The ability of organisms to keep track of external time, by means of the circadian clock interacting with the environment, is essential for health. The focus of this review is recent methods to detect the internal circadian time of an omics sample. Before reaching our main topic, we introduce the circadian clock, its hierarchical structure, and its main functions; we will also explain the notion of internal time, or circadian phase, and how it differs from the geophysical time. We then focus on the role played by the clock in the maintenance of human heath, in particular in the context of cancer. Thereafter, we analyze an important methodological question: how to infer the circadian phase of unlabeled omics snapshot measurements. Answering this question could both significantly increase our understanding of the circadian clock and allow the use of this knowledge in biomedical applications. We review existing methods, focusing on the more recent ones, following a historical trajectory. We explain the basic concepts underlying the methods, as well as some crucial technical aspects of each. We conclude by reporting how some of these methods have, more or less effectively, enabled furthering our understanding of the clock and given insights regarding potential biomedical applications. The circadian clock is a molecular and pervasive2 cell autonomous oscillator. This clock gains its periodicity of almost 24 hours3 from an as-yet incompletely understood mechanism, which most likely involves a transcriptional–translational negative feedback loop4 but probably also other regulatory layers. In mammals, the structure for time-keeping is hierarchical5: a master clock6 is hosted in the brain’s suprachiasmatic nuclei (SCN) and peripheral clocks tick in virtually all of the other organs. The clock in the SCN responds to the external light cycle and synchronizes the body’s peripheral clocks7. However, these peripheral clocks are susceptible to temporal cues different from light8, e.g. the clock in the liver is responsive to feeding patterns9. The molecular mechanisms of the clock have been subjects of thorough studies, especially in the last 20 years. A series of key studies12 has shown that the core clock oscillator involves a fairly small set (likely in the order of 20) of genes, called core clock genes. These genes in turn regulate the temporal expression of other genes, which drive programs of 24 hour tissue-specific rhythms in gene expression and physiology14.Earth rotation around its axis imposes 24 hour rhythms to all life on the planet. The circadian, from Latin “circa diem” (about a day), clock19.The circadian clock’s free running period is not exactly 24 hours. However, under normal physiological conditions where the organism is subject to external 24 hour periodic cues (Zeitgebers), the clock synchronizes to external time. An important consequence is that the internal positioning of an individual’s circadian timekeeper with respect to the external time varies from individual to individual, depending on the combination of free running period and amplitude of that individual’s clock and importantly the Zeitgeber strength, e.g. the intensity of light. The difference (dephasing) between the internal and external time is called chronotype and varies in humans with a standard deviation of 2 hours20 and has been under scrutiny ever since. Faults in the circadian clock have also been connected with the aberrant metabolism of cancerous cells21. However, it is only fairly recently that the role of the circadian clock in human health has earned a more prevalent position in biomedical research, as ties have been discovered with a wider variety of diseases. Studies have shown that in humans a perturbed circadian clock, due to shift work and sleep disruption, leads to metabolic pathologies22. In mice, different aging-related phenotypes appear depending on which gear of the clock is broken; in particular, in some cases, lifespan is significantly shorter23. In addition, the synchrony both among all the internal clocks in different organs and with the external cycle is responsible for proper timing of downstream metabolic processes, thereby contributing to the health of the organism; the interplay of the circadian and the metabolic networks can be disrupted, especially in humans, by a variety of factors such as aging, meal timing, jet-lag, and shift work24. The issue of understanding exactly how light conditions and timing of food intake affect the daily metabolic and sleep cycles has been recently discussed in26. In addition, pharmacological modulations of the circadian clock have been introduced as a concept to fight cancer27. In recent years, because of the vast availability of data, more and more connections between the circadian clock and cancerous growths have been identified30. To sum up, our current understanding suggests that the lack of a functioning circadian clock contributes to irregular and spread-out food intake and metabolic disorders and may lead to higher cancer rates and a shorter life span. However, it should be mentioned that, although promising in offering potential novel therapeutic avenues, complete biochemical proofs of these notions are still lacking in many cases. On the other hand, many factors contribute to the weakening of the circadian rhythm; some are due to our lifestyle, such as sleep disruption, shift work, and absence of 12 hour fasting periods, and others are further from our control, such as aging, chronic diseases characterized by altered metabolism, and cancer.A lot of attention has been drawn to the circadian clock in recent years as more links were found between various illnesses and a malfunctioning clock. In particular, during the last 20 years, two questions were repeatedly addressed: what are the most prominent effects on human health of a disrupted circadian clock, and what are the main causes in today’s lifestyle that deteriorate our internal timing system? Notably, the idea that the clock, due to reciprocal interactions with cell-cycle control, could be involved in a tumor suppressor function has existed since the end of the last centuryTo study the mammalian clock, many experiments spanning the full day have been performed, mostly on mice. The general setup is to sacrifice mice every 2 to 6 hours over 24 hours. Most of these experiments use highly controlled conditions. Notably, the mice are typically of one genetic background and taken from the same gender; moreover, environmental light or temperature conditions are tightly