PANoptosis-related molecular subtype and prognostic model associated with the immune microenvironment and individualized therapy in pancreatic cancer

BackgroundPANoptosis is an inflammatory type of programmed cell death regulated by PANopotosome. Mounting evidence has shown that PANoptosis could be involved in cancer pathogenesis and the tumor immune microenvironment. Nevertheless, there have been no studies on the mechanism of PANoptosis on pancreatic cancer (PC) pathogenesis.MethodsWe downloaded the data on transcriptomic and clinical features of PC patients from the Cancer Genome Atlas (TCGA) and Gene Expression Omnibus databases. Additionally, the data on copy number variation (CNV), methylation and somatic mutations of genes in 33 types of cancers were obtained from TCGA. Next, we identified the PANoptosis-related molecular subtype using the consensus clustering analysis, and constructed and validated the PANoptosis-related prognostic model using LASSO and Cox regression analyses. Moreover, RT-qPCR was performed to determine the expression of genes involved in the model.ResultsWe obtained 66 PANoptosis-related genes (PANRGs) from published studies. Of these, 24 PC-specific prognosis-related genes were identified. Pan-cancer analysis revealed complex genetic changes, including CNV, methylation, and mutation in PANRGs were identified in various cancers. By consensus clustering analysis, PC patients were classified into two PANoptosis-related patterns: PANcluster A and B. In PANcluster A, the patient prognosis was significantly worse compared to PANcluster B. The CIBERSORT algorithm showed a significant increase in the infiltration of CD8+ T cells, monocytes, and naïve B cells, in patients in PANcluster B. Additionally, the infiltration of macrophages, activated mast cells, and dendritic cells were higher in patients in PANcluster A. Patients in PANcluster A were more sensitive to erlotinib, selumetinib and trametinib, whereas patients in PANcluster B were highly sensitive to irinotecan, oxaliplatin and sorafenib. Moreover, we constructed and validated the PANoptosis-related prognostic model to predict the patient’s survival. Finally, the GEPIA and Human Protein Atlas databases were analyzed, and RT-qPCR was performed. Compared to normal tissues, a significant increase in CXCL10 and ITGB6 (associated with the model) expression was observed in PC tissues.ConclusionWe first identified the PANoptosis-related molecular subtypes and established a PANoptosis-related prognostic model for predicting the survival of patients with PC. These results would aid in exploring the mechanisms of PANoptosis in PC pathogenesis

Fully 3D printed flexible, conformal and multi-directional tactile sensor with integrated biomimetic and auxetic structure

Tactile sensors play a crucial role in the development of biologically inspired robotic prostheses, particularly in providing tactile feedback. However, existing sensing technology still falls short in terms of sensitivity under high pressure and adaptability to uneven working surfaces. Furthermore, the fabrication of tactile sensors often requires complex and expensive manufacturing processes, limiting their widespread application. Here we develop a conformal tactile sensor with improved sensing performance fabricated using an in-house 3D printing system. Our sensor detects shear stimuli through the integration of an auxetic structure and interlocking features. The design enables an extended sensing range (from 0.1 to 0.26 MPa) and provides sensitivity in both normal and shear directions, with values of 0.63 KPa−1 and 0.92 N−1, respectively. Additionally, the sensor is capable of detecting temperature variations within the range of 40−90 °C. To showcase the feasibility of our approach, we have printed the tactile sensor directly onto the fingertip of an anthropomorphic robotic hand, the proximal femur head, and lumbar vertebra. The results demonstrate the potential for achieving sensorimotor control and temperature sensing in artificial upper limbs, and allowing the monitoring of bone-on-bone load

The relationship between daytime napping and glycemic control in people with type 2 diabetes

AimTo examine the association between napping characteristics and glycemic control in people with type 2 diabetes.DesignThis study used a cross-sectional design.MethodsA convenience sample of people with type 2 diabetes (N=226) were included. Glycemic control was indicated by HbA1c which was measured by A1C Now®+. Napping characteristics including napping frequency, duration, timing, and type were measured by validated questionnaires. Other variables, such as insomnia, cognitive impairment, and depression were measured by the Insomnia Severity Index, Montreal Cognitive Assessment, and Patient Health Questionnaire-9, respectively. Multivariate linear regression analyses were performed.ResultsThe sample consisted of 122 women (54.0%), with a median age of 67 years. Their median HbA1c was 6.8%. No significant relationship was found between napping frequency and HbA1c. Among nappers, after controlling for covariates, long napping duration (≥60 min) and morning napping were both associated with poorer glycemic control. Compared with appetitive napping, restorative napping was associated with better glycemic control.ConclusionDaytime napping (e.g., duration and type) is an important modifiable factor for glycemic control in people with type 2 diabetes. This study provides new insights into the relationship between napping and glucose management among people with diabetes

Axonal Fiber Terminations Concentrate on Gyri

Convoluted cortical folding and neuronal wiring are 2 prominent attributes of the mammalian brain. However, the macroscale intrinsic relationship between these 2 general cross-species attributes, as well as the underlying principles that sculpt the architecture of the cerebral cortex, remains unclear. Here, we show that the axonal fibers connected to gyri are significantly denser than those connected to sulci. In human, chimpanzee, and macaque brains, a dominant fraction of axonal fibers were found to be connected to the gyri. This finding has been replicated in a range of mammalian brains via diffusion tensor imaging and high–angular resolution diffusion imaging. These results may have shed some lights on fundamental mechanisms for development and organization of the cerebral cortex, suggesting that axonal pushing is a mechanism of cortical folding

