In this study, we investigated the effects of extracellular matrix rigidity, an important physical property of microenvironments regulating cell morphology and functions, on sonoporation facilitated by targeted microbubbles, highlighting the role of microbubbles. We conducted mechanistic studies at the cellular level on physiologically relevant soft and rigid substrates. By developing a unique imaging strategy, we first resolved details of the 3D attachment configurations between targeted microbubbles and cell membrane. High-speed video microscopy then unveiled bubble dynamics driven by a single ultrasound pulse. Finally, we evaluated the cell membrane permeabilization using a small molecule model drug. Our results demonstrate that (1) stronger targeted microbubble attachment was formed for cells cultured on the rigid substrate, while six different attachment configurations were revealed in total; (2) more violent bubble oscillation was observed for cells cultured on the rigid substrate, while one third of bubbles attached to cells on the soft substrate exhibited deformation shortly after ultrasound was turned off; (3) higher acoustic pressure was needed to permeabilize the cell membrane for cells on the soft substrate, while under the same ultrasound condition, acoustically-activated microbubbles generated larger pores as compared to cells cultured on the soft substrate. The current findings provide new insights to understand the underlying mechanisms of sonoporation in a physiologically relevant context and may be useful for the clinical translation of sonoporation. The effect of dual-frequency ultrasound treatment with different working modes on the lysinoalanine (LAL) formation and structural characterization of rice dreg protein isolates (RDPI) was studied during alkaline exaction processing. Ultrasonic notably decreased the LAL amount of RDPI and enhanced the protein dissolution rate. The LAL content of RDPI, especially sequential dual frequency 20/40 kHz, decreased by 12.02% (P  less then  0.05), compared to non-sonicated samples. Herein, the protein dissolution rate was higher. The changes in sulfhydryl groups was positively correlated with the LAL formation. The amino acids (AA) such as threonine (Thr), lysine (Lys), and arginine (Arg) were reduced, resulting in a decrease in LAL content following sonication. Besides, ultrasonication altered protein secondary structure by reducing random coil and β-sheet contents, while α-helix and β-turn contents increased. Alterations in the surface hydrophobicity, particle size, particle size distribution, and microstructure indicated more irregular fragment with microparticles of RDPI by sonochemical treatment. Thus, ultrasound treatment may be a new and efficacious process for controlling the LAL generation in prepared-protein food(s) during alkali extraction. BACKGROUND AND OBJECTIVES Diagnosis and early intervention of chronic kidney disease are essential to prevent loss of kidney function and a large amount of financial resources. To this end, we developed a fuzzy logic-based expert system for diagnosis and prediction of chronic kidney disease and evaluate its robustness against noisy data. METHODS At first, we identified the diagnostic parameters and risk factors through a literature review and a survey of 18 nephrologists. Depending on the features selected, a set of fuzzy rules for the prediction of chronic kidney disease was determined by reviewing the literature, guidelines and consulting with nephrologists. Fuzzy expert system was developed using MATLAB software and Mamdani Inference System. Finally, the fuzzy expert system was evaluated using data extracted from 216 randomly selected medical records of patients with and without chronic kidney disease. We added noisy data to our dataset and compare the performance of the system on original and noisy datasets. RESULTS We selected 16 parameters for the prediction of chronic kidney disease. The accuracy, sensitivity, and specificity of the final system were 92.13 %, 95.37 %, and 88.88 %, respectively. The area under the curve was 0.92 and the Kappa coefficient was 0.84, indicating a very high correlation between the system diagnosis and the final diagnosis recorded in the medical records. The performance of the system on noisy input variables indicated that in the worse scenario, the accuracy, sensitivity, and specificity of the system decreased only by 4.43 %, 7.48 %, and 5.41 %, respectively. CONCLUSION Considering the desirable performance of the proposed expert system, the system can be useful in the prediction of chronic kidney disease. BACKGROUND Implantable cardioverter-defibrillators (ICDs) have been shown to reduce sudden cardiac death in appropriately selected patients, but they remain underutilized among indicated patients. OBJECTIVE To develop a new approach to identifying guideline indications among patients implanted with ICDs by creating algorithms that extract data from electronic health records (EHR). METHODS Published guidelines providing recommendations for ICD use were distilled into categories of diagnoses, measures, procedures, and terminologies. Criteria for each indication category were translated into clinical algorithms using administrative codes, search terms, and other required data. Cardiologists with guideline-development expertise reviewed these algorithms. selleck chemicals llc After developing applications using a subset of data, phenotypes were evaluated against a curated Optum® de-identified EHR dataset, including 94,441 patients with ≥1 procedure codes for ICD implantation or follow-ups from 47 US provider networks. RESULTS Guideline-concordant indications were identified in 83.7 % of 49,560 patients with new ICD implants. The percentage of ICD patients with guideline-concordant indications ranged from 69.4%-88.1% for patients whose initial EHR records were 0-6 days to >365 days prior to implant, respectively. Many guideline criteria used data which could only be derived from unstructured provider notes and required significant algorithm development. CONCLUSIONS Defibrillator implant indications were detected in >80 % of patients receiving ICDs using rule-based algorithms in a curated EHR dataset. Computable phenotypes may enable researchers to analyze EHRs in more reproducible ways, by identifying guideline indications in patients with specific therapies such as ICDs, and, by extension, identifying patients who meet indications yet do not yet have indicated therapies. V.