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Abstract Background Cefiderocol is the first antibiotic with effluent flow rate–based dosing recommendations outlined in the product label for patients receiving continuous renal replacement therapy (CRRT). We aimed to investigate the population pharmacokinetics of cefiderocol among patients receiving CRRT and validate these dosing recommendations. Methods A multicenter, prospective cefiderocol pharmacokinetic study among intensive care unit patients receiving CRRT was conducted (2022–2023). Blood sampling was performed at steady-state and cefiderocol concentrations were assayed by validated liquid chromatography–tandem mass spectrometry. Population pharmacokinetic analyses were conducted in Pmetrics using R software. The free time above the minimum inhibitory concentration (f T > MIC) and total daily area under the concentration time curve (AUCdaily) were calculated. Results Fourteen patients with effluent flow rates ranging from 2.1 to 5.1 L/h were enrolled. Cefiderocol concentrations best fitted a 2-compartment model. Mean ± standard deviation (SD) parameter estimates for clearance, central compartment volume, and intercompartment transfer constants (k12 and k21) were 3.5 ± 1.5 L/hour, 10.7 ± 8.4 L, 3.9 ± 1.8 hours−1, and 2.2 ± 2.2 hours−1, respectively. With simulations based on product label dosing recommendations, all patients achieved 100% fT > MIC up to MIC 8 mg/L with an AUCdaily (mean ± SD) of 1444 ± 423 mg × hour/L. Cefiderocol was well tolerated among the 14 patients. Conclusions The current package insert dosing recommendations resulted in pharmacodynamically optimized cefiderocol exposures. Cefiderocol concentrations exceeded relevant MIC breakpoints in all patients at each effluent flow rate, and AUCdaily was within the range observed in patients in the phase 3 clinical trials, suggestive of a safe and therapeutic drug profile.

Video surveillance faces challenges due to the need for improved anomalous event recognition techniques for human activity recognition. Growing security concerns make standard CCTV systems insufficient because of high monitoring costs and operator exhaustion. Therefore, automated security systems with real-time event recognition are essential. This research introduces a semantic key frame extraction algorithm based on action recognition to minimize frame volume big video data. This approach has not been previously applied with ResNet50, VGG19, EfficientNetB7, and ViT_b16 models for recognizing anomalous events in surveillance videos. The findings demonstrate the effectiveness of this method in achieving high accuracy rates. The proposed method addresses the challenges posed by large volumes of frames generated by surveillance videos, requiring effective processing techniques. A large number of videos from the UCF-Crime dataset were used for proposed model evaluation, including both abnormal and normal videos during the training and testing phase. EfficientNetB7 achieved 86.34% accuracy, VGG19 reached 87.90%, ResNet50 attained 90.46%, and ViT_b16 excelled with 95.87% accuracy. Compared to state-of-the-art models from other studies, the transformer model (ViT_b16) outperformed these algorithms, demonstrating significant improvements in recognizing anomalous events.