comparing “seen” and “unseen” paradigms (SUP). We benchmarked our results against the SemEval (2016) sentimental dataset and established reliability tests. The mean increase in accuracy for EDL over their corresponding SDL components was 4.49%. Regarding the effect of attention block, the increase in the mean accuracy (AUC) of aeSDL over aneSDL was 2.58% (1.73%), and the increase in the mean accuracy (AUC) of aeEDL over aneEDL was 2.76% (2.80%). When comparing EDL vs. SDL for non-attention and attention, the mean aneEDL was greater than aneSDL by 4.82% (3.71%), and the mean aeEDL was greater than aeSDL by 5.06% (4.81%). For the benchmarking dataset (SemEval), the best-performing aeEDL model (ALBERT+BERT-BiLSTM) was superior to the best aeSDL (BERT-BiLSTM) model by 3.86%. Our scientific validation and robust design showed a difference of only 2.7% in SUP, thereby meeting the regulatory constraints. We validated all our hypotheses and further demonstrated that aeEDL is a very effective and generalized method for detecting symptoms of depression in cross-domain settings. Full article

This work presents a comparison between a global and a national land cover map, namely the ESA WorldCover 2020 (WC20) and the Portuguese use/land cover map (Carta de Uso e Ocupação do Solo 2018) (COS18). Such a comparison is relevant given the current amount of publicly available LULC products (either national or global) where such comparative studies enable a better understanding regarding different sets of LULC information and their production, focus and characteristics, especially when comparing authoritative maps built by national mapping agencies and global land cover focused products. Moreover, this comparison is also aimed at complementing the global validation report released with the WC20 product, which focused on global and continental level accuracy assessments, with no additional information for specific countries. The maps were compared by following a framework composed by four steps: (1) class nomenclature harmonization, (2) computing cross-tabulation matrices between WC20 and the Portuguese map, (3) determining the area occupied by each harmonized class in each data source, and (4) visual comparison between the maps to illustrate their differences focusing on Portuguese landscape details. Some of the differences were due to the different minimum mapping unit ofCOS18 and WC20, different nomenclatures and focuses on either land use or land cover. Overall, the results show that while WC20 detail is able to distinguish small occurrences of artificial surfaces and grasslands within an urban environment, WC20 is often not able to distinguish sparse/individual trees from the neighboring cover, which is a common occurrence in the Portuguese landscape. While selecting a map, users should be aware that differences between maps can have a range of causes, such as scale, temporal reference, nomenclature and errors. Full article

Loneliness is considered a prognostic factor for poorer health status in the elderly. It is proposed to analyze the role of loneliness in health status in terms of various factors. A total of 1747 individuals from the pilot survey of the Aging in Spain Longitudinal Study (ELES-PS) were reviewed. ELES is a cross-sectional study for collecting health variables, food habits, socioeconomic data, and cognitive and functional capacities, which was carried out on a Spanish representative sample of noninstitutionalized persons of 50 years of age or older. Moreover, since telomere shortening is associated with cellular senescence, 35 telomere-related SNPs and cognitive impairments were analyzed. The results characterize the “solos” as males of 50–60 years, who were overweight and had lower levels of hemoglobin and neutrophils. There is also an association between five SNPs related to telomere length and BDNF. A group of people with loneliness and depression was identified with poorer health and cognitive status, poorer perception of their quality of life, poorer quality of sleep, and lower physical activity. Therefore, it follows that telomeres and BDNF play a role as intermediaries between loneliness and depression and their relationship with a worse state of health. Full article

