Material Separation from Polyester/Cotton Blended Fabrics Using Hydrothermal Treatment.

The production of textile products is increasing annually, and most of them are disposed of after use without recycling. One of the reasons for the low recycling percentage of discarded textile products is the difficulty of recycling as a single material as these products are produced from a combination of two or more materials. Therefore, a technology to separate materials is necessary to improve the recycling percentage of textile products and to build a sustainable recycling industry. The aim of this study was to separate the most common combination of materials, such as cotton/polyester, in an environmentally friendly technique using hydrothermal treatment with only water. Herein, the optimal treatment conditions for blended fabrics in a high-pressure reactor were studied. Moreover, cotton could be separated by treating the fabrics at 220 to 230 °C for 10 min while maintaining the shape of the fabrics. Additionally, polyester showed a melting point, confirming that polyester could be separated without decomposition into monomers, unlike common chemical recycling. The strength of the separated cotton and the molecular weight of the polyester were evaluated, and a kinetic analysis of the changes due to the treatment was conducted. The activation energy obtained from the Arrhenius plot was 111.8 kJ/mol for PET, which was smaller than 142.6 kJ/mol for cotton. This indicates that the decrease in the molecular weight of PET is more likely to occur than the change in the strength of cotton, suggesting the possibility of separating the materials from the kinetic analysis.

Myocardial infarction in type 2 diabetes using sodium-glucose co-transporter-2 inhibitors, dipeptidyl peptidase-4 inhibitors or glucagon-like peptide-1 receptor agonists: proportional hazards analysis by deep neural network based machine learning.

Aims: Some hypoglycemic therapies are associated with lower risk of cardiovascular outcomes. We investigated the incidence of cardiovascular disease among patients with type 2 diabetes using antidiabetic drugs from three classes, which were sodium-glucose co-transporter-2 inhibitors (SGLT-2is), glucagon-like peptide-1 receptor agonists (GLP-1RAs) and dipeptidyl peptidase-4 inhibitors (DPP-4is).Materials and methods: We compared the risk of myocardial infarction (MI) among these drugs and developed a machine learning model for predicting MI in patients without prior heart disease. We analyzed US health plan data for patients without prior MI or insulin therapy who were aged ≥40 years at initial prescription and had not received oral antidiabetic drugs for ≥6 months previously. After developing a machine learning model to predict MI, proportional hazards analysis of MI incidence was conducted using the risk obtained with this model and the drug classes as explanatory variables.Results: We analyzed 199,116 patients (mean age: years), comprising 110,278 (58.6) prescribed DPP-4is, 43,538 (55.1) prescribed GLP-1RAs and 45,300 (55.3) prescribed SGLT-2is. Receiver operating characteristics analysis showed higher precision of machine learning over logistic regression analysis. Proportional hazards analysis by machine learning revealed a significantly lower risk of MI with SGLT-2is or GLP-1RAs than DPP-4is (hazard ratio: 0.81, 95% confidence interval: 0.72-0.91, p = .0004 vs. 0.63, 0.56-0.72, p < .0001). MI risk was also significantly lower with GLP-1RAs than SGLT-2is (0.77, 0.66-0.90, p = .001).Limitations: All patients analyzed were covered by US commercial health plans, so information on patients aged ≥65 years was limited and the socioeconomic background may have been biased. Also, the observation period differed among the three classes of drugs due to differing release dates.Conclusions: Machine learning analysis suggested the risk of MI was 37% lower for type 2 diabetes patients without prior MI using GLP-1RAs versus DPP-4is, while the risk was 19% lower for SGLT-2is versus DPP-4is.

Sequence specific interstrand photocrosslinking for effective SNP typing.

We describe a simple and inexpensive SNP typing method by using sequence specific interstrand photocrosslinking via p-carbamoylvinyl phenol nucleosides. Interstrand photocrosslinking showed a high degree of single nucleotide specificity as high as 10(3)-fold and more, and can be used in the diagnostic detection of DNA sequences.

Degenerative osteoarthrosis of tarsometatarsal joints in hallux valgus: a radiographic study.

To clarify the pathogenesis of degenerative osteoarthrosis of the tarsometatarsal joints in hallux valgus, we evaluated dorsoplantar and lateral radiographs during weight-bearing in 16 patients (25 feet) with hallux valgus accompanied by degenerative osteoarthrosis of the tarsometatarsal joints and 25 controls (25 feet) with hallux valgus alone. The proximal second metatarsal articular angle (a parameter we devised), the hallux valgus angle, intermetatarsal angle, metatarsal length, sesamoid displacement, calcaneal pitch, and foot length were measured and then evaluation using a mapping system was performed. There were no significant differences in the hallux valgus angle, intermetatarsal angle, sesamoid displacement, calcaneal pitch, or foot length. In the presence of degenerative osteoarthrosis of the tarsometatarsal joints, the second, third, and fourth metatarsals were long, and a large inclination of the proximal articular surface of the second metatarsal and adduction of the first to fourth metatarsals were observed. These findings appeared to be involved in the development of this disorder.