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The various effect of social isolation on depression risk among old population in China during covid-19 pandemic: A population based survey

Background The aim of the current study is to assess various effect of social isolation on depression risk among older adults during COVID-19 in China. Methods Data was obtained from the China Longitudinal Ageing Social Survey (CLASS) conducted in 2020. A total of 9883 participants were included. Depression status was assessed by 9-item Center for Epidemiological Studies Depression Scale (CES-D). Social isolation was assessed by Lubben Social Network Scale-6 (LSNS-6). The odds ratios of depression risk according to LSNS-6 categories were obtained using a logistic regression model with adjustment for potential confounding variables. Results The prevalence of depression was 31.2%, and the presence of social isolation was 37.9% during the COVID-19 among older population. A decrease in depression risk was observed with reduced isolation. The odds ratio for the lowest versus highest was 0.75 (95% confidence interval: 0.63, 0.89; Ptrend = 0.012). Friend support reduced depression risk more significantly than family support. The association between LSNS-6 friend subscale and depression risk was differentiated by LSNS-6 family subscale. In men, LSNS-6 friend subscale tended to be associated with depression risk inversely when their LSNS-6 family subscale was less than 6 (interaction P = 0.041). Similar associations and stratified modifications were observed among those who lived in rural areas (interaction P = 0.002), married (interaction P = 0.003), Han (interaction P = 0.01), lived with others (interaction P = 0.001), and so on. Conclusions Most depression cases were found to be strongly associated with social isolation during the pandemic. Our findings have provided empirical evidence for researchers to understand the association between social isolation and depression, which could help them evaluate and manage depression promptly. Because of the cross-sectional design, we cannot establish causal relationships between depression and isolation.

Occupational fall incidence associated with heated tobacco product use and lifestyle behaviors in Japan

Leveraging technology to probe mechanisms of psychopathology: A proof of concept study of inhibitory control

Objective Quantifying relevant behavioral mechanisms has relied on rigorous, time-consuming tools restricted to laboratory settings and inaccessible to the clinical community. Advances in technology provide an opportunity to develop more accessible platforms. Here, we developed CALM-IT, a novel mobile-application to experimentally assess inhibitory control in vivo Method In a transdiagnostic sample of 200 youth aged 8–20, we (i) apply knowledge from canonical inhibitory control tasks in the methodological design of the mobile application, (ii) establish feasibility and engagement with CALM-IT, (iii) assess test-retest reliability of CALM-IT, (iv) investigate the convergent validity of CALM-IT with behavioral and neural responses to laboratory-based tasks, and (v) probe clinical relevance via associations with clinical symptoms. Results First, we provide evidence that our novel inhibitory control mobile application, CALM-IT, was accessible, feasible, and engaging. Second, we found performance was reliable over time. Third, we found CALM-IT performance was associated with established measures of inhibitory control and activation in the bilateral inferior frontal gyrus. Associations with brain but not behavior survived after controlling for age. Finally, we found evidence linking impaired CALM-IT performance to increased levels of co-occurring anxiety, irritability, and attention deficit hyperactivity disorder (ADHD) symptoms. Conclusion Validation of this neuroscience-informed mobile application represents a critical first step in bridging precise, mechanism-driven research and community-based assessment of childhood psychopathology. The present work lays the groundwork for future research that could provide researchers and clinicians with a multifaceted tool to measure clinically-relevant behaviors in an engaging and accessible manner.

Enhanced absorption in organic solar cells via combined anti-reflection and Mie resonance effects in subwavelength ellipsoidal dielectric nanostructures

Exploring factors impacting Hispanic/Latinx individuals’ response to a type 2 diabetes digital storytelling intervention