The biochar application can improve the physiochemical properties of both sandy and clayey loam soils and is considered a potential adaptation tool toward climate change. Therefore, the current study is novel in combining water-hyacinth-derived biochar with organic manures as a suggested effective way of treating the soil with biochar under arid and semiarid conditions. Water hyacinth weeds were slow pyrolyzed at a temperature of 300 °C, which resulted in nonalkaline biochar with a pH value of 6.31, which is suitable for alkaline soils. A pot experiment was established to study the impact of the solo application of nonalkaline water-hyacinth-derived biochar (WHB) and its combined application with farmyard (WHB/FM) and poultry manure (WHB/PM) at a rate of 1.5 and 3%, respectively, on some chemical and physical properties of sandy and clay loam soils and some barley’s growth parameters. WHB, WHB/FM, and WHB/PM significantly affected the soil pH at different application rates (1.5 and 3%) in sandy soil. A considerable alteration in water-stable aggregates (WSA), dispersion ratio (DR), available water content (AWC), and cation ratio of soil structural stability (CROSS) index resulted from combining manures (FM and PM) with biochar better than the solo application of biochar. WHB/PM treatments had a superior effect in improving barley’s growth. Relative increases were by 37.3 and 11.0% in plant height and by 61.6 and 28.5% in the dry matter in sandy and clayey loam soils, respectively. Under the conditions of this study, we can conclude that treating the soil with WHB/PM at a rate of 1.5 and 3% is the most effective application. The current study may have a vital role in Egyptian agriculture sustainability by enhancing the soil characteristics of the old agricultural and the newly reclaimed lands. Full article

Background: COVID-19 is a disease with multiple variants, and is quickly spreading throughout the world. It is crucial to identify patients who are suspected of having COVID-19 early, because the vaccine is not readily available in certain parts of the world. Methodology: Lung computed tomography (CT) imaging can be used to diagnose COVID-19 as an alternative to the RT-PCR test in some cases. The occurrence of ground-glass opacities in the lung region is a characteristic of COVID-19 in chest CT scans, and these are daunting to locate and segment manually. The proposed study consists of a combination of solo deep learning (DL) and hybrid DL (HDL) models to tackle the lesion location and segmentation more quickly. One DL and four HDL models—namely, PSPNet, VGG-SegNet, ResNet-SegNet, VGG-UNet, and ResNet-UNet—were trained by an expert radiologist. The training scheme adopted a fivefold cross-validation strategy on a cohort of 3000 images selected from a set of 40 COVID-19-positive individuals. Results: The proposed variability study uses tracings from two trained radiologists as part of the validation. Five artificial intelligence (AI) models were benchmarked against MedSeg. The best AI model, ResNet-UNet, was superior to MedSeg by 9% and 15% for Dice and Jaccard, respectively, when compared against MD 1, and by 4% and 8%, respectively, when compared against MD 2. Statistical tests—namely, the Mann–Whitney test, paired t-test, and Wilcoxon test—demonstrated its stability and reliability, with p < 0.0001. The online system for each slice was <1 s. Conclusions: The AI models reliably located and segmented COVID-19 lesions in CT scans. The COVLIAS 1.0_Lesion lesion locator passed the intervariability test. Full article

Recent data have demonstrated substantial efficacy with poly (ADP-ribose) polymerase (PARP) inhibitors as treatment and/or maintenance therapy in patients with newly diagnosed advanced epithelial ovarian cancer (EOC). Here, we review efficacy and safety results from four recent Phase III trials in newly diagnosed EOC: SOLO1 (olaparib), PAOLA-1 (olaparib in combination with bevacizumab), PRIMA (niraparib), and VELIA (veliparib). The implications of these data for current clinical practice and areas for future research are discussed, including ongoing studies of targeted agents in the newly diagnosed setting. Data from SOLO1, PAOLA-1, PRIMA, and VELIA confirm the benefit of PARP inhibitors (olaparib, niraparib, veliparib) for women with newly diagnosed EOC. The greatest benefit was seen in patients with a BRCA1 and/or BRCA2 mutation or in the homologous recombination deficiency (HRD)-test positive subgroup. These four well-conducted studies have generated practice-changing data. However, deciding how to apply these results in clinical practice is challenging, and substantial differences in trial design impede cross-trial comparisons. Recent PARP inhibitor approvals (olaparib, niraparib) in the newly diagnosed EOC setting have provided new maintenance treatment options for a broader patient population. The results of these studies call for personalized medicine based on biomarker profile and other factors, including tolerability, cost considerations, and physician and patient preference. Important areas for future research include appropriate use of both BRCA mutation and HRD testing to inform magnitude of PARP inhibitor benefit as well as exploring further options for patients who are HRD-test negative and for those who become PARP inhibitor resistant. Full article

