Evaluation of controlled hydroxychloroquine releasing performance from calcium-alginate beads

The aim of this study was to develop an effective controlled drug delivery system based on alginate beads for the treatment of autoimmune diseases such as Rheumatoid Arthritis (RA) and Systemic Lupus Erythematosus (SLE). The present study describes the drug delivery systems to control the effective uses of hydroxychloroquine (HCQ) by Ca-alginate beads. The characterization techniques were employed to evaluate the physicochemical properties as scanning electron microscopy (SEM), swelling test (S), hydrolytic degradation (weight loss, WL) and Fourier transform infrared-attenuated total reflection (FTIR-ATR). The release studies from alginate beads prepared in various drug dose were carried out in the aqueous solutions at different pH (5–8) and temperature (4-37oC). The approximately half-amount of HCQ in HCQ-AB3 was released in 12 h and about 84.38% was released within 8 days. Kinetic model, Korsmeyer-Peppas was applied to model the HCQ release kinetic of alginate beads, which corresponded to non-Fickian transport mechanism

Selective aptasensor for trinitrotoluene detection: Comparison of the detecting performances from liquid and vapor phases

In general, chromatographic and sensor analyses have been utilized for explosive detection. The main interest on those systems is to develop a method to selectively detect explosives at a single step as well as from vapor phase if possible. Moreover, on-site and real-time detection with portable systems is another challenge for the researchers. On the other hand, the detection of 2,4,6-trinitrotoluene (TNT) vapor at the crime scene, preferably before the explosion is highly demanded in order to prevent the negative effects of terrorism and to ensure the safety of the civilian population. In this study, initially, Quartz Crystal Microbalance (QCM) sensor was prepared for real-time monitoring of TNT in aqueous solution, through the attachment of TNT peptide aptamer on the gold surface of QCM sensor. Secondly, after providing optimum conditions, TNT detection was investigated even from vapor phase through the QCM aptasensor. According to results, the selectivity coefficient of QCM-based aptasensor was calculated as 6.78 for TNT in respect to DNT whereas that was calculated as 9.02 for TNT in respect to TNB. In addition, the evaluation of the reusability and storage stability emphasized that the sensor could be used repeatedly without significant reduction in dissipation (∆D) values. The linearity coefficient (R2) was found to be 0.9965. The limit of detection (LOD) and the limit of quantitation (LOQ) were determined as 0.0238 and 0.0739 nM, respectively. The studies demonstrated that the portable QCM sensor decorated with the aptamer selective for TNT molecules could be classified as a promising alternative, selective, cost-friendly, easy-to-prepare, ready-to-use, and applicable for on-site and real-time explosive measurements (even from vapor phase)

An intriguing future is approaching: Artificial intelligence meets molecularly imprinted polymers

Artificial intelligence (AI) is developing and expanding rapidly beyond the expectations of engineers who develop it as well. It is evaluated that it has/will have great potential not only in terms of software but also in terms of material science, data analysis, and decision-making. On the other hand; molecularly imprinted polymers (MIPs) still attract researchers’ attention with their superior features in terms of wide range of design options and application fields. In this review, the combination of AI and MIP applications and their potential contributions to the future were compiled. In this context, firstly, a brief introduction to MIPs were given in a combination with recent sensor design approaches. Subsequently, the subject of artificial intelligence, in other commonly used words, machine learning, was discussed while summarizing the commonly used algorithms. In the last section, pioneering studies involving the combination of AI and MIPs are highlighted. This review will be the first article compiling AI and MIPs as well as it is thought to be an important main resource for researchers and enthusiasts

Analgesia Nociception Index Monitoring in Management of Perioperative Analgesia in Total Knee Arthroplasty Surgeries with Femoral Nerve Block

Background and Objectives: The aim of our study is to determine the effects of analgesia nociception index (ANI) monitoring on intraoperative opioid consumption, postoperative analgesia, and the recovery unit length of stay in patients with a preoperative femoral nerve block (FNB) undergoing total knee arthroplasty (TKA) surgery under general anesthesia. Materials and Methods: Seventy-four patients in the American Society of Anesthesiologists Physical Status (ASA-PS) I-III scheduled for TKA under general anesthesia were included in this study. After FNB, the patients were divided into two groups (control group (n = 35)–ANI group (n = 35)). After standard anesthesia induction in both groups, maintenance was conducted using sevoflurane and remifentanil infusion with a bispectral index (BIS) between 40 and 60. In the control group, the intraoperative remifentanil infusion dose was adjusted using conventional methods, and in the ANI group, the dose was adjusted using ANI values of 50–70. The duration of operation, duration of surgery, extubation time, tourniquet duration and pressure, and the amount of remifentanil consumed intraoperatively were recorded. Results: Intraoperative remifentanil consumption was lower in the ANI group compared to the control group (p = 0.001). The time to reach a Modified Aldrete Scale score (MAS) ≥ 9 was shorter in the ANI group (p < 0.001). NRS scores in the recovery unit and 4, 8, 12, and 24 h postoperatively were lower in the ANI group compared to the control group (p = 0.006, p < 0.05). There was a weak significant inverse relationship between the last ANI values measured before extubation and NRS scores in the postoperative recovery unit (r: −0.070–0.079, p: 0.698–0.661). No difference was observed between the groups in other data. Conclusions: In patients undergoing TKA with FNB under general anesthesia, ANI monitoring decreased the amount of opioids consumed intraoperatively and postoperative pain scores and shortened the length of stay in the recovery unit. We suggest that ANI monitoring in intraoperative analgesia management may be helpful in determining the dose of opioid needed by the patient and individualized analgesia management