Ionizable drug delivery systems for efficient and selective gene therapy

Gene therapy has shown great potential to treat various diseases by repairing the abnormal gene function.However,a great challenge in bringing the nucleic acid formulations to the market is the safe and effective delivery to the specific tissues and cells.To be excited,the development of ionizable drug delivery systems(IDDSs)has promoted a great breakthrough as evidenced by the approval of the BNT162b2 vaccine for prevention of coronavirus disease 2019(COVID-19)in 2021.Compared with conventional cationic gene vectors,IDDSs can decrease the toxicity of carriers to cell membranes,and increase cellular uptake and endosomal escape of nucleic acids by their unique pH-responsive structures.Despite the progress,there remain necessary requirements for designing more efficient IDDSs for precise gene therapy.Herein,we systematically classify the IDDSs and summarize the characteristics and advantages of IDDSs in order to explore the underlying design mechanisms.The delivery mechanisms and therapeutic applications of IDDSs are comprehensively reviewed for the delivery of plasmid DNA(pDNA)and four kinds of RNA.In particular,organ selecting considerations and high-throughput screening are highlighted to explore efficiently multifunctional ionizable nanomaterials with superior gene delivery capacity.We anticipate providing references for researchers to rationally design more efficient and accurate targeted gene delivery systems in the future,and indicate ideas for developing next generation gene vectors.

Approximate Gaussian conjugacy： parametric recursive filtering under nonlinearity, multimodality, uncertainty, and constraint, and beyond

Since the landmark work of R. E. Kalman in the 1960s, considerable efforts have been devoted to time series state space models for a large variety of dynamic estimation problems. In particular, parametric filters that seek analytical estimates based on a closed-form Markov-Bayes recursion, e.g., recursion from a Gaussian or Gaussian mixture （GM） prior to a Gaussian/GM posterior （termed ＇Gaussian conjugacy＇ in this paper）, form the backbone for a general time series filter design. Due to challenges arising from nonlinearity, multimodality （including target maneuver）, intractable uncertainties （such as unknown inputs and/or non-Gaussian noises） and constraints （including circular quantities）, etc., new theories, algorithms, and technologies have been developed continuously to maintain such a conjugacy, or to approximate it as close as possible. They had contributed in large part to the prospective developments of time series parametric filters in the last six decades. In this paper, we review the state of the art in distinctive categories and highlight some insights that may otherwise be easily overlooked. In particular, specific attention is paid to nonlinear systems with an informative observation, multimodal systems including Gaussian mixture posterior and maneuvers, and intractable unknown inputs and constraints, to fill some gaps in existing reviews and surveys. In addition, we provide some new thoughts on alternatives to the first-order Markov transition model and on filter evaluation with regard to computing complexity.

Resampling methods for particle filtering: identical distribution, a new method, and comparable study

Resampling is a critical procedure that is of both theoretical and practical significance for efficient implementation of the particle filter. To gain an insight of the resampling process and the filter, this paper contributes in three further respects as a sequel to the tutorial （Li et al., 2015）. First, identical distribution （ID） is established as a general principle for the resampling design, which requires the distribution of particles before and after resampling to be statistically identical. Three consistent met- rics including the （symmetrical） Kullback-Leibler divergence, Kolmogorov-Smimov statistic, and the sampling variance are introduced for assessment of the ID attribute of resampling, and a corresponding, qualitative ID analysis of representative resampling methods is given. Second, a novel resampling scheme that obtains the optimal ID attribute in the sense of minimum sampling variance is proposed. Third, more than a dozen typical resampling methods are compared via simulations in terms of sample size variation, sampling variance, computing speed, and estimation accuracy. These form a more comprehensive under- standing of the algorithm, providing solid guidelines for either selection of existing resampling methods or new implementations

Editorial:Special issue on distributed computing and artificial intelligence

4:1!Google’s artificial intelligence(AI)program,Alpha Go,has won Go Master Lee Sedol in a best-of-five competition held in Korean March 9-15,2016.Seen by many as a landmark moment for AI,the outcome did not come as a surprise,considering the excellent combination of 1920 CPUs with so-

Abscess of Zygomatic Root： A Rare Otogenic Complication

To the Editor： Zygomatic root abscess is a rare extracranial otogenic complication. Atypical orogenic symptoms and lack of awareness are responsible for misdiagnosis. Here, we presented a case of zygomatic root abscess resulting from an acute attack of masked mastoiditis.