Background: COVID-19 lung segmentation using Computed Tomography (CT) scans is important for the diagnosis of lung severity. The process of automated lung segmentation is challenging due to (a) CT radiation dosage and (b) ground-glass opacities caused by COVID-19. The lung segmentation methodologies proposed in 2020 were semi- or automated but not reliable, accurate, and user-friendly. The proposed study presents a COVID Lung Image Analysis System (COVLIAS 1.0, AtheroPoint™, Roseville, CA, USA) consisting of hybrid deep learning (HDL) models for lung segmentation. Methodology: The COVLIAS 1.0 consists of three methods based on solo deep learning (SDL) or hybrid deep learning (HDL). SegNet is proposed in the SDL category while VGG-SegNet and ResNet-SegNet are designed under the HDL paradigm. The three proposed AI approaches were benchmarked against the National Institute of Health (NIH)-based conventional segmentation model using fuzzy-connectedness. A cross-validation protocol with a 40:60 ratio between training and testing was designed, with 10% validation data. The ground truth (GT) was manually traced by a radiologist trained personnel. For performance evaluation, nine different criteria were selected to perform the evaluation of SDL or HDL lung segmentation regions and lungs long axis against GT. Results: Using the database of 5000 chest CT images (from 72 patients), COVLIAS 1.0 yielded AUC of ~0.96, ~0.97, ~0.98, and ~0.96 (p-value < 0.001), respectively within 5% range of GT area, for SegNet, VGG-SegNet, ResNet-SegNet, and NIH. The mean Figure of Merit using four models (left and right lung) was above 94%. On benchmarking against the National Institute of Health (NIH) segmentation method, the proposed model demonstrated a 58% and 44% improvement in ResNet-SegNet, 52% and 36% improvement in VGG-SegNet for lung area, and lung long axis, respectively. The PE statistics performance was in the following order: ResNet-SegNet > VGG-SegNet > NIH > SegNet. The HDL runs in <1 s on test data per image. Conclusions: The COVLIAS 1.0 system can be applied in real-time for radiology-based clinical settings. Full article

Although solo dining motivated by self-determined solitude can be a positive and healthy experience for individuals, solo dining that is not motivated by self-determined solitude can trigger physical and mental health problems. This study examined the associations among solo dining, self-determined solitude, and depression in university students. Accordingly, an online survey was conducted on 372 university students. The results show that students who live alone, those in poor health, and those with more frequent solo dining experiences had higher depression scores than others. Whereas satisfaction with solo dining was high when voluntary solitude was high, female students displayed higher depression scores when they had low self-determined solitude or high non-self-determined solitude, and when they had a higher frequency of eating lunch alone, compared to their male counterparts. University undergraduates who live and dine alone, owing to non-self-determined solitude, are highly vulnerable to mental health problems, including depression. Hence, interventions that foster social connectedness and entail the identification of factors accounting for students’ non-self-determined solitude should be developed. Full article

