Managing diabetes during pregnancy is challenging, given the significant risk it poses for both maternal and foetal health outcomes. While traditional methods involve capillary self-monitoring of blood glucose level monitoring and periodic HbA1c tests, the advent of continuous glucose monitoring (CGM) systems has revolutionized the approach. These devices offer a safe and reliable means of tracking glucose levels in real-time, benefiting both women with diabetes during pregnancy and the healthcare providers. Moreover, CGM systems have shown a low rate of side effects and high feasibility when used in pregnancies complicated by diabetes, especially when paired with continuous subcutaneous insulin infusion pump as hybrid closed loop device. Such a combined approach has been demonstrated to improve overall blood sugar control, lessen the occurrence of preeclampsia and neonatal hypoglycaemia, and minimize the duration of neonatal intensive care unit stays. This paper aims to offer a comprehensive evaluation of CGM metrics specifically tailored for pregnancies impacted by type 1 diabetes mellitus.

The primary goal of glucose sensing at the point of care is to identify glucose concentrations within the diabetes range. However, lower glucose levels also pose a severe health risk. In this paper, we propose quick, simple, and reliable glucose sensors based on the absorption and photoluminescence spectra of chitosan-capped ZnS-doped Mn nanomaterials in the range of 0.125 to 0.636 mM glucose corresponding to 2.3 mg/dL to 11.4 mg/dL. The detection limit was 0.125 mM (or 2.3 mg/dL), much lower than the hypoglycemia level of 70 mg/dL (or 3.9 mM). Chitosan-capped ZnS-doped Mn nanomaterials retain their optical properties while improving sensor stability. This study reports for the first time how the sensors' efficacy was affected by chitosan content from 0.75 to 1.5 wt.%. The results showed that 1 %wt chitosan-capped ZnS-doped Mn is the most-sensitive, -selective, and -stable material. We also put the biosensor through its paces with glucose in phosphate-buffered saline. In the same range of 0.125 to 0.636 mM, the sensors-based chitosan-coated ZnS-doped Mn had a better sensitivity than the working water environment.

In order to experimentally confirm the dependence of the bioelectrical impedance on the flow rate of charged aerosol particles and the composition of bronchial secretions in small airways, the electrical impedance of an aerosol of 0.9% NaCl solution in polyethylene tubes of various diameters was studied. The electrical impedance was also measured in cylindrical chambers of various diameters and volumes filled with a 0.9% NaCl solution or a gelatin solution. The studies were carried out at frequencies of alternating electric current from 20 Hz to 150 kHz. It was shown that the electrical current is not recorded in the absence of a flow of aerosol particles, and the impedance decreases with an increase in the flow velocity. The impedance modulus and the phase angle of the electrical impedance has an expressed dependence on the composition of the conductive medium, the impedance modulus increases in the gelatin solution medium, with a decrease in the diameter of the electric current conductor, and decreases with an increase in the frequency of the probing alternating current. The obtained results confirmed the hypothesis about the influence of the speed of salty aerosol particles flow and the composition of bronchial secretions on the results of measuring electrical impedance.

Innovations in computer technology and implant design have paved the way for the development of smart instruments and intelligent implants in trauma and orthopaedics to improve patient-related functional outcomes. Sensor technology uses embedded devices that detect physical, chemical and biological signals and provide a way for these signals to be measured and recorded. Sensor technology applications have been introduced in various fields of medicine in the diagnosis, treatment and monitoring of diseases. Intelligent 'Smart' implants are devices that can provide diagnostic capabilities along with therapeutic benefits. In trauma and orthopaedics, applications of sensors is increasing because of the advances in microchip technologies for implant devices and research designs. It offers real-time monitoring from the signals transmitted by the embedded sensors and thus provides early management solutions. Smart orthopaedic implants have applications in total knee arthroplasty, hip arthroplasty, spine surgery, fracture healing, early detection of infection and implant loosening. Here we have explored the role of Smart sensor implant technology in total knee arthroplasty. Smart sensor assisted can be used intraoperatively to provide objective assessment of ligament and soft tissue balancing whilst maintaining the sagittal and coronal alignment to achieve desired kinematic targets following total knee arthroplasty. It can also provide post-implantation data to monitor implant performance in natural conditions and patient's clinical recovery during rehabilitation. The use of Smart Sensor implant technology in total knee arthroplasty appears to provide superior patient satisfaction rates and improved functional outcomes.

The microbial fuel cell (MFC) is a promising environmental biotechnology that has been proposed mainly for power production and wastewater treatment. Though small power output constrains its application for directly operating most electrical devices, great progress in its chemical, electrochemical, and microbiological aspects has expanded the applications of MFCs into other areas such as the generation of chemicals (e.g., formate or methane), bioremediation of contaminated soils, water desalination, and biosensors. In recent decades, MFC-based biosensors have drawn increasing attention because of their simplicity and sustainability, with applications ranging from the monitoring of water quality (e.g., biochemical oxygen demand (BOD), toxicants) to the detection of air quality (e.g., carbon monoxide, formaldehyde). In this review, we summarize the status quo of MFC-based biosensors, putting emphasis on BOD and toxicity detection. Furthermore, this review covers other applications of MFC-based biosensors, such as DO and microbial activity. Further, challenges and prospects of MFC-based biosensors are briefly discussed.

Hospitalised patients, who suffer cardiac arrest and require unanticipated intensive care unit (ICU) admission or die, often exhibit premonitory abnormalities in vital signs. Sometimes, the deterioration is well documented, though there is little discernable evidence of intervention. In other cases, monitoring and recording of vital signs is infrequent or incomplete. Healthcare providers have introduced "track and trigger" systems to allow early identification of patients with physiological abnormalities, and rapid response teams to facilitate rapid and appropriate management. However, even when "track and trigger" systems are used, the recording of vital signs, patient chart completion and team activation remain sub-optimal. We have developed a system for collecting routine vital signs data at the bedside using standard personal digital assistants (PDA). The PDAs act as "thin clients" linked by a wireless local area network (W-LAN) to the hospital's intranet system, where raw and derived data are integrated with other patient information, e.g., name, hospital number, laboratory results. It is possible for raw physiology data, early warning scores (EWS), vital signs charts and oxygen therapy records to be made instantaneously available to any member of the hospital healthcare team via the W-LAN or hospital intranet. Early and direct contact with members of the patient's primary clinical team or rapid response team can be made through an automated alerting system, triggered by the EWS data. The ability to capture physiological data at the bedside, and to make these available to anyone with appropriate access rights at any time and in any place, should provide previously unattainable, clinical and administrative benefits. Analysis of the raw physiological data and patient outcomes will also make it possible to validate existing and future "track and trigger" systems.