Isolation,purification and biochemical characterization of Conopomorpha cramerella farnesol dehydrogenase

In Southeast Asia, Conopomorpha cramerella (Snellen) which is commonly known as the cocoa pod borer (CPB) moth has been identified as the most detrimental pest of Theobroma cacao L. Apart from the various side effects on human health and non-target organisms, heavily relying on synthetic pyrethroid insecticides to control CPB infestations also increases the environmental contamination risks. Thus, developing biorational insecticides that minimally affect the non-target organism and environment by targeting the insect growth regulation process is needed to manage the pest population. In insects, juvenile hormones (JH) regulate critical biological events, especially metamorphosis, development and reproduction. Since the physiological roles of JH III vary among different organisms, the biochemical properties, especially substrate specificity and analogue inhibition, may also be different. Therefore, studies on the JH III biosynthetic pathway enzymes in both plants and insects are beneficial to discover more effective analogues. Bioinformatic analysis and biochemical characterization of a NADP⁺-dependent farnesol dehydrogenase, an intermediate enzyme of the JH III pathway, from C. cramerella (CcFolDH), were described in this study. In addition, the farnesol analogues that may act as a potent analogue inhibitor for CcFolDH ware determined using in vitro enzymatic study. The phylogenetic analysis indicated that CcFolDH shared a close phylogenetic relationship to the honeybee's short-chain dehydrogenase/reductase. The 27 kDa CcFolDH has an NADP(H) binding domain with a typical Rossmann fold and is likely a homotetrameric protein in the solution. The enzyme had a greater preference for substrate trans, trans-farnesol and coenzyme NADP⁺. In terms of analogue inhibitor inhibition, hexahydroxyfarnesyl acetone showed the highest inhibition (the lowest K_i) compared to other farnesol analogues. Thus, hexahydroxyfarnesyl acetone would serve as the most potent active ingredient for future biorational pesticide management for C. cramerella infestation. Based on the bioinformatic analyses and biochemical characterizations conducted in this research, we proposed that rCcFolDH differs slightly from other reported farnesol dehydrogenases in terms of molecular weight, substrate preference, coenzymes utilization and analogue inhibitors selection.     

Knee arthroplasty: post-operative care,rehab-ilitation and follow-up

Approximately 100,000 primary knee replacements are performed in the UK each year. There has been increasing focus on enhanced recovery after surgery (ERAS) programmes aimed at reducing complications and improving patient functional outcomes, with a consequent reduction in length of stay. Management of patients expectations preoperatively have been shown to be very important in providing better improvement of pain and function postoperatively. As part of ERAS programmes, management of postoperative pain control is essential to help promote early mobilization, compliance in rehabilitation with physiotherapists and minimizing the risk of venous thromboembolism (VTE). We focus this review on VTE prophylaxis, perioperative management of anticoagulation, rehabilitation and follow-up. In conclusion, success in ERAS programmes is heavily reliant on strong collaboration within the multidisciplinary team consisting of orthopaedic surgeons, anaesthetists, physiotherapists, nurses and occupational therapists. All of the above provide simultaneous patient-centred care.     

Deep learning for label-free nuclei detection from implicit phase information of mesenchymal stem cells

Monitoring of adherent cells in culture is routinely performed in biological and clinical laboratories, and it is crucial for large-scale manufacturing of cells needed in cell-based clinical trials and therapies. However, the lack of reliable and easily implementable label-free techniques makes this task laborious and prone to human subjectivity. We present a deep-learning-based processing pipeline that locates and characterizes mesenchymal stem cell nuclei from a few bright-field images captured at various levels of defocus under collimated illumination. Our approach builds upon phase-from-defocus methods in the optics literature and is easily applicable without the need for special microscopy hardware, for example, phase contrast objectives, or explicit phase reconstruction methods that rely on potentially bias-inducing priors. Experiments show that this label-free method can produce accurate cell counts as well as nuclei shape statistics without the need for invasive staining or ultraviolet radiation. We also provide detailed information on how the deep-learning pipeline was designed, built and validated, making it straightforward to adapt our methodology to different types of cells. Finally, we discuss the limitations of our technique and potential future avenues for exploration.