Background Hispanic/Latinx individuals have high prevalence of type 2 diabetes and its complications yet often face barriers in accessing diabetes prevention and self-management interventions. One possible approach is to implement digital storytelling interventions, which involve narrative-driven videos made by individuals who have lived experience with particular conditions or illnesses. These stories can inspire viewers with similar life experiences to change behaviors or attitudes. Little is known about which characteristics influence how individuals respond to digital storytelling interventions with healthful behaviors and improved outcomes – information necessary to further tailor these interventions to improve type 2 diabetes outcomes. Previously, the Rochester Healthy Community Partnership used the digital storytelling process to develop Stories for Change Diabetes and tested intervention effectiveness. Methods We conducted a secondary analysis to examine the sociodemographic and disease-related factors that affected participants’ responses to the Stories for Change Diabetes intervention. Drawing on Social Cognitive Theory and Culture-Centric Health Promotion principles, we analyzed results from the 227 intervention participants stratified by whether they experienced a clinically meaningful decrease (>0.5%) in hemoglobin A1c between baseline and three-month follow-up. We then used multivariable logistic regressions to identify factors associated with change in hemoglobin A1c. Results Participants with diabetes duration <5 years and/or whose diabetes self-efficacy improved between baseline and 3-month follow-up were more likely to experience a meaningful decrease in hemoglobin A1c at three months (compared to participants without those characteristics). Conclusions These findings will provide insight into how digital storytelling interventions can be effectively tailored to Hispanic/Latinx individuals most likely to benefit. Trial registration: Not applicable

Modeling eye gaze velocity trajectories using GANs with spectral loss for enhanced fidelity

Abstract Accurate modeling of eye gaze dynamics is essential for advancement in human-computer interaction, neurological diagnostics, and cognitive research. Traditional generative models like Markov models often fail to capture the complex temporal dependencies and distributional nuance inherent in eye gaze trajectories data. This study introduces a Generative Adversarial Network (GAN) framework employing Long Short-Term Memory (LSTM) and Convolutional Neural Network (CNN) generators and discriminators to generate high-fidelity synthetic eye gaze velocity trajectories. We conducted a comprehensive evaluation of four GAN architectures: CNN-CNN, LSTM-CNN, CNN-LSTM, and LSTM-LSTM–trained under two conditions: using only adversarial loss ( $$L_G$$ ) and using a weighted combination of adversarial and spectral losses. Our findings reveal that the LSTM-CNN architecture trained with this new loss function exhibits the closest alignment to the real data distribution, effectively capturing both the distribution tails and the intricate temporal dependencies. The inclusion of spectral regularization significantly enhances the GANs’ ability to replicate the spectral characteristics of eye gaze movements, leading to a more stable learning process and improved data fidelity. Comparative analysis with a Hidden Markov Model (HMM) optimized to four hidden states further highlights the advantages of the LSTM-CNN GAN. Statistical metrics show that the HMM-generated data significantly diverges from the real data in terms of mean, standard deviation, skewness, and kurtosis. In contrast, the LSTM-CNN model closely matches the real data across these statistics, affirming its capacity to model the complexity of eye gaze dynamics effectively. These results position the spectrally regularized LSTM-CNN GAN as a robust tool for generating synthetic eye gaze velocity data with high fidelity. Its ability to accurately replicate both the distributional and temporal properties of real data holds significant potential for applications in simulation environments, training systems, and the development of advanced eye-tracking technologies, ultimately contributing to more naturalistic and responsive human-computer interactions.

Estimating sales transitions between competing products via optimal transport

In mature markets, where products are widely adopted, understanding how customers switch between competing products is crucial for companies to conduct effective marketing actions. However, due to privacy regulations, it is increasingly difficult to obtain point-of-sale (POS) data with individual customer identifiers (IDs). In this paper, we propose a method that estimates how sales shift between products using aggregated POS data without customer IDs. We formulate this as an optimal transport problem aimed at minimizing the total cost of brand-switching and introduce two regularization terms based on assumptions about sales transitions. We then solve the optimization problem with these regularizations using a projected gradient method. We validated our approach on proprietary POS data from the Japanese beverage industry and found that the estimated transitions aligned with real market changes. For instance, during a liquor tax reform period, customers switched from products whose tax rates increased to those with lower rates. In the coffee market, many customers moved toward a newly launched brand. Although these results suggest that our method can capture market dynamics, the proprietary data limits reproducibility. In addition, the absence of customer IDs makes it impossible to track individual customer transitions. Incorporating such identifiers in future research could offer more deeper insights into consumer behavior.

