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Insulin/IGF-1 Signaling (IIS) is known to constrain longevity by inhibiting the transcription factor FOXO. How phosphorylation mediated by IIS kinases regulates lifespan beyond FOXO remains unclear. Here, we profile IIS-dependent phosphorylation changes in a large-scale quantitative phosphoproteomic analysis of wild-type and three IIS mutant Caenorhabditis elegans strains. We quantify more than 15,000 phosphosites and find that 476 of these are differentially phosphorylated in the long-lived daf-2/insulin receptor mutant. We develop a machine learning-based method to prioritize 25 potential lifespan-related phosphosites. We perform validations to show that AKT-1 pT492 inhibits DAF-16/FOXO and compensates the loss of daf-2 function, that EIF-2α pS49 potently inhibits protein synthesis and daf-2 longevity, and that reduced phosphorylation of multiple germline proteins apparently transmits reduced DAF-2 signaling to the soma. In addition, an analysis of kinases with enriched substrates detects that casein kinase 2 (CK2) subunits negatively regulate lifespan. Our study reveals detailed functional insights into longevity. How phosphorylation mediated by Insulin/IGF-1 Signaling kinases regulates lifespan remains unclear. Here the authors perform a large-scale quantitative phosphoproteomic analysis of wildtype and IIS mutant C. elegans strains to reveal detailed functional insights into longevity.

To improve chemical cross-linking of proteins coupled with mass spectrometry (CXMS), we developed a lysine-targeted enrichable cross-linker containing a biotin tag for affinity purification, a chemical cleavage site to separate cross-linked peptides away from biotin after enrichment, and a spacer arm that can be labeled with stable isotopes for quantitation. By locating the flexible proteins on the surface of 70S ribosome, we show that this trifunctional cross-linker is effective at attaining structural information not easily attainable by crystallography and electron microscopy. From a crude Rrp46 immunoprecipitate, it helped identify two direct binding partners of Rrp46 and 15 protein-protein interactions (PPIs) among the co-immunoprecipitated exosome subunits. Applying it to E. coli and C. elegans lysates, we identified 3130 and 893 inter-linked lysine pairs, representing 677 and 121 PPIs. Using a quantitative CXMS workflow we demonstrate that it can reveal changes in the reactivity of lysine residues due to protein-nucleic acid interaction. Proteins fold into structures that are determined by the order of the amino acids that they are built from. These structures enable the protein to carry out its role, which often involves interacting with other proteins. Chemical cross-linking coupled with mass spectrometry (CXMS) is a powerful method used to study protein structure and how proteins interact, with a benefit of stabilizing and capturing brief interactions. CXMS uses a chemical compound called a linker that has two arms, each of which can bind specific amino acids in a protein or in multiple proteins. Only when the regions are close to each other can they be “cross-linked” in this way. After cross-linking, the proteins are cut into small pieces known as peptides. The cross-linked peptides are then separated from the non cross-linked ones and characterized. Although CXMS is a popular method, there are aspects about it that limit its use. It does not work well on complex samples that contain lots of different proteins, as it is difficult to separate the cross-linked peptides from the overwhelming amounts of non cross-linked peptides. Also, although it can be used to detect changes in the shape of a protein, which are often crucial to the protein's role, the method has not been smoothed out. Tan, Li et al. have now developed a new cross-linker called Leiker that addresses these limitations. Leiker cross-links the amino acid lysine to another lysine, and contains a molecular tag that allows cross-linked peptides to be efficiently purified away from non cross-linked peptides. As part of a streamlined workflow to detect changes in the shape of a protein, Leiker also contains a region that can be labeled. Analysing a bacterial ribosome, which contains more than 50 proteins, showed that Leiker-based CXMS could detect many more protein interactions than previous studies had. These included interactions that changed too rapidly to be studied by other structural methods. Tan, Li et al. then applied Leiker-based CXMS to the entire contents of bacterial cells at different stages of growth, and identified a protein interaction that is only found in growing cells. In future, Leiker will be useful for analyzing the structure of large protein complexes, probing changes in protein structure, and mapping the interactions between proteins in complex mixtures.

