Explore the vast range of titles available.

Background and aims In recent years, more and more inflammatory indicators have been studied to predict the long-term survival of patients with ampullary carcinoma (AC) after radical resection, but these prognostic indicators are still controversial. Therefore, based on previous inflammation scores, this study established a novel, easily accessible, more feasible and more predictive prognostic marker [Carbohydrate antigen199 to gamma-glutamyltransferase ratio (CA19-9/GGT)] to better assess the prognostic significance in AC patients undergoing radical resection. Methods Overall survival (OS) and recurrence-free survival (RFS) were analyzed by Cox regression model. Correlation between CA19-9/GGT and clinicopathological variables were analyzed by Chi-squared test, Fisher ' s exact test, independent sample t test and Mann–Whitney U test. The performance of prognostic indexes is compared by the consistency index (C-index). The prediction accuracy of nomogram is further confirmed by calibration curve and decision curve analysis (DCA). Results CA19-9/GGT was an independent risk factor affecting OS [ P  = 0.001, hazard ratio (HR) 2.459, 95% confidence intervals (CI) 1.450–4.167] and RFS ( P  = 0.002, HR 2.333, 95% CI 1.371–3.971) in multivariate analysis. The optimal cut-off value of CA19-9/GGT was 0.14. In CA19-9/GGT correlation analysis, high risk group (> 0.14) was significantly associated with poor prognosis. The predictive performance of CA19-9/GGT (OS: C-index = 0.753, RFS: C-index = 0.745) was confirmed to be superior to other prognostic indicators according to the C-index. Compared with the simple AJCC staging system, the Nomogram prediction model (OS: C-index = 0.787, RFS: C-index = 0.795) established by the combination of CA19-9/GGT and AJCC 8th TNM staging system has higher prediction accuracy. Conclusions CA19-9/GGT was an independent prognostic indicator after radical resection of AC. Incorporating CA19-9/GGT into the AJCC TNM staging system optimized the prediction accuracy of the TNM staging system, and further verified the predictive value of CA19-9/GGT.

Depression is the most prevalent psychiatric disorder worldwide and remains incurable; however, there is little research on its prevention. The leading cause of depression is stress, and music has been hypothesized to alleviate stress. To examine the potential beneficial effects of music on stress and depression, we subjected mice to chronic unpredictable mild stress (CUMS) during the day and music at night. Strikingly, our results indicated that music completely prevented CUMS-induced depression and anxiety-like behaviors in mice, as assessed by the open field, tail suspension, sucrose preference, novelty suppressed feeding, and elevated plus maze tests. We found that listening to music restored serum corticosterone levels in CUMS mice, which may contribute to the beneficial effects of music on the mouse brain, including the restoration of BDNF and Bcl-2 levels. Furthermore, listening to music prevented CUMS-induced oxidative stress in the serum, prefrontal cortex, and hippocampus of mice. Moreover, the CUMS-induced inflammatory responses in the prefrontal cortex and hippocampus of mice were prevented by listening to music. Taken together, we have demonstrated for the first time in mice experiments that listening to music prevents stress-induced depression and anxiety-like behaviors in mice. Music may restore hypothalamus-pituitary-adrenal axis homeostasis, preventing oxidative stress, inflammation, and neurotrophic factor deficits, which had led to the observed phenotypes in CUMS mice.

An imbalance of the gut microbiota, termed dysbiosis, has a substantial impact on host physiology. However, the mechanism by which host deals with gut dysbiosis to maintain fitness remains largely unknown. In Caenorhabditis elegans , Escherichia coli , which is its bacterial diet, proliferates in its intestinal lumen during aging. Here, we demonstrate that progressive intestinal proliferation of E. coli activates the transcription factor DAF-16, which is required for maintenance of longevity and organismal fitness in worms with age. DAF-16 up-regulates two lysozymes lys-7 and lys-8 , thus limiting the bacterial accumulation in the gut of worms during aging. During dysbiosis, the levels of indole produced by E. coli are increased in worms. Indole is involved in the activation of DAF-16 by TRPA-1 in neurons of worms. Our finding demonstrates that indole functions as a microbial signal of gut dysbiosis to promote fitness of the host.

