Biosensors & Bioelectronics

With the recognition of wearable devices, along with the development of clouds and cloudlet technology, there has been increasing got to give higher treatment. The process chain of medical knowledge chiefly includes knowledge assortment, data storage and knowledge sharing, etc. ancient tending system often needs the delivery of medical knowledge to the cloud, which involves users sensitive info and causes communication energy consumption much, medical knowledge sharing could be a vital and difficult issue therefore during this paper, we tend to build up a completely unique healthcare system by utilizing the exibility of cloudlet. The functions of cloudlet embody privacy protection, knowledge sharing and intrusion detection within the stage of information assortment, we RST utilize range Theory analysis Unit (NTRU) methodology to encrypt users body knowledge collected by wearable devices. Firstly, those data are going to be transmitted to close cloudlet in AN energy efficient fashion. Secondly, we tend to gift a replacement trust model to assist users to select trustable partners UN agency need to share hold on knowledge in the cloudlet. The trust model conjointly helps similar patients to communicate with one another concerning their diseases. Thirdly, we divide users medical knowledge hold on in remote cloud of hospital into 3 elements, and provides them correct protection. Finally, in order to guard the tending system from malicious attacks, we develop a completely unique cooperative intrusion detection system (IDS) method supported cloudlet mesh, which may effectively forestall the remote tending massive knowledge cloud from attacks. Our experiments demonstrate the effectiveness of the projected theme. Index terms privacy protection, knowledge sharing, cooperative intrusion detection system (IDS), healthcare.

A real-time detection and monitoring (RTDM) of microbial contamination on solid surfaces is mandatory in a range of security, safety and bio-medical applications where surfaces are exposed to accidental, natural or intentional microbial contamination. This work presents a new device, the BC-Sense, which allows a rapid and user-friendly RTDM of microbial contamination on various surfaces while assessing the decontamination kinetics and degree of cleanliness. The BC-Sense LIDAR (Light Detection and Ranging) device uses the Laser-Induced Fluorescence (LIF) method based on dual wavelength sensing with multispectral pattern recognition system to rapidly detect microbial contamination on a solid surface. Microbial simulants (bacteria, bacterial spores, fungal conidia and virus) were spread at varying concentrations on a panel of solid surfaces which were assessed by BC-Sense. The spectra of dead and living E. coli showed differences at various sensing wavelengths. The limit of detection (LoD) of E. coli and MS2 virus was 2.9 × 10⁴ and 9.5 × 10⁴ PFU and CFU/cm², respectively. Random samples (n=200) tested against a training dataset (n=800) were optimally discriminated for contamination versus background with a threshold of predicted response (PR) >0.55 and <0.4, respectively. Decontamination kinetics on copper surface showed a complete disappearance of fluorescence in 1 min with MS2 versus >10 min with spores and E. coli.