controlled, as well as the feeding regimen and schedules.31. In this experiment, two mice were sacrificed every 2 hours, then 12 tissues were analyzed for their mRNA expression levels using RNA sequencing. This seminal dataset has since then served as a benchmark for other around-the-clock experiments on mice32. This mouse dataset has also served as testing ground for a number of methods aiming to reconstruct circadian phase from single omics snapshot samples. Given the strictly controlled experimental conditions typical of mice experiments, this atlas minimizes all the variation in gene expression due to factors different from the circadian clock. More recently, an atlas on baboons33 has been published in an effort to close the gap between mice and humans. In this case, one baboon was sacrificed every 2 hours and 64 tissues were collected and analyzed. As baboons are more similar to humans than mice, these data may in principle provide a better benchmark for future circadian studies in humans, especially considering the genetic diversity of the sacrificed baboons. In particular, mice are nocturnal animals and there are thus intrinsic differences in clock programs34, though these are not yet fully characterized. We note that some of the animal studies are powered to overcome false discovery rate (FDR) corrections, while others are not.Although many experiments focus on specific organs or conditions, some transcriptome analyses provide comprehensive views on temporal gene regulation across an entire organism. Five years ago, the first atlas of gene expression around the clock was published35 for a variety of illnesses, most importantly cancer. In addition, reliable methods to infer circadian phase would allow us to further study the structure and effects of the clock by leveraging the vast quantity of existing unlabeled RNA sequencing data on humans available in public databases, thus gaining important insights on clock biology in healthy and diseased human tissues, notably useful for future biomedical applications. Although many outstanding works have been recently published, how reliably and robustly this can be achieved remains an open question. An array of methods is currently available. The majority of algorithms available are supervised in the sense that the parameters are trained on time-labeled data (the training set). A word of caution concerns the intrinsic difference between the circadian phase and the external time, as explained above, which is generally not considered: circadian phase and the external time are often taken as equal in labeled datasets. One natural method to assign a circadian phase to unlabeled samples consists of applying the four-quadrant arc-tangent function to the two first principal components36; we will refer to this unsupervised method as PCA. In 2004, a first significant step was made37 by developing a method, molecular timetable, to infer the circadian phase based on the temporal and expression patterns of known clock-related marker genes in mice livers. The core idea is shared with several other supervised methods that will follow it. Namely, it consists of finding a suitable low-dimensional representation  and then, in this coordinate system, finding the one-dimensional trajectory (termed here circadian trajectory) that best matches the known labels of the samples in the training set. A graphical representation of this core idea is shown in38 in 2016. This method adds a supervised twist to PCA. The low-dimensional space is a set of sparse principal components (SPCs), each component being a sparse linear combination of genes. The linear combination of each component and the non-linear mapping from the SPCs to the circadian phase is optimized on the labeled training set, while the number of SPCs and the maximum nonzero entries in each of them are optimized by cross validation. To infer the phase of new samples, the algorithm linearly projects the gene expression levels to the SPC space and then finds the phase at which the circadian trajectory is closest to the projected gene expression levels. This method exhibits good performance in inferring the circadian phase; in addition, because of its easy and clear structure, it is able to identify which genes carry the most information about the circadian oscillation in a labeled sample. A few months after, a machine learning method was developed: BIO_CLOCK14. This method is made of a three-layer dense neural network taking as input the normalized expression of 16 clock genes and yielding as an output the sine and cosine of the circadian phase. This method was trained and tested on mice microarray data from different organs with a 70–30 training-test split. Being a dense neural network, the training can be performed via backpropagation. To infer the phase of a new standardized sample, one simply inputs the required gene expression levels and takes the four-quadrant arc-tangent of the network’s output. Although this method uses a black box neural network, its performance is on a par with ZeitZeiger. Finally, the need for an unsupervised method (no labeled dataset is available) was recognized, and in 2017 CYCLOPS was invented39. This method uses a linear autoencoder with a circular node, constructed with two coupled neurons, so the low-dimensional space is the two-dimensional space of the code neurons and the circadian trajectory is the unit circle in the two-dimensional space from which the circadian phase is extracted via the arc-tangent. CYCLOPS does not need a training set to learn its governing parameters; however, it cannot predict the phase of only one new sample. The general scheme of this algorithm is as follows: project to a low-dimensional space, from this project to a circadian trajectory, and from the circadian trajectory try to project back onto the original space, making the smallest possible error across all samples. For CYCLOPS, the low-dimensional representation is reached by linear projection onto two dimensions and the circadian trajectory is simply the unit circle where all points of the two-dimensional space are orthogonally projected; from the two-dimensional unit circle, points are linearly projected back to try to match the measured gene expression levels. The difference between CYCLOPS and PCA is the non-linearity from the two-dimensional space to the one-dimensional circadian trajectory included in the autoencoder. CYCLOPS offers one crucial advantage over supervised methods: it is more easily generalizable. This means that it can be directly applied to a