Background Although frailty is highly prevalent in hospitalized older heart failure (HF) patients, its management is often delayed. Understanding the unique experiences of frail elderly HF patients and the perceptions of healthcare professionals (HPs) regarding frailty can facilitate the integration of frailty prevention and intervention into clinical practice. This study aimed to use a descriptive qualitative approach to obtain the experiences of frailty in older HF patients and the perceptions of HPs concerning frailty. Methods Qualitative interviews were conducted with 16 frail elderly HF patients and 13 healthcare providers. Data were analyzed using thematic analysis. Results The interviews yielded several themes with associated subthemes: “a state of predicament,” “hope is gone and social isolation,” “daily adjustment and attempts to do something,” “thoughts on factors contributing to frailty,” “the need for transformation of stakeholders’ mindset regarding frailty,” “management is imperative, but there is still a way to go”. Conclusions Both elderly patients with HF and HPs described frailty as a state of predicament, involving being caught in life difficulties and falling into a treatment dilemma. Due to the overlap of symptoms and evaluating indicators between frailty and HF, it’s essential to thoroughly understand modifiable risk factors that could worsen frailty and develop a specific frailty assessment tool for HF patients. Adequate social support, multidisciplinary collaboration, and a frailty education program for patients, caregivers, and HPs are essential to facilitate frailty management and improvement.

Invasive fractional flow reserve (FFR) is the gold standard to assess the functional coronary stenosis. The non-invasive assessment of diameter stenosis (DS) using coronary computed tomography angiography (CTA) has high false positive rate in contrast to FFR. Combining CTA with computational fluid dynamics (CFD), recent studies have shown promising predictions of FFRCT for superior assessment of lesion severity over CTA alone. The CFD models tend to be computationally expensive, however, and require several hours for completing analysis. Here, we introduce simplified models to predict noninvasive FFR at substantially less computational time. In this retrospective pilot study, 21 patients received coronary CTA. Subsequently a total of 32 vessels underwent invasive FFR measurement. For each vessel, FFR based on steady-state and analytical models (FFRSS and FFRAM, respectively) were calculated non-invasively based on CTA and compared with FFR. The accuracy, sensitivity, specificity, positive predictive value and negative predictive value were 90.6% (87.5%), 80.0% (80.0%), 95.5% (90.9%), 88.9% (80.0%) and 91.3% (90.9%) respectively for FFRSS (and FFRAM) on a per-vessel basis, and were 75.0%, 50.0%, 86.4%, 62.5% and 79.2% respectively for DS. The area under the receiver operating characteristic curve (AUC) was 0.963, 0.954 and 0.741 for FFRSS, FFRAM and DS respectively, on a per-patient level. The results suggest that the CTA-derived FFRSS performed well in contrast to invasive FFR and they had better diagnostic performance than DS from CTA in the identification of functionally significant lesions. In contrast to FFRCT, FFRSS requires much less computational time.