Background Sleep deprivation and overload have been associated with increased risks of both depression and mortality. However, no study has quantitatively compared the effects of objective and subjective sleep duration on mortality or examined the mediating role of depressive symptoms in these associations. Methods Utilizing data from the NHANES 2011–2014, this study employed structural equation modeling (SEM) to explore the impact of depressive symptoms, measured by Patient Health Questionnaire (PHQ-9) scores, on the relationship between both objective and subjective sleep durations and all-cause mortality. Results The study included 7838 participants, comprising 4392 women (55.96%) with a mean age of 46.51 (0.46) years. Over a median 6.83-year follow-up, 582 deaths occurred. The restricted cubic spline curves demonstrated a J-shaped relationship between objective sleep duration and the all-cause mortality risk, and a U-shaped relationship between subjective sleep duration and the all-cause mortality risk. SEM analysis revealed that when subjective sleep duration was < 7 h/day, the indirect effect of sleep duration on all-cause mortality was − 0.013 ( P  = 0.003), and the mediation proportion of PHQ-9 scores was 40.63%. When objective sleep duration ≥ 7 h/day, the indirect effect of sleep duration on all-cause mortality was 0.003 ( P  = 0.028), and the mediation proportion of PHQ-9 scores was 2.10%. Conclusions The study confirmed a J-shaped and a U-shaped correlation for objective and subjective sleep duration with mortality risk. Depressive symptoms significantly mediated the association between shorter subjective sleep duration and mortality. This suggests that there is a need to focus on the co-morbidity of subjective sleep deprivation and depression.

Depression is associated with poorer outcomes in a wide spectrum of surgeries but the specific effects of depression in patients undergoing cervical spine surgery are unknown. This study aimed to evaluate the prevalence and impact of pre-surgical clinical depression on pain and other outcomes after surgery for cervical degenerative disc disease using a national representative database. Data of patients with cervical myelopathy and radiculopathy were extracted from the 2005–2014 US Nationwide Inpatient Sample (NIS) database. Included patients underwent anterior discectomy and fusion (ACDF). Acute or chronic post-surgical pain, postoperative complications, unfavorable discharge, length of stay (LOS) and hospital costs were evaluated. Totally 215,684 patients were included. Pre-surgical depression was found in 29,889 (13.86%) patients, with a prevalence nearly doubled during 2005–2014 in the US. Depression was independently associated with acute or chronic post-surgical pain (aOR: 1.432), unfavorable discharge (aOR: 1.311), prolonged LOS (aOR: 1.152), any complication (aOR: 1.232), respiratory complications/pneumonia (aOR: 1.153), dysphagia (aOR: 1.105), bleeding (aOR: 1.085), infection/sepsis (aOR: 1.529), and higher hospital costs (beta: 1080.640) compared to non-depression. No significant risk of delirium or venous thrombotic events was observed in patients with depression as compared to non-depression. Among patients receiving primary surgery, depression was independently associated with prolonged LOS (aOR: 1.150), any complication (aOR:1.233) and postoperative pain (aOR:1.927). In revision surgery, no significant associations were found for prolonged LOS, any complication or pain. In conclusion, in the US patients undergoing ACDF, pre-surgical clinical depression predicts post-surgical acute or chronic pain, a slightly prolonged LOS and the presence of any complication. Awareness of these associations may help clinicians stratify risk preoperatively and optimize patient care.

The BeiDou navigation satellite system (BDS) developed by China can provide users with high precision, as well as all-weather and real-time positioning and navigation. It can be widely used in many applications. However, new challenges emerge with the development of 5G communication system and Internet of Things (IoT) technologies. The BDS needs to be suitable for the large-scaled terminal scenario and provides higher positioning precision. In this paper, a BeiDou differential positioning method based on IoT and edge computing is proposed. The computational pressure on the data center is offloaded to the edge nodes when the massive positioning requests of IoT terminals need to be processed. To ensure the load balancing of the edge nodes, the resource allocation of the terminal positioning requests is performed with the improved genetic algorithm, thereby reducing the service delay of the entire edge network. Moreover, the optimized unscented Kalman filter based on the edge node (EUKF) algorithm is used to improve the positioning precision of IoT terminals. The results demonstrate that the proposed positioning method has better positioning performance which can provide the real-time positioning service for the large-scale IoT terminals.

