Methylene Blue in Septic Shock: A Systematic Review and Meta-Analysis.

OBJECTIVES: Although clinicians may use methylene blue (MB) in refractory septic shock, the effect of MB on patient-important outcomes remains uncertain. We conducted a systematic review and meta-analysis to investigate the benefits and harms of MB administration in patients with septic shock. DATA SOURCES: We searched six databases (including PubMed, Embase, and Medline) from inception to January 10, 2024. STUDY SELECTION: We included randomized clinical trials (RCTs) of critically ill adults comparing MB with placebo or usual care without MB administration. DATA EXTRACTION: Two reviewers performed screening, full-text review, and data extraction. We pooled data using a random-effects model, assessed the risk of bias using the modified Cochrane tool, and used Grading of Recommendations Assessment, Development, and Evaluation to rate certainty of effect estimates. DATA SYNTHESIS: We included six RCTs (302 patients). Compared with placebo or no MB administration, MB may reduce short-term mortality (RR [risk ratio] 0.66 [95% CI, 0.47-0.94], low certainty) and hospital length of stay (mean difference [MD] -2.1 d [95% CI, -1.4 to -2.8], low certainty). MB may also reduce duration of vasopressors (MD -31.1 hr [95% CI, -16.5 to -45.6], low certainty), and increase mean arterial pressure at 6 hours (MD 10.2 mm Hg [95% CI, 6.1-14.2], low certainty) compared with no MB administration. The effect of MB on serum methemoglobin concentration was uncertain (MD 0.9% [95% CI, -0.2% to 2.0%], very low certainty). We did not find any differences in adverse events. CONCLUSIONS: Among critically ill adults with septic shock, based on low-certainty evidence, MB may reduce short-term mortality, duration of vasopressors, and hospital length of stay, with no evidence of increased adverse events. Rigorous randomized trials evaluating the efficacy of MB in septic shock are needed. REGISTRATION: Center for Open Science (https://osf.io/hpy4j).

Impact of Low-Level Laser Therapy on Orthodontic Tooth Movement and Various Cytokines in Gingival Crevicular Fluid: A Split-Mouth Randomized Study.

BACKGROUND AND OBJECTIVES: A few investigations have detailed the influence of low-level laser therapy (LLLT) on orthodontic tooth movement (OTM), with varying results. The objectives of this study were twofold: to assess the impact of LLLT on OTM and various cytokine levels in gingival crevicular fluid and to contrast the pain levels experienced by patients receiving orthodontic treatment with and without LLLT. MATERIALS AND METHODS: This split-mouth randomized controlled prospective study comprised 40 patients with an average age of 19.7±2.4 years with Angle Class I malocclusion combined with bimaxillary protrusion who were advised for extraction of the maxillary first premolar and bilateral canine distalization. The control-side canine was distalized solely by the coil spring. On the test arm, a low-level gallium-aluminum-arsenide semiconductor diode laser operating at 980 nm and 100 mW with a continuous-wave energy of 8 J/cm2 was used. The canine distalization on either side was measured with a digital caliper following the first premolar extraction (TO), one month after treatment (TOTM1), two months later (TOTM2), and three months later (TOTM3). The gingival index and the level of various cytokines were determined by an enzyme-linked immunosorbent assay at the beginning of the study, on the third and seventh days, and at four, eight, and 12 weeks following the canine distalization. The intra-group and inter-group comparisons were carried out using one-way analysis of variance (ANOVA) and t-tests, respectively, at a 5% significance level. RESULTS: The results show a highly statistically significant difference in the extent of canine distalization in the test group (TOTM1=2.92±0.44; TOTM2=1.04±0.1; TOTM3​=0.62±0.21 mm) in contrast to the control group (TOTM1=3.23±0.8; TOTM2=2.65±0.2; TOTM3​​​​=2.11±0.24 mm) (p<0.01). After three months of canine distalization, the laser and control group had 34 and 27 patients with mild gingivitis, respectively. Interleukin-1ß and interleukin-6 concentrations surged with values of 0.74±0.13 and 0.049±0.001 pg/g at seven days following treatment in the laser group, respectively. The difference in tumor necrosis factor concentration between the groups was shown to be highly statistically significant in all treatment phases (p<0.001). The differences in the epidermal growth factor and microglobulin levels were found to be statistically significant within both groups from T0 to T5. The average visual analog scale (VAS) scores at several subsequent evaluations of the laser and control groups were found to be highly statistically significant. CONCLUSION: The findings imply that when the periodontal microenvironment is stimulated by orthodontic force, several paramount cytokines are released, triggering an ordered sequence of biological processes that appear to expedite OTM with reduced associated pain.