As genetic genetic factors are important in SLE, so screening causative genes is of great significance for the prediction and early prevention in people who may develop SLE. At present, it is very difficult to screen causative genes through pedigrees. The analytical method described herein can be used to screen causative genes for SLE and other complex diseases through pedigrees. For the first time, 24 lupus pedigrees were analyzed by combining whole exon sequencing and a variety of biological information tools including common-specific analysis, pVAAST (pedigree variant annotation, analysis and search tool), Exomiser (Combining phenotype and PPI associated analysis), and FARVAT (family based gene burden), and the causative genes of these families with lupus identified. Selected causative genes in peripheral-blood mononuclear cells (PBMCs) were evaluated by quantitative polymerase chain reaction (qPCR). Cell division cycle 27 (CDC27) was screened out by common-specific analysis and Exomiser causative gene screening. FARVAT analysis on these families detected only CDC27 at the extremely significant level (false discovery rate <0.05) by three family-based burden analyses (BURDEN, CALPHA, and SKATO). QPCR was performed to detect for CDC27 in the PBMCs of the SLE family patients, sporadic lupus patients, and healthy people. Compared with the healthy control group, CDC27 expression was low in lupus patients (familial and sporadic patients) ( <0.05) and correlated with lupus activity indicators: negatively with C-reactive protein (CRP) ( <0.05) and erythrocyte sedimentation rate ( <0.05) and positively with complement C3 and C4 ( <0.05). The CDC27 expression was upregulated in PBMCs from SLE patients with reduced lupus activity after immunotherapy ( <0.05). Based on Receiver operating characteristic (ROC) curve analysis, the sensitivity and specificity of CDC27 in diagnosing SLE were 82.30% and 94.40%. The CDC27 gene, as found through WES combined with multiple analytical method may be a causative gene of lupus. CDC27 may serve as a marker for the diagnosis of SLE and is closely related to the lupus activity. We hope that the analytical method in this study will be used to screen causative genes for other diseases through small pedigrees, especially among non-close relatives.

Previous research has explored the factors influencing frailty in older patients with chronic heart failure (CHF), but these studies have not revealed the potential network interactions among the related factors. This study aimed to construct a Bayesian network (BN) model of frailty in older patients with CHF, analyze the predictive factors, and explore the internal relationships between these factors. A total of 439 older patients with CHF were selected using a convenience sampling method from September 2023 to March 2024 at the cardiology department of a comprehensive tertiary hospital in Nanchang, Jiangxi, China. Multivariate logistic regression analysis was used to explore the influencing factors of frailty in older patients with CHF. The BN structure was learned using the max–min hill-climbing algorithm, with parameter estimation achieved through maximum likelihood estimation. Netica software was utilized for prediction and diagnosis. The effectiveness of the model was validated using the Receiver Operating Characteristic (ROC) curve. The prevalence of frailty in older patients with CHF was found to be 53.3%. After using a multivariate logistic regression analysis model that screened the variables, the nutritional risk, physical activity, depression, multimorbidity, grip strength and left atrial diameter were included into the Bayesian network model. The Bayesian network model of frailty related factors in older CHF patients showed that nutritional risk, physical activity, depression, and multimorbidity were directly related to frailty, while grip strength and left atrial diameter were indirectly related. The study results indicated that malnutrition risk, inactivity, depression, and multimorbidity were directly related to frailty, while lower grip strength and a wider left atrial diameter were indirectly related to frailty. Enhancing frailty assessment and implementing measures addressing disease, nutrition, exercise, and psychological well-being are crucial for delaying and potentially reversing the onset and progression of frailty.

Traditionally, cardiac image analysis is done manually. Automatic image processing can help with the repetitive tasks, and also deal with huge amounts of data, a task which would be humanly tedious. This study aims to develop a spectrum-based computer-aided tool to locate the left ventricle using images obtained via cardiac magnetic resonance imaging. Discrete Fourier Transform was conducted pixelwise on the image sequence. Harmonic images of all frequencies were analyzed visually and quantitatively to determine different patterns of the left and right ventricles on spectrum. The first and fifth harmonic images were selected to perform an anisotropic weighted circle Hough detection. This tool was then tested in ten volunteers. Our tool was able to locate the left ventricle in all cases and had a significantly higher cropping ratio of 0.165 than did earlier studies. In conclusion, a new spectrum-based computer aided tool has been proposed and developed for automatic left ventricle localization. The development of this technique, which will enable the automatic location and further segmentation of the left ventricle, will have a significant impact in research and in diagnostic settings. We envisage that this automated method could be used by radiographers and cardiologists to diagnose and assess ventricular function in patients with diverse heart diseases.

