KVarPredDB: a database for predicting pathogenicity of missense sequence variants of keratin genes associated with genodermatoses

Spoilage Monitoring and Early Warning for Apples in Storage Using Gas Sensors and Chemometrics

In the process of storage and cold chain logistics, apples are prone to physical bumps or microbial infection, which easily leads to spoilage in the micro-environment, resulting in widespread infection and serious post-harvest economic losses. Thus, development of methods for monitoring apple spoilage and providing early warning of spoilage has become the focus for post-harvest loss reduction. Thus, in this study, a spoilage monitoring and early warning system was developed by measuring volatile component production during apple spoilage combined with chemometric analysis. An apple spoilage monitoring prototype was designed to include a gas monitoring array capable of measuring volatile organic compounds, such as CO2, O2 and C2H4, integrated with the temperature and humidity sensor. The sensor information from a simulated apple warehouse was obtained by the prototype, and a multi-factor fusion early warning model of apple spoilage was established based on various modeling methods. Simulated annealing-partial least squares (SA-PLS) was the optimal model with the correlation coefficient of prediction set (Rp) and root mean square error of prediction (RMSEP) of 0.936 and 0.828, respectively. The real-time evaluation of the spoilage was successfully obtained by loading an optimal monitoring and warning model into the microcontroller. An apple remote monitoring and early warning platform was built to visualize the apple warehouse's sensors data and spoilage level. The results demonstrated that the prototype based on characteristic gas sensor array could effectively monitor and warn apple spoilage.

Medical Information Mining-Based Visual Artificial Intelligence Emergency Nursing Management System

This study aims to design a set of the visual artificial intelligence system based on medical information mining for hospital emergency care management. A visual artificial intelligence emergency first aid nursing management system is designed by analyzing the needs of the emergency first aid nursing management system. The results show that system personnel allocation, comparative management, record management, query management analysis, basic setup analysis, nursing management basis, and nonfunctional requirements all need to be optimized for the emergency first aid management system. In this study, the comparative management module, log management module, and the query management module are designed, and the emergency first aid management system of different APP terminal functions in different modules is described in detail. The nursing document query business is tested, and the corresponding time of query of nursing assessment sheet, nurse shift record, nurse record, and physical sign observation sheet is 375.50 ms, 351.48 ms, 336.36 ms, and 245.57 ms, respectively. It shows that the visual artificial intelligence emergency nursing management system based on medical information mining can provide convenience for clinical work to a large extent and has potential application value in hospital emergency nursing work.

Genotype-structurotype-phenotype correlations in pachyonychia congenita patients

Pachyonychia congenita (PC) is a genetic disorder of keratin that presents with nail dystrophy, painful palmoplantar keratoderma, and other clinical manifestations. We investigated genotype-structurotype-phenotype correlations seen with mutations in keratin genes (KRT6A, KRT6B, KRT6C, KRT16, KRT17) and utilized protein structure modeling of high frequency mutations to examine the functional importance of keratin structural domains in PC pathogenesis. Participants of the International PC Research Registry underwent genetic testing and completed a standardized survey on their symptoms. Our results support prior reports associating oral leukokeratosis with KRT6A mutations, and cutaneous cysts, follicular hyperkeratosis, and natal teeth with KRT17 mutations. Painful keratoderma was prominent with KRT6A and KRT16 mutations. Nail involvement was most common in KRT6A and least common in KRT6C patients. Across keratin subtypes, patients with coil 2B mutations had greatest impairment in ambulation, and patients with coil 1A mutations reported more emotional issues. Molecular modeling demonstrated that hotspot missense mutations in PC largely disrupted hydrophobic interactions or surface charge. The former may destabilize keratin dimers/tetramers, while the latter likely interferes with higher-order keratin filament formation. Understanding pathologic alterations in keratin structure improves our knowledge of how PC genotype correlates with clinical phenotype, advancing insight into disease pathogenesis and therapeutic development.