S100 A16 promotes the progression of osteosarcoma by activating the PI3 K/AKT signaling pathway through ANXA2

Abstract Osteosarcoma is a common primary malignant bone tumor. S100A16 gene was reported to highly expressed in several tumor tissues while the relationship between S100A16 and osteosarcoma remains less well-understood. This study aimed to investigate the expression characteristics of S100A16 in osteosarcoma and the mechanism by which it promotes osteosarcoma progression. Firstly, by analyzing databases and assessing mRNA and protein level, we found that the expression of S100A16 was significantly promoted in osteosarcoma, as compared with normal tissue. Then transfection techniques were employed to upregulate and downregulate S100A16 in osteosarcoma cells, the results demonstrated that S100A16 can increase osteosarcoma cell viability, migration and invasion capacities, while decline osteosarcoma cell apoptosis. GSEA (gene set enrichment analysis) revealed that increased expression of S100A16 was enriched in the PI3K/AKT pathway. Cellular experiments showed that the S100A16 promoted osteosarcoma progression by activating the PI3K/AKT signaling pathway, and upregulated expression of ANXA2, a crucial protein in occurrence and development of tumors. We also found that overexpression of ANXA2 can restore the decreased levels of p-PI3K and p-AKT induced by S100A16 inhibition, which indicated that S100A16 stimulates PI3K/AKT pathway activation via ANXA2. To sum up, S100A16 can promotes osteosarcoma progression by activating the PI3K/AKT signaling pathway through ANXA2, suggesting that the S100A16/ANXA2 axis may represent a novel therapeutic target for osteosarcoma.

The TMA team and TTP pathway improved outcomes in a cohort with Thrombotic thrombocytopenic purpura

Background Providing optimal care for patients with thrombotic thrombocytopenic purpura (TTP) is challenging because of multiple involved specialties, knowledge gaps, and a high rate of disease relapse. A thrombotic microangiopathy (TMA) Team and TTP Pathway could improve outcomes. Objectives To assess if a structured TTP Pathway, supported by a TMA Team, improved TTP care by reducing TTP relapse and TTP-related death (TTP-RRD) at a rural Appalachian medical center. Methods Prospective cohort quality improvement project using the DMAIC quality improvement framework (Define, Measure, Analyze, Improve, Control) to develop a TMA Team and TTP Pathway. Pathway care included standardized use of therapeutic plasma exchange (TPE), rituximab, caplacizumab, as well as improved coordination between medical services, and regular outpatient biochemical TTP surveillance. Outcomes were determined by retrospective chart review for patients with acute TTP treated with usual care (N = 16 episodes) and the TTP Pathway (N = 16 episodes). Results and conclusions All patients had acquired TTP. TTP-RRD at 90 days was reduced from 69% with usual care to 6% with Pathway care (95% CI 0.35 to 0.90, P = 0.0004), a relative risk reduction of 91%; TTP relapse alone at 90 days was reduced from 62% to 0% (95% CI 0.36 to 0.88, P = 0.0002) with Pathway care. The number needed to treat to prevent TTP-RRD was 1.59 at 90 days. Over the project duration usual care demonstrated a hazard ratio for TTP-RRD of 12.58 compared to Pathway care. With the intervention, the duration of TPE was increased (median 6 vs 12 sessions, P < 0.05), as was use of rituximab (31.3% vs 93.8%, 95% CI −0.36 to −0.88, P = 0.003), and caplacizumab (6.3% vs 62.5%, 95% CI −0.027 to −0.81, P = 0.001). All Pathway patients underwent biochemical surveillance, and 31% had pre-emptive rituximab to reduce possibility of clinical relapse. A structured TTP Pathway significantly reduces morbidity and aligns care with modern clinical guidelines. The TMA Team is a valuable institutional resource to improve outcomes.

Investigating the impact of meteorological parameters on daily soil temperature changes using machine learning models

Abstract Soil temperature (ST) is one of the critical parameters in agricultural meteorology and significantly influences physical, chemical, and biological activities in the soil environment. One of the major challenges in agricultural studies is the limited number of synoptic stations for measuring ST. Novel data mining methods offer effective solutions for obtaining reliable estimations while reducing costs and improving accuracy. This study estimated daily ST at depths of 5 cm, 10 cm, 20 cm, 50 cm, and 100 cm using meteorological parameters from an American synoptic station over three years (2020–2022). Seasonal ARIMA (SARIMA), Multiple Linear Regression (MLR), and Artificial Neural Network (ANN) models were applied for ST prediction. A bivariate correlation test with a p-value less than 0.05 was conducted to determine the relationship between meteorological variables and ST. The results indicate that the ANN model outperforms SARIMA and MLR in predicting ST at all depths. For instance, at 5 cm depth, the ANN model achieved RMSE = 0.85, r = 0.98, MAE = 1, and PBIAS = 1.5%, compared to SARIMA (RMSE = 1.5, r = 0.96, MAE = 1.16, PBIAS = 2.5%) and MLR (RMSE = 1.35, r = 0.97, MAE = 1.3, PBIAS = 3%). Similarly, at 100 cm depth, the ANN model achieved RMSE = 0.65, r = 0.91, MAE = 0.55, and PBIAS = 2.2%, compared to SARIMA (RMSE = 1.3, r = 0.96, MAE = 0.58, PBIAS = 3.5%) and MLR (RMSE = 1.15, r = 0.91, MAE = 0.68, PBIAS = 3.8%). The analysis also revealed that surface temperature (Avg. Infrared) and air temperature (Avg. T) were the most influential parameters in ST prediction. Additionally, the ANN model exhibited the lowest error rates across all depths, highlighting its superior capability for estimating daily ST in agricultural soils. This study provides a comprehensive framework for accurately estimating daily ST at different depths, offering valuable insights for agricultural and environmental applications.