Sleep is a ubiquitous, tightly regulated, and evolutionarily conserved behavior observed in almost all animals. Prolonged sleep deprivation can be fatal, indicating that sleep is a physiological necessity. However, little is known about its core function. To gain insight into this mystery, we used advanced quantitative proteomics technology to survey the global changes in brain protein abundance. Aiming to gain a comprehensive profile, our proteomics workflow included filter-aided sample preparation (FASP), which increased the coverage of membrane proteins; tandem mass tag (TMT) labeling, for relative quantitation; and high resolution, high mass accuracy, high throughput mass spectrometry (MS). In total, we obtained the relative abundance ratios of 9888 proteins encoded by 6070 genes. Interestingly, we observed significant enrichment for mitochondrial proteins among the differentially expressed proteins. This finding suggests that sleep deprivation strongly affects signaling pathways that govern either energy metabolism or responses to mitochondrial stress. Additionally, the differentially-expressed proteins are enriched in pathways implicated in age-dependent neurodegenerative diseases, including Parkinson's, Huntington's, and Alzheimer's, hinting at possible connections between sleep loss, mitochondrial stress, and neurodegeneration.

The morbidity and mortality rates of coronary heart disease are significant, with PCI being the primary treatment. The high incidence of ISR following PCI poses a challenge to its effectiveness. Currently, there are numerous studies on ISR risk prediction models after PCI, but the quality varies and there is still a lack of systematic evaluation and analysis. To systematically retrieve and evaluate the risk prediction models for ISR after PCI. A comprehensive search was conducted across 9 databases from inception to March 1, 2024. The screening of literature and extraction of data were independently carried out by two investigators, utilizing the checklist for critical appraisal and data extraction for systematic reviews of prediction modeling studies (CHARMS). Additionally, the risk of bias and applicability were evaluated using the Prediction Model Risk of Bias Assessment Tool (PROBAST). A total of 17 studies with 29 models were included, with a sample size of 175-10,004 cases, and the incidence of outcome events was 5.79%-58.86%. The area under the receiver operating characteristic curve was 0.530-0.953. The top 5 predictors with high frequency were diabetes, number of diseased vessels, age, LDL-C and stent diameter. Bias risk assessment into the research of the risk of higher bias the applicability of the four study better. The overall risk of bias in the current ISR risk prediction model post-PCI is deemed high. Moving forward, it is imperative to enhance study design and specify the reporting process, optimize and validate the model, and enhance its performance.

Objective: The purpose of this study was to clarify the concept of transitional care in patients with heart failure. Background: Transitional care is increasingly being applied in patients with heart failure, but the concept of transitional care in heart failure patients is not uniform and confused with other definitions, which limits further research and practice on transitional care for these patients. Design: Rodgers' evolutionary concept analysis. Methods: A comprehensive literature search was conducted using the PUBMED, EMBASE, EBSCO, Chinese Biological Medicine (CBM), CNKI, and WANFANG databases (up to January 26, 2023). We used Rodgers' evolutionary concept analysis method to identify related concepts, attributes, antecedents, and consequences of transitional care in patients with heart failure. Results: A total of 33 articles were included. The following attributes belonging to transitional care in patients with heart failure were extracted from the literature: self-care, multidisciplinary collaboration, and information transmission. The antecedents were patients' health status, the health literacy of patients and caregivers, the role functions of the main implementer and social and medical resources. Consequences were separated into two categories: patient-centered health outcomes (all-cause mortality, health-related quality of life, discharge preparedness, self-care behaviors, satisfaction of patients) and healthcare utilization outcomes (hospital readmission, length of hospital stay, emergency department visits). Conclusion: This study found that transitional care in heart failure patients is a systemic care process during a vulnerable period that improves patient self-management and coordination between hospital resources and social support systems for continuous management to promote smooth patient transitions between different locations. This concept analysis will inform healthcare providers in designing evidence-based interventions and quality improvement strategies to ensure that transition processes lead to desired outcomes. In addition, this study will also be helpful for developing specific assessment tools to identify patients with HF who need transitional care. Keywords: concept analysis, heart failure, transitional care, self care, collaboration