Real-Time Monitoring of High-Intensity Focused Ultrasound Thermal Therapy Using the Manifold Learning Method

High-intensity focused ultrasound (HIFU) induces thermal lesions by increasing the tissue temperature in a tight focal region. The main ultrasound imaging techniques currently used to monitor HIFU treatment are standard pulse-echo B-mode ultrasound imaging, ultrasound temperature estimation and elastography-based methods. The present study was carried out on ex vivo animal tissue samples, in which backscattered radiofrequency (RF) signals were acquired in real time at time instances before, during and after HIFU treatment. The manifold learning algorithm, a non-linear dimensionality reduction method, was applied to RF signals which construct B-mode images to detect the HIFU-induced changes among the image frames obtained during HIFU treatment. In this approach, the embedded non-linear information in the region of interest of sequential images is represented in a 2-D manifold with the Isomap algorithm, and each image is depicted as a point on the reconstructed manifold. Four distinct regions are chosen in the manifold corresponding to the four phases of HIFU treatment (before HIFU treatment, during HIFU treatment, immediately after HIFU treatment and 10-min after HIFU treatment). It was found that disorganization of the points is achieved by increasing the acoustic power, and if the thermal lesion has been formed, the regions of points related to pre- and post-HIFU significantly differ. Moreover, the manifold embedding was repeated on 2-D moving windows in RF data envelopes related to pre- and post-HIFU exposure data frames. It was concluded that if mean values of the points related to pre- and post-exposure frames in the reconstructed manifold are estimated, and if the Euclidean distance between these two mean values is calculated and the sliding window is moved and this procedure is repeated for the whole image, a new image based on the Euclidean distance can be formed in which the HIFU thermal lesion is detectable.     

中医医案方剂多维相似度算法设计与探索＊

中医医案方剂具有多维度的特性，单独采用一种相似度算法难以展现方剂数据在各维度的相似情况，具有一定的局限性。本研究基于中医医案方剂的多维数据特点，结合中医临床遣方用药的思维过程，设计了中医医案方剂的多维相似度算法，并以七则国医大师医案为示例，详细展示了该算法的计算过程，同时将多维相似度算法计算结果与传统算法计算结果进行了对比分析。通过对比可以看出本算法能够相对全面地反映不同方剂间的相似度，且结果更为合理也更贴合中医临床实际。     

Building longitudinal medication dose data using medication information extracted from clinical notes in electronic health records

ObjectiveTo develop an algorithm for building longitudinal medication dose datasets using information extracted from clinical notes in electronic health records (EHRs).Materials and MethodsWe developed an algorithm that converts medication information extracted using natural language processing (NLP) into a usable format and builds longitudinal medication dose datasets. We evaluated the algorithm on 2 medications extracted from clinical notes of Vanderbilt’s EHR and externally validated the algorithm using clinical notes from the MIMIC-III clinical care database.ResultsFor the evaluation using Vanderbilt’s EHR data, the performance of our algorithm was excellent; F1-measures were ≥0.98 for both dose intake and daily dose. For the external validation using MIMIC-III, the algorithm achieved F1-measures ≥0.85 for dose intake and ≥0.82 for daily dose.DiscussionOur algorithm addresses the challenge of building longitudinal medication dose data using information extracted from clinical notes. Overall performance was excellent, but the algorithm can perform poorly when incorrect information is extracted by NLP systems. Although it performed reasonably well when applied to the external data source, its performance was worse due to differences in the way the drug information was written. The algorithm is implemented in the R package, “EHR,” and the extracted data from Vanderbilt’s EHRs along with the gold standards are provided so that users can reproduce the results and help improve the algorithm.ConclusionOur algorithm for building longitudinal dose data provides a straightforward way to use EHR data for medication-based studies. The external validation results suggest its potential for applicability to other systems.     

A Non-Ersatz Material Approach for the Topology Optimization of Elastic Structures Based on Piecewise Constant Level Set Method

The topology optimization of a linearized elasticity system with the area (volume) constraint is investigated. A non-ersatz material approach is proposed. By introducing a fixed background domain, the linearized elasticity system is extended into the background domain by a characteristic function. The piecewise constant level set (PCLS) method is applied to represent the original material region and the void region. A quadratic function of PCLS function is proposed to replace the characteristic function. The functional derivative of the objective functional with respect to PCLS function is derived, which is zero in the void region and nonzero in the original material region. A penalty gradient algorithm is proposed. Four numerical experiments of 2D and 3D elastic structures with different boundary conditions are presented, illustrating the validity of the proposed algorithm.     

New diagnostic algorithm for detection of covert Bow Hunter's Syndrome

Bow hunter''s syndrome (BHS) should not be overlooked as a cause of cerebral infarction in the posterior circulation. However, covert BHS, which does not impair blood flow with simple rotation but only at certain angles, may make the diagnosis of BHS difficult. We propose a new algorithm to detect BHS or covert BHS. We recommend that BHS and covert BHS be detected by noninvasive duplex ultrasonography, which will allow for appropriate treatment.     

Using machine learning to optimize antibiotic combinations: dosing strategies for meropenem and polymyxin B against carbapenem-resistant Acinetobacter baumannii

ObjectivesIncreased rates of carbapenem-resistant strains of Acinetobacter baumannii have forced clinicians to rely upon last-line agents, such as the polymyxins, or empirical, unoptimized combination therapy. Therefore, the objectives of this study were: (a) to evaluate the in vitro pharmacodynamics of meropenem and polymyxin B (PMB) combinations against A. baumannii; (b) to utilize a mechanism-based mathematical model to quantify bacterial killing; and (c) to develop a genetic algorithm (GA) to define optimal dosing strategies for meropenem and PMB.MethodsA. baumannii (N16870; MIC_meropenem = 16 mg/L, MIC_PMB = 0.5 mg/L) was studied in the hollow-fibre infection model (initial inoculum 10⁸ cfu/mL) over 14 days against meropenem and PMB combinations. A mechanism-based model of the data and population pharmacokinetics of each drug were used to develop a GA to define the optimal regimen parameters.ResultsMonotherapies resulted in regrowth to ~10¹⁰ cfu/mL by 24 h, while combination regimens employing high-intensity PMB exposure achieved complete bacterial eradication (0 cfu/mL) by 336 h. The mechanism-based model demonstrated an SC₅₀ (PMB concentration for 50% of maximum synergy on meropenem killing) of 0.0927 mg/L for PMB-susceptible subpopulations versus 3.40 mg/L for PMB-resistant subpopulations. The GA had a preference for meropenem regimens that improved the %T > MIC via longer infusion times and shorter dosing intervals. The GA predicted that treating 90% of simulated subjects harbouring a 10⁸ cfu/mL starting inoculum to a point of 10⁰ cfu/mL would require a regimen of meropenem 19.6 g/day 2 h prolonged infusion (2 hPI) q5h + PMB 5.17 mg/kg/day 2 hPI q6h (where the 0 h meropenem and PMB doses should be ‘loaded’ with 80.5% and 42.2% of the daily dose, respectively).ConclusionThis study provides a methodology leveraging in vitro experimental data, a mathematical pharmacodynamic model, and population pharmacokinetics provide a possible avenue to optimize treatment regimens beyond the use of the ‘traditional’ indices of antibiotic action.