Terrain Characterization via Machine vs. Deep Learning Using Remote Sensing.

Terrain traversability is critical for developing Go/No-Go maps for ground vehicles, which significantly impact a mission's success. To predict the mobility of terrain, one must understand the soil characteristics. In-situ measurements performed in the field are the current method of collecting this information, which is time-consuming, costly, and can be lethal for military operations. This paper investigates an alternative approach using thermal, multispectral, and hyperspectral remote sensing from an unmanned aerial vehicle (UAV) platform. Remotely sensed data combined with machine learning (linear, ridge, lasso, partial least squares (PLS), support vector machines (SVM), and k nearest neighbors (KNN)) and deep learning (multi-layer perceptron (MLP) and convolutional neural network (CNN)) are used to perform a comparative study to estimate the soil properties, such as the soil moisture and terrain strength, used to generate prediction maps of these terrain characteristics. This study found that deep learning outperformed machine learning. Specifically, a multi-layer perceptron performed the best for predicting the percent moisture content (R2/RMSE = 0.97/1.55) and the soil strength (in PSI), as measured by a cone penetrometer for the averaged 0-6" (CP06) (R2/RMSE = 0.95/67) and 0-12" depth (CP12) (R2/RMSE = 0.92/94). A Polaris MRZR vehicle was used to test the application of these prediction maps for mobility purposes, and correlations were observed between the CP06 and the rear wheel slip and the CP12 and the vehicle speed. Thus, this study demonstrates the potential of a more rapid, cost-efficient, and safer approach to predict terrain properties for mobility mapping using remote sensing data with machine and deep learning algorithms.

A Free Open-Source Bayesian Vancomycin Dosing App for Adults: Design and Evaluation Study.

BACKGROUND: It has been suggested that Bayesian dosing apps can assist in the therapeutic drug monitoring of patients receiving vancomycin. Unfortunately, Bayesian dosing tools are often unaffordable to resource-limited hospitals. Our aim was to improve vancomycin dosing in adults. We created a free and open-source dose adjustment app, VancoCalc, which uses Bayesian inference to aid clinicians in dosing and monitoring of vancomycin. OBJECTIVE: The aim of this paper is to describe the design, development, usability, and evaluation of a free open-source Bayesian vancomycin dosing app, VancoCalc. METHODS: The app build and model fitting process were described. Previously published pharmacokinetic models were used as priors. The ability of the app to predict vancomycin concentrations was performed using a small data set comprising of 52 patients, aged 18 years and over, who received at least 1 dose of intravenous vancomycin and had at least 2 vancomycin concentrations drawn between July 2018 and January 2021 at Lakeridge Health Corporation Ontario, Canada. With these estimated and actual concentrations, median prediction error (bias), median absolute error (accuracy), and root mean square error (precision) were calculated to evaluate the accuracy of the Bayesian estimated pharmacokinetic parameters. RESULTS: A total of 52 unique patients' initial vancomycin concentrations were used to predict subsequent concentration; 104 total vancomycin concentrations were assessed. The median prediction error was -0.600 ug/mL (IQR -3.06, 2.95), the median absolute error was 3.05 ug/mL (IQR 1.44, 4.50), and the root mean square error was 5.34. CONCLUSIONS: We described a free, open-source Bayesian vancomycin dosing calculator based on revisions of currently available calculators. Based on this small retrospective preliminary sample of patients, the app offers reasonable accuracy and bias, which may be used in everyday practice. By offering this free, open-source app, further prospective validation could be implemented in the near future.