The application of deep learning in economic analysis and marketing strategy formulation in the tourism industry

The tourism industry is ever-evolving in nature, as it operates in a global marketplace that has become progressively global and offers great potential due to technological advances. The tourism industry faces challenges in accurately forecasting economic impacts and understanding visitor patterns that rapid global changes. Motivated by these needs, this research introduces the Tourism Variational Recurrent Neural Network (TourVaRNN), aiming to enhance the tourism industry by predicting economic impacts and visitor behaviors for effective marketing strategies through advanced Deep Learning (DL) techniques. The research applies a variational recurrent neural network for enhancing tourism demands and model the complex temporal dependencies within tourism data. The proposed TourVaRNN integrates variational autoencoders to capture latent variables representing visitor preferences and spending habits, while recurrent neural networks model complex temporal dependencies in tourism data. Marketing campaigns in the tourism sector can be fine-tuned through visitor segmentation, which seeks to comprehend and classify visitors according to their demographics, preferences, and behaviors. The model employs robust forecasting of economic impacts, visitor spending patterns, and behavior while accounting for uncertainty through variational inference. The implementation uses Python language on a tourism dataset comprising necessary attributes like visitor numbers, days, spending patterns, employment, international tourism samples over a specific region, and a diverse age group analyzed over a year. The proposed method is evaluated in terms of performance metrics such as economic impact assessment, visitor segmentation efficiency, inference time analysis, and budget allocation utilization for effective economic and marketing strategy analysis in the tourism industry. TourVaRNN’s improved segmentation efficiency of 15.7 percent allows for more targeted marketing, increasing engagement with visitors and income. Decisions may be made in real-time, improving operational efficiency in tourism management, thanks to a 17.5% reduction in inference time (to 40 ms). The most efficient use of funds is guaranteed by a 13.4% rise in budget allocation utilization, leading to maximum economic benefits.

Laryngeal and pleural ultrasound and acoustic radiation force impulse elastography in dogs with brachycephalic obstructive airway syndrome

Human umbilical cord mesenchymal stem cell-derived exosomes mitigate acute radiation-induced intestinal oxidative damage via the Nrf2/HO-1/NQO1 signaling pathway

Acute radiation-induced intestinal injury (ARII), a prevalent complication of abdominal radiotherapy, remains clinically challenging due to limited therapeutic options. This study demonstrates the therapeutic efficacy of human umbilical cord mesenchymal stem cell-derived exosomes (hucMSC-Exos) in mitigating ARII through Nrf2/HO-1/NQO1 pathway activation. In a rat model receiving 12 Gy abdominal irradiation, systemic hucMSC-Exos administration significantly restored intestinal mucosal integrity and reduced oxidative damage markers. Mechanistically, hucMSC-Exos potentiated the antioxidant axis by upregulating Nrf2 signaling, as evidenced by histopathological, biochemical, and molecular analyses. Complementary in vitro experiments revealed hucMSC-Exos protected irradiated IEC-6 cells from oxidative dysfunction while enhancing proliferation, effects substantially attenuated upon Nrf2 silencing via siRNA. These findings establish that hucMSC-Exos orchestrate redox equilibrium through targeted Nrf2 pathway modulation, effectively counteracting radiation-induced enterocyte apoptosis. The elucidated mechanism expands the therapeutic paradigm of MSC-derived exosomes in radioprotection and provides a clinically translatable strategy for managing ARII in oncological radiotherapy.