Future deep space astronauts must maintain adequate nutrition despite highly stressful, isolated, confined and dangerous environments. The present case-study investigated appetite regulating hormones, nutrition status, and physical and emotional stress in a space analog condition: an explorer conducting a 93-day unsupported solo crossing of Antarctica. Using the dried blood spot (DBS) method, the subject drew samples of his blood on a regular basis during the expedition. The DBSs were later analyzed for the appetite regulating hormones leptin and adiponectin. Energy intake and nutritional status were monitored by analysis of albumin and globulin (including their ratio). Interleukin-6 (IL-6) was also analyzed and used as an energy sensor. The results showed a marked reduction in levels of the appetite-reducing hormone, leptin, and the appetite stimulating hormone, adiponectin, during both extreme physical and psychological strain. Nutrition status showed a variation over the expedition, with below-normal levels during extreme psychological strain and levels abutting the lower bounds of the normal range during a phase dominated by extreme physical hardship. The IL-6 levels varied substantially, with levels above the normal range except during the recovery phase. It was concluded that a daily intake of 5058 to 5931 calories seemed to allow recovery of both appetite and nutritional status between extreme physical and psychological hardship during a long Arctic expedition. Furthermore, IL-6 may be a sensor in the muscle-liver, muscle-fat and muscle-brain crosstalk. These results may help guide nutrition planning for future astronaut crews, mountaineers and others involved in highly demanding missions. Full article

An observational cross-sectional survey was planned to analyze the weekly workload reduction of German dentists during lockdown due to the global COVID-19 pandemic. Participants were predominantly members of the Free Association of German Dentists and filled in an online questionnaire. The questionnaire was sent to a total of 9416 dentists, with a response rate of 27.98% (n = 2635). Respondents were divided into seven macro areas by gross domestic product. Nearly two-thirds of dentists (65.16%) reported a reduction in their practice workload of more than 50% compared to the pre-pandemic period with statistically significant differences between German macro areas (p < 0.01). Weekly workload was reduced during the lockdown in 93.00% of study participants, while 55.33% dental care centers with multiple employed dentists under the direction of a non-dentist general manager had only a 40% reduction in weekly workload compared to a solo practice or a practice of a dentist with an employed dentist (30.24% and 28.39%, respectively). Dentists in Germany drastically reduced their practice activity during the first wave of the COVID-19 lockdown, both in rural and urban areas. Short, medium, and long-term effects of the pandemic on dental practices, dental staff as well as patient care need to be further investigated. Full article

The U.S. Geological Survey is actively investigating remote sensing of surface velocity and river discharge (discharge) from satellite-, high altitude-, small, unmanned aircraft systems- (sUAS or drone), and permanent (fixed) deployments. This initiative is important in ungaged basins and river reaches that lack the infrastructure to deploy conventional streamgaging equipment. By coupling alternative discharge algorithms with sensors capable of measuring surface velocity, streamgage networks can be established in regions where data collection was previously impractical or impossible. To differentiate from satellite or high-altitude platforms, near-field remote sensing is conducted from sUAS or fixed platforms. QCam is a Doppler (velocity) radar mounted and integrated on a 3DR^© Solo sUAS. It measures the along-track surface velocity by spot dwelling in a river cross section at a vertical where the maximum surface velocity is recorded. The surface velocity is translated to a mean-channel (mean) velocity using the probability concept (PC), and discharge is computed using the PC-derived mean velocity and cross-sectional area. Factors including surface-scatterer quality, flight altitude, propwash, wind drift, and sample duration may affect the radar-returns and the subsequent computation of mean velocity and river discharge. To evaluate the extensibility of the method, five science flights were conducted on four rivers of varying size and dynamics and included the Arkansas River, Colorado (CO), USA (two events); Salcha River near Salchaket, Alaska (AK), USA; South Platte River, CO, USA; and the Tanana River, AK, USA. QCam surface velocities and river discharges were compared to conventional streamgaging methods, which represented truth. QCam surface velocities for the Arkansas River, Salcha River, South Platte River, and Tanana River were 1.02 meters per second (m/s) and 1.43 m/s; 1.58 m/s; 0.90 m/s; and 2.17 m/s, respectively. QCam discharges (and percent differences) were 9.48 (0.3%) and 20.3 cubic meters per second (m³/s) (2.5%); 62.1 m³/s (−10.4%); 3.42 m³/s (7.3%), and 1579 m³/s (−18.8%). QCam results compare favorably with conventional streamgaging and are a viable near-field remote sensing technology that can be operationalized to deliver real-time surface velocity, mean velocity, and river discharge, if cross-sectional area is available. Full article