Mini Review: Impedance Measurement in Neuroscience and Its Prospective Application in the Field of Surgical Neurooncology

Impedance measurement of human tissue can be performed either in vivo or ex vivo. The majority of the in-vivo approaches are non-invasive, and few are invasive. To date, there is no gold standard for impedance measurement of intracranial tissue. In addition, most of the techniques addressing this topic are still experimental and have not found their way into clinical practice. This review covers available impedance measurement approaches in the neuroscience in general and specifically addresses recent advances made in the application of impedance measurement in the field of surgical neurooncology. It will provide an understandable picture on impedance measurement and give an overview of limitations that currently hinders clinical application and require future technical and conceptual solutions.

Assessing SARS-CoV-2 Neutralizing Antibodies after BNT162b2 Vaccination and Their Correlation with SARS-CoV-2 IgG Anti-S1, Anti-RBD and Anti-S2 Serological Titers

The humoral response through neutralizing antibodies (NAbs) is a key component of the immune response to COVID-19. However, the plaque reduction neutralization test (PRNT), the gold standard for determining NAbs, is technically demanding, time-consuming and requires BSL-3 conditions. Correlating the NAbs and total antibodies levels, assessed by generalized and automated serological tests, is crucial. Through a commercial surrogate virus neutralization test (sVNT), we aimed to evaluate the production of SARS-CoV-2 NAbs in a set of vaccinated healthcare workers and to correlate these NAbs with the SARS-CoV-2 IgG anti-S1, anti-RBD and anti-S2 serological titers. We found that 6 months after vaccination, only 74% maintain NAbs for the Wuhan strain/UK variant (V1) and 47% maintain NAbs for the South African and Brazil variants (V2). Through Spearman’s correlation, we found the following correlations between the percentage of inhibition of NAbs and the SARS-CoV-2 IgG II Quant (Abbott Laboratories, Chicago, IL, USA) and BioPlex 2200 SARS-CoV-2 IgG Panel (Bio-Rad, Hercules, CA, USA) immunoassays: rho = 0.87 (V1) and rho = 0.73 (V2) for anti-S1 assessed by Abbott assay; rho = 0.77 (V1) and rho = 0.72 (V2) for anti-S1, rho = 0.88 (V1) and rho = 0.82 (V2) for anti-RBD, and rho = 0.68 (V1) and rho = 0.60 (V2) for anti-S2 assessed by BioPlex assay (p < 0.001 for all). In conclusion, we found a strong correlation between this fast, user-friendly, mobile and bio-safe sVNT and the serological immunoassays.

Use of Circulating Tumour DNA (ctDNA) for Measurement of Therapy Predictive Biomarkers in Patients with Cancer

Biomarkers that predict likely response or resistance to specific therapies are critical in personalising treatment for cancer patients. Such biomarkers are now available for an increasing number of anti-cancer therapies, especially targeted therapy and immunotherapy. The gold-standard method for determining predictive biomarkers requires tumour tissue. Obtaining tissue, however, is not always possible and even if possible, the amount or quality of tissue obtained may be inadequate for biomarker analysis. Tumour DNA, however, can be released into the bloodstream, giving rise to what is referred to as circulating tumour DNA (ctDNA). In contrast to tissue, blood can be obtained from effectively all patients in a minimally invasive and safe manner. Other advantages of blood over tissue for biomarker testing include a shorter turn-around time and an ability to perform serial measurements. Furthermore, blood should provide a more complete profile of mutations present in heterogeneous tumours than a single-needle tissue biopsy. A limitation of blood vis-à-vis tissue, however, is lower sensitivity and, thus, the possibility of missing an actionable mutation. Despite this limitation, blood-based predictive biomarkers, such as mutant EGFR for predicting response to EGFR tyrosine kinase inhibitors in advanced non-small-cell lung cancer and mutant PIK3CA for predicting response to alpelisib in combination with fulvestrant in advanced breast cancer, may be used when tissue is unavailable. Although tissue remains the gold standard for detecting predictive biomarkers, it is likely that several further blood-based assays will soon be validated and used when tissue is unavailable or unsuitable for analysis.