Relationships Between Sleep and Self-Care in Type 2 Diabetes: An Ecological Momentary Perspective

Aim: Sleep disturbance is a widespread yet often ignored complaint among people with type 2 diabetes. Sleep disturbance has been an under-examined risk factor for poor diabetes self-care. The aim of this study was to examine whether sleep disturbance is related to diabetes self-care using longitudinal data in the real-world setting. Methods: A correlational, longitudinal design was used. A convenience sample of 64 older adults with type 2 diabetes was recruited. Validated instruments were used to measure subjective sleep quality, self-care, and other primary constructs including self-efficacy, distress, fatigue, and daytime sleepiness. Participants completed a sleep and self-care diary each day for eight days to assess their subjective sleep and self-care behaviors (e.g., eating behavior, physical activity, and medication adherence). The ActiGraph was used to measure daily objective sleep and physical activity. Data were analyzed using multiple linear regression analyses and mixed-effect models. Results: Regression analysis suggested that six variables explained 51% of the variation in overall self-care. Subjective sleep quality was a strong predictor of overall self-care. The effect of sleep quality (β=-0.26) on self-care was smaller than that of the commonly reported diabetes distress (β=-0.39) but larger than that of daytime sleepiness (β=-0.21). Mixed-effect models indicated that total sleep time was negatively related to light-intensity physical activity the following day (estimates: -0.24 to -0.43). Based on the mixed-effect models, although sleep alone did not predict eating behavior the following day, its interaction with morning fatigue was a significant predictor. These findings suggested that the effect of sleep on eating behavior (e.g., conscious restriction on eating, loss of control over eating, and eating in response of emotional cues) was different for people with different levels of fatigue. Sleep was not associated with medication adherence. Conclusions: In older adults with type 2 diabetes, poor sleep quality is associated with poor self-care. Sleep likely affects self-care behaviors, especially eating behavior, through its interaction with fatigue. There is little evidence supporting the significant relationships between night sleep and daytime accelerometer-derived physical activity and medication adherence

Relationship and variation of diabetes related symptoms, sleep disturbance and sleep‐related impairment in adults with type 2 diabetes

Aim The objective of this study was to examine whether diabetes‐related symptoms (e.g. fatigue, neuropathic pain, diabetes distress and depressive symptoms) were related to sleep disturbance and sleep‐related impairment in adults with type 2 diabetes while controlling for potential covariates. Background In people with type 2 diabetes, sleep disturbance and sleep‐related impairment are common and likely associated with diabetes‐related symptoms. However, limited research has investigated the predictive ability of diabetes‐related symptoms on sleep. Design A correlational, cross‐sectional design was used. Methods Data were collected at a large university in the Midwestern United States from September 2013–March 2014. Multiple linear regression analyses were used to examine the relationship of diabetes‐related symptoms (fatigue, neuropathic pain, distress and depressive symptoms) to sleep disturbance and sleep‐related impairment. The instruments included Patient‐Reported Outcomes Measurement Information System instruments, Diabetes Symptom Checklist and Diabetes Distress Scale. Findings In this study of adults with type 2 diabetes (N = 90; 52.2% female, mean age 57.4 years), gender, A1C, neuropathic pain and fatigue were significantly related to sleep disturbance when age, diabetes duration, depressive symptoms and distress were controlled. Those variables collectively explained 52% of the variation in sleep disturbance. Fatigue was significantly associated with sleep‐related impairment when the same covariates were controlled. Conclusion Findings suggested that diabetes‐related symptoms, including neuropathic pain and fatigue, are strongly related to sleep disturbance and sleep‐related impairment in adults with type 2 diabetes, underscoring the need to include detailed assessments of neuropathic pain and fatigue when evaluating sleep

Adaptation of the Pittsburgh Sleep Quality Index in Chinese adults with type 2 diabetes

Background: Sleep disturbance is a major health issue in people with type 2 diabetes (T2DM). The Pittsburgh Sleep Quality Index (PSQI) has been the most widely used instrument to measure subjective sleep disturbance. Nevertheless, its factor structure in the context of T2DM has not been examined. The purpose of the study is to evaluate the factor structure of the PSQI in Chinese adults with T2DM and thereby to facilitate its use in clinical practice and research. Methods: The PSQI (Chinese version) was administered to 240 patients with T2DM. Confirmatory factor analysis was conducted to examine the one-factor, adapted one-factor by removing the component “use of sleep medication”, and the three-factor structure of the PSQI. Goodness-of-fit indices were used to evaluate the fit of the model. Construct validity of the resultant model was further examined using contrasted groups. Cronbach's α of the resultant model was obtained to evaluate its internal consistency. Results: The three-factor model proposed by Cole et al. did not fit the sleep data. Confirmatory factor analysis supported the adapted one-factor model with the PSQI global score as an indicator of overall sleep quality, and the goodness-of-fit indices for the adapted model were better compared to the original one-factor model. As expected, women, older adults, and patients with poor glycemic control had higher adapted PSQI global score (p < 0.01). Cronbach's α of the adapted PSQI was 0.78. Conclusion: The adapted PSQI was similar to the original PSQI in that only the component “use of sleep medication” was removed from the original scale and the one-factor scoring worked better. In contrast, the three-factor model has limited usefulness in this population