Machine learning-based predictive model for prevention of metabolic syndrome

Metabolic syndrome (MetS) is a chronic disease caused by obesity, high blood pressure, high blood sugar, and dyslipidemia and may lead to cardiovascular disease or type 2 diabetes. Therefore, the detection and prevention of MetS at an early stage are imperative. Individuals can detect MetS early and manage it effectively if they can easily monitor their health status in their daily lives. In this study, a predictive model for MetS was developed utilizing solely noninvasive information, thereby facilitating its practical application in real-world scenarios. The model\u27s construction deliberately excluded three features requiring blood testing, specifically those for triglycerides, blood sugar, and HDL cholesterol. We used a large-scale Korean health examination dataset (n = 70, 370; the prevalence of MetS = 13.6%) to develop the predictive model. To obtain informative features, we developed three novel synthetic features from four basic information: waist circumference, systolic and diastolic blood pressure, and gender. We tested several classification algorithms and confirmed that the decision tree model is the most appropriate for the practical prediction of MetS. The proposed model achieved good performance, with an AUC of 0.889, a recall of 0.855, and a specificity of 0.773. It uses only four base features, which results in simplicity and easy interpretability of the model. In addition, we performed calibrations on the prediction probability and calibrated the model. Therefore, the proposed model can provide MetS diagnosis and risk prediction results. We also proposed a MetS risk map such that individuals could easily determine whether they had metabolic syndrome

On positive solutions of some pairs of differential equations, II

Background. Lung cancer (LC) is often diagnosed late when curative intervention is no longer viable. However, current referral guidelines (e.g. UK National Institute for Health and Care Excellence guidelines) for suspected LC are based on a weak evidence base. Aim. The purpose of this systematic review is to identify symptoms that are independently associated with LC and to identify the key methodological issues relating to symptomatic diagnosis research in LC. Methods. Medline, Ovid and Cumulative Index to Nursing and Allied Health Literature were searched for the period between 1946 and 2012 using the MeSH terms ‘lung cancer’ and ‘symptom*’. Quality of each paper was assessed using Scottish Intercollegiate Guidelines Network and Consolidated Criteria for Reporting Qualitative Research Checklists and checked by a second and third reviewer. Results. Evidence regarding the diagnostic values of most symptoms was inconclusive; haemoptysis was the only symptom consistently indicated as a predictor of LC. Generally, evidence was weakened by methodological issues such as the lack of standardized data collection (recording bias) and the lack of comparability of findings across the different studies that extend beyond the spectrum of disease. Qualitative studies indicated that patients with LC experienced symptoms months before diagnosis but did not interpret them as serious enough to seek health care. Therefore, early LC symptoms might be under-represented in primary care clinical notes. Conclusion. Current evidence is insufficient to suggest a symptom profile for LC across the disease stages, nor can it be concluded that classical LC symptoms are predictors of LC apart from, perhaps, haemoptysis. Prospective studies are now needed that systematically record symptoms and explore their predictive values for LC diagnosis

The p38 MAPK pathway is essential for skeletogenesis and bone homeostasis in mice

Nearly every extracellular ligand that has been found to play a role in regulating bone biology acts, at least in part, through MAPK pathways. Nevertheless, much remains to be learned about the contribution of MAPKs to osteoblast biology in vivo. Here we report that the p38 MAPK pathway is required for normal skeletogenesis in mice, as mice with deletion of any of the MAPK pathway member–encoding genes MAPK kinase 3 (Mkk3), Mkk6, p38a, or p38b displayed profoundly reduced bone mass secondary to defective osteoblast differentiation. Among the MAPK kinase kinase (MAP3K) family, we identified TGF-β–activated kinase 1 (TAK1; also known as MAP3K7) as the critical activator upstream of p38 in osteoblasts. Osteoblast-specific deletion of Tak1 resulted in clavicular hypoplasia and delayed fontanelle fusion, a phenotype similar to the cleidocranial dysplasia observed in humans haploinsufficient for the transcription factor runt-related transcription factor 2 (Runx2). Mechanistic analysis revealed that the TAK1–MKK3/6–p38 MAPK axis phosphorylated Runx2, promoting its association with the coactivator CREB-binding protein (CBP), which was required to regulate osteoblast genetic programs. These findings reveal an in vivo function for p38β and establish that MAPK signaling is essential for bone formation in vivo. These results also suggest that selective p38β agonists may represent attractive therapeutic agents to prevent bone loss associated with osteoporosis and aging

Defect-engineered graphene for bulk supercapacitors with high energy and power densities

The development of high-energy and high-power density supercapacitors (SCs) is critical for enabling next-generation energy storage applications. Nanocarbons are excellent SC electrode materials due to their economic viability, high-surface area, and high stability. Although nanocarbons have high theoretical surface area and hence high double layer capacitance, the net amount of energy stored in nanocarbon-SCs is much below theoretical limits due to two inherent bottlenecks: i) their low quantum capacitance and ii) limited ion-accessible surface area. Here, we demonstrate that defects in graphene could be effectively used to mitigate these bottlenecks by drastically increasing the quantum capacitance and opening new channels to facilitate ion diffusion in otherwise closed interlayer spaces. Our results support the emergence of a new energy paradigm in SCs with 250% enhancement in double layer capacitance beyond the theoretical limit. Furthermore, we demonstrate prototype defect engineered bulk SC devices with energy densities 500% higher than state-of-the-art commercial SCs without compromising the power density.Comment: 15 pages, 5 figures, and 8 supplemental figure