Factors Associated With the Accuracy of Depth Gauge Measurements

Background: It is important to select appropriate screws in orthopedic surgeries, as excessively long or too short a screw may results failure of the surgeries. This study explored factors that affect the accuracy of measurements in terms of the experience of the surgeons, passage of drilled holes and different depth gauges.Methods: Holes were drilled into fresh porcine femurs with skin in three passages, straight drilling through the metaphysis, straight drilling through the diaphysis, and angled drilling through the diaphysis. Surgeons with different surgical experiences measured the holes with the same depth gauge and using a vernier caliper as gold standard. The length of selected screws, and the time each surgeon spent were recorded. The measurement accuracy was compared based on the experiences of the surgeons and the passage of drilled holes. Further, parameters of depth gauges and 12-mm cortical bone screws from five different manufacturers were measured.Results: A total of 13 surgeons participated in 585 measurements in this study, and each surgeon completed 45 measurements. For the surgeons in the senior, intermediate, and junior groups, the average time spent in measurements was 689, 833, and 785 s with an accuracy of 57.0, 42.2, and 31.5%, respectively. The accuracy and measurement efficiency were significantly different among the groups of surgeons (P &lt; 0.001). The accuracy of measurements was 45.1% for straight metaphyseal drilling, 43.6% for straight diaphyseal drilling, and 33.3% for angled diaphyseal drilling (P = 0.036). Parameters of depth gauges and screws varied among different manufacturers.Conclusion: Both observer factor and objective factors could affect the accuracy of depth gauge measurement. Increased surgeon's experience was associated with improvements in the accuracy rate and measurement efficiency of drilled holes based on the depth gauge. The accuracy rate varied with hole passages, being the lowest for angled drilled holes.

Quantitative Ultrasound of Phalanx in Primary and Secondary Osteoporosis: Mini-review and Practical Experience

Objective: Dual x-ray absorptiometry (DXA) is gold standard of bone densitometry, but quantitative ultrasound (QUS) of bone is less expensive and portable. This study was designed to assess its usefulness in secondary osteoporosis diagnosis. Materials and Methods: There were 200 secondary osteoporosis cases (rheumatoid arthritis, hemodialysis, kidney transplant patients, and levothyroxine users) and of those, their phalanx QUS results were compared with normal controls. Also, the QUS and DXA results were compared to find any correlation of these methods for diagnosing osteoporosis. Results: There was not significantly different results compared with normal controls, except for those of hemodialysis patients ( P = .00). Also, the comparison of QUS with DXA results showed no significant correlation except in hemodialysis patients, in both spinal and femoral regions ( P = .023 and .21, respectively), as well as the levothyroxine group’s spinal region ( P = .005). Conclusion: These results suggest that QUS of phalanx may be useful in screening secondary osteoporosis but for establishment of diagnosis, DXA measurements are still needed.

Visualizing the native cellular organization by coupling cryofixation with expansion microscopy (Cryo-ExM)

Cryofixation has proven to be the gold standard for efficient preservation of native cell ultrastructure compared to chemical fixation, but this approach is not widely used in fluorescence microscopy owing to implementation challenges. Here, we develop Cryo-ExM, a method that preserves native cellular organization by coupling cryofixation with expansion microscopy. This method bypasses artifacts associated with chemical fixation and its simplicity will contribute to its widespread use in super-resolution microscopy.

Computational Prediction of Bacteriophage Host Ranges

Increased antibiotic resistance has prompted the development of bacteriophage agents for a multitude of applications in agriculture, biotechnology, and medicine. A key factor in the choice of agents for these applications is the host range of a bacteriophage, i.e., the bacterial genera, species, and strains a bacteriophage is able to infect. Although experimental explorations of host ranges remain the gold standard, such investigations are inherently limited to a small number of viruses and bacteria amendable to cultivation. Here, we review recently developed bioinformatic tools that offer a promising and high-throughput alternative by computationally predicting the putative host ranges of bacteriophages, including those challenging to grow in laboratory environments.

Machine Learning-Based Enzyme Engineering of PETase for Improved Efficiency in Degrading Non-Biodegradable Plastic

Globally, nearly a million plastic bottles are produced every minute (1). These non-biodegradable plastic products are composed of Polyethylene terephthalate (PET). In 2016, researchers discovered PETase, an enzyme from the bacteria Ideonella sakaiensis which breaks down PET and nonbiodegradable plastic. However, PETase has low efficiency at high temperatures. In this project, we optimized the rate of PET degradation by PETase by designing new mutant enzymes which could break down PET much faster than PETase, which is currently the gold standard. We used machine learning (ML) guided directed evolution to modify the PETase enzyme to have a higher optimal temperature (Topt), which would allow the enzyme to degrade PET more efficiently. First, we trained three machine learning models to predict Topt with high performance, including Logistic Regression, Linear Regression and Random Forest. We then used Random Forest to perform ML-guided directed evolution. Our algorithm generated hundreds of mutants of PETase and screened them using Random Forest to select mutants with the highest Topt, and then used the top mutants as the enzyme being mutated. After 1000 iterations, we produced a new mutant of PETase with Topt of 71.38℃. We also produced a new mutant enzyme after 29 iterations with Topt of 61.3℃. To ensure these mutant enzymes would remain stable, we predicted their melting temperatures using an external predictor and found the 29-iteration mutant had improved thermostability over PETase. Our research is significant because using our approach and algorithm, scientists can optimize additional enzymes for improved efficiency.