Comparative sustainability investigation on a novel industrial-waste-based soil stabilizer and cement based on life cycle assessment

Parental experience of the diagnostic process and its role in the decision to terminate pregnancy due to fetal abnormality; A qualitative interview study

Introduction The detection of fetal malformation is a shock to expectant parents and often initiates a diagnostic process of additional tests and ultrasound scans, that may be uncertain and stressful to the parents. The aim of the present study was to investigate how expectant parents experienced the diagnostic process and how their decision to terminate the pregnancy was reached during that process. Methods Semi-structured interviews with 11 Danish women and nine male partners one to five months after termination of pregnancy. All interviews were conducted in the participants’ homes and lasted 97–135 minutes. Thematic analysis was performed. Results 1) The theme, ‘In no man’s land’, describes the two-phased diagnostic process: First, the initial shock of a potential ultrasound finding, and the uncertain – but still hopeful – days of waiting for a follow-up scan and specialist consultation. Second, the feeling of professionalism and companionship when interacting with the fetal medicine specialists, who still could not always provide the hoped-for answers. 2) The theme, ‘Inescapable decision’, describes how decision-making oscillated as new information or potential interpretations entered the diagnostic process. The participants described a continuous contemplation of the inevitable final choice regarding continuation or termination. Being in this process – for days or weeks – was described as an emotional rollercoaster with feelings of both hope and despair until the final decision to terminate was made. Conclusion During a prenatal diagnostic process parents must endure uncertainty, waiting times and an ongoing oscillation between hope and no-hope for the pregnancy. However, the diagnostic process may also be understood as an opportunity for dialogue, reflection and adjustment, allowing for a personal and well-considered decision, even if painful.

Air quality prediction based on factor analysis combined with Transformer and CNN-BILSTM-ATTENTION models

Examining Chat GPT with nonwords and machine psycholinguistic techniques

Strings of letters or sounds that lack meaning (i.e., nonwords) have been used in cognitive psychology and psycholinguistics to provide foundational knowledge of human processing and representation, and insights into language-related performance. The present set of studies used the machine psycholinguistic approach (i.e., using nonword stimuli and tasks similar to those used with humans) to gain insight into the performance of Chat GPT in comparison to human performance. In Study 1, Chat GPT was able to provide correct definitions to many extinct words (i.e., real English words that are no longer used). In Study 2 the nonwords were real words in Spanish, and Chat GPT was prompted to provide a word that sounded similar to the nonword. Responses tended to be Spanish words unless the prompt specified that the similar sounding word should be an English word. In Study 3 Chat GPT provided subjective ratings of wordlikeness (and buyability) that correlated with ratings provided by humans, and with the phonotactic probabilities of the nonwords. In Study 4, Chat GPT was prompted to generate a new English word for a novel concept. The results of these studies highlight certain strengths and weaknesses in human and machine performance. Future work should focus on developing AI that complements or extends rather than duplicates or competes with human abilities. The machine psycholinguistic approach may help to discover additional strengths and weaknesses of human and artificial intelligences.

Early replacement of re-induction therapy following failed intensive induction treatment enhances the therapeutic efficacy of newly diagnosed AML

Long-COVID is associated with increased absenteeism from work

Long-COVID, defined as COVID-19 symptoms persisting for more than 3 months, may lead to persistent health issues requiring extensive medical care. Despite its long-term health impact, the economic impact of long-COVID remains understudied. This study examined whether individuals with long-COVID had more missed workdays compared to those without long-COVID. Adults (≥18 years old) with full-time jobs were identified from the 2022 Full-Year Population Characteristics file of the Medical Expenditure Panel Survey (MEPS). A weighted two-part model was used to identify factors associated with missed workdays due to illness. The total population analyzed included 131,685,516 adults (unweighted n = 8,210), with an average (SD) age of 43 (14) years. Among them, 46% were female and 62% were non-Hispanic White. Approximately 7% of the population experienced long-COVID. Individuals with long-COVID reported an average of 8 days missed from work (SD: 12 days), while those without long-COVID reported an average of 4 days missed (SD: 9 days). The two-part model revealed that individuals with long-COVID had 2.54 more missed workdays compared to those without long-COVID (p < 0.01), after controlling for relevant variables. These results underscore significant productivity losses associated with long-COVID, highlighting the need for policymakers and employers to implement effective strategies to address this condition.

Biomechanics characterization of an implantable ultrathin intracortical electrode through finite element method