Background Chronic diseases pose substantial healthcare burdens globally, notably in aging nations like China. Internet Medical Services (IMS) demonstrate significant potential to mitigate healthcare challenges in chronic disease management through optimized resource allocation and enhanced remote care capabilities. However, persistent adoption disparities and the “high demand–low penetration” paradox highlight persistent barriers stemming from the digital divide. This study aims to investigate factors influencing IMS utilization among chronic disease patients, examining their effects across specific IMS domains and acceptance pathways, thereby offering new insights for optimizing chronic disease management. Methods This study extended the Technology Acceptance Model (TAM) by integrating eHealth literacy and Technology anxiety to evaluate the utilization of IMS among 520 patients with chronic diseases in Jinan, China. IMS was categorized by functional domains (Information Access, Convenience Services, Online Health) and utilization stages (Awareness, Want, Adoption). The dual-method analysis: Awareness-Want-Adoption Gap (AWAG) matrix for service-specific disparity mapping and Structural Equation Modeling (SEM) to quantify perceptual drivers, providing a panoramic perspective to deconstruct the complex utilization. Results Information Access IMS showed the highest acceptance, while Online Health exhibited severe Want-to-Adoption collapse (71.43% gap). Affluent patients demonstrated paradoxical rejection of Online Health despite high Awareness. SEM confirmed Perceived Usefulness (β = 0.338–0.423, P  < 0.001) and eHealth literacy (β = 0.184–0.395, P  < 0.001) are significant and direct drivers of IMS utilization, with stage-specificity observed across the utilization process. Matrix analysis identified critical barriers for vulnerable subgroups: rural residents, elders (≥ 70 years), and low-education (≤ 9 years) patients. Conclusions IMS adoption is governed by multidimensional determinants beyond access, including cognitive, socio-economic, and other factors. Counterintuitive patterns (e.g., affluent patients’ rejection of Online Health) necessitate tiered interventions, such as eHealth literacy programs for vulnerable groups, service standardization to mitigate distrust, and regulatory frameworks to ensure data security. This study’s dual-method framework (matrix analysis and SEM) critically delineated barrier typologies through staged decomposition, establishing an evidence-based scaffold for optimizing digital health equity.

Spiking neural networks (SNNs) have recently demonstrated outstanding performance in a variety of high-level tasks, such as image classification. However, advancements in the field of low-level assignments, such as image reconstruction, are rare. This may be due to the lack of promising image encoding techniques and corresponding neuromorphic devices designed specifically for SNN-based low-level vision problems. This paper begins by proposing a simple yet effective undistorted weighted-encoding-decoding technique, which primarily consists of an Undistorted Weighted-Encoding (UWE) and an Undistorted Weighted-Decoding (UWD). The former aims to convert a gray image into spike sequences for effective SNN learning, while the latter converts spike sequences back into images. Then, we design a new SNN training strategy, known as Independent-Temporal Backpropagation (ITBP) to avoid complex loss propagation in spatial and temporal dimensions, and experiments show that ITBP is superior to Spatio-Temporal Backpropagation (STBP). Finally, a so-called Virtual Temporal SNN (VTSNN) is formulated by incorporating the above-mentioned approaches into U-net network architecture, fully utilizing the potent multiscale representation capability. Experimental results on several commonly used datasets such as MNIST, F-MNIST, and CIFAR10 demonstrate that the proposed method produces competitive noise-removal performance extremely which is superior to the existing work. Compared to ANN with the same architecture, VTSNN has a greater chance of achieving superiority while consuming ~1/274 of the energy. Specifically, using the given encoding-decoding strategy, a simple neuromorphic circuit could be easily constructed to maximize this low-carbon strategy.

Methicillin-resistant Staphylococcus aureus (MRSA) pneumonia is an important issue with significant morbidity and mortality in clinical practice, especially in diabetes mellitus (DM). Studies focusing on S. aureus pneumonia in DM is limited, we sought to make a relatively comprehensive exploration of clinical characteristics, antimicrobial resistance, and risk factors for mortality of S. aureus pneumonia in DM and non-diabetics mellitus (non-DM). A retrospective study was conducted in Ruijin Hospital from 2014 to 2017. The characteristics of DM and non-DM patients were assessed, including demographics, comorbidities, using of invasive mechanical ventilation, Hemoglobin A1c (HbA1C), confusion, urea, respiratory rate, blood pressure, age ≥65 years (CURB-65) score, length of hospital stay, clinical outcomes, antimicrobial susceptibility. Independent risk factors for mortality were identified by univariate and multivariate logistic regression analysis. A total of 365 patients with S. aureus pneumonia were included in our study, including 144 with DM and 221 non-DM. DM patients were more susceptible to MRSA infection (65.3% vs. 56.1%, P > 0.05), suffered from much severer pneumonia with a higher CURB-65 score, invasive mechanical ventilation rate (46.5% vs. 28.1%, P < 0.01) and mortality rates (30.6% vs. 23.1%, P > 0.05); almost all DM patients had higher antimicrobial resistance than non-DM patients, the DM group had a higher co-infection rate (47.2% vs. 45.7%, P > 0.05), and Acinetobacter baumannii was the most common bacterium in DM, while Klebsiella pneumoniae ranked first in patients with non-DM. Independent risk factors for pneumonia-related mortality were MRSA and CURB-65. Higher HbA1c levels were linked to a higher MRSA infection and co-infection rate and more severe pneumonia, leading to an increase in mortality. DM patients with poor glucose control are more susceptible to MRSA infection. They suffer from higher antimicrobial resistance, a higher co-infection rate, and much severer pneumonia than non-DM. MRSA itself is an independent risk factor for mortality in all patients.