much wider range of datasets, even ones with biologically different periodic behaviors, like the cell cycle, without the need for enough labeled data to retrain the algorithm. However, one must note that to obtain the best results the input of CYCLOPS should be restricted to a subset of genes, which have been previously implicated with time. Still, in 2017, a new supervised method, PLSR40, was published. The idea of this method is to linearly project both the sequencing data and the two-dimensional representation of the phase (its sine and cosine) onto a five-dimensional feature space and maximize the correlation between the two. In this case, the projection to the feature space is optimized on the training set and the dimensionality of the feature space is optimized via cross validation. This method was built, trained, and tested on in-house blood sample measurements, which, unlike all the other training sets, used the dim light melatonin onset (DLMO), the gold standard assay to estimate circadian phase in humans, as the measure for circadian phase, not the external time. Using the gold standard for circadian phase detection in the training set gives this method a good advantage; however, it was never applied to datasets not collected in the authors’ lab. Lastly, another supervised method to infer circadian phase from blood RNA sequencing data was developed: TimeSignature41. This approach is reminiscent of42. A summary of the main characteristics of each of these methods is presented inSince the importance of circadian timing in human health was established, an interesting question arose: how can we detect the internal time of a tissue sample from its gene expression level? Answering this question might open up significant novel medically relevant opportunities, especially for diagnosis, prognosis, and potentially therapeutic strategies43. A year later, in another study using blood samples, ZeitZeiger was trained on monocytes purified from the blood to accurately obtain the circadian phases from single blood samples44. The resulting improved accuracy can be explained by the fact that peripheral blood mononuclear cells (PBMCs), which are used by most studies, consist of a complex mixture of many cells including T and B cells, which together make up more than 80% of the cells; however, the clock in T or B cells is weak. On the other hand, monocytes host a high amplitude clock, which likely explains the superior accuracy at predicting the DLMO. Other researchers focused on human skin: in a first step, they applied CYCLOPS to reorder a large set of unlabeled samples, then they applied ZeitZeiger to find a small subset of biomarkers for circadian phase in human skin45. Application of these algorithms to data from blood or skin samples is particularly relevant, as they are easily accessible and can potentially be easily used in biomedical research. Of note, the different studies listed differ by the type of readouts used (candidate genes vs. whole genome); in the context of clinical applications, it appears that candidate gene approaches such as NanoString assays44 might have a clear advantage in terms of costs and complexity. An interesting application of these methods was published in 2018. CYCLOPS was applied to temporally order human RNA sequencing data from 13 different post-mortem tissues and used to perform a comparative rhythmic analysis across them46. In particular, the authors focused on drug target genes to propose improvements in the effectiveness of therapies by delivering drugs at the optimal time of day. In conclusion, the discussed advances in biomedical circadian biostatistics application promise to sprout important advances in around-the-clock treatments, especially those concerning the maximization of drug efficacy and the minimization of their side effects47.Finally, we dedicate a few words to the impact that some of these methods had and how they have been applied. As we have seen, ZeitZeiger is a supervised method originally trained on various mouse tissues; in 2017, it was trained on existing whole human blood microarray data and a small set of marker genes was found to reliably reconstruct the circadian phase of each sample


Due to the development of computer vision and natural language processing technologies in recent years, there has been a growing interest in multimodal intelligent tasks that require the ability to concurrently understand various forms of input data such as images and text. Vision-and-language navigation (VLN) require the alignment and grounding of multimodal input data to enable real-time perception of the task status on panoramic images and natural language instruction. This study proposes a novel deep neural network model (JMEBS), with joint multimodal embedding and backtracking search for VLN tasks. The proposed JMEBS model uses a transformer-based joint multimodal embedding module. JMEBS uses both multimodal context and temporal context. It also employs backtracking-enabled greedy local search (BGLS), a novel algorithm with a backtracking feature designed to improve the task success rate and optimize the navigation path, based on the local and global scores related to candidate actions. A novel global scoring method is also used for performance improvement by comparing the partial trajectories searched thus far with a plurality of natural language instructions. The performance of the proposed model on various operations was then experimentally demonstrated and compared with other models using the Matterport3D Simulator and room-to-room (R2R) benchmark datasets. Driven by the rapid growth of computer vision and natural language processing technologies, in recent years there has been a growing interest in multimodal intelligent tasks that require the ability to concurrently understand various forms of input data such as images and text. Some of the typical multimodal intelligent tasks are vision question answering (VQA)  that genAmong these multimodal intelligent tasks, we will explore VLN tasks, in which an agent moving in a three-dimensional virtual environment is requested to navigate to a particular location following natural language instructions. Consider an agent placed in a 3D virtual indoor environment generated by the Matterport3D Simulator . As showAs shown in Because most natural language instructions provide only partial descriptions of the trajectory to the target position, the agent encounters difficulties in understanding the instructions unless they are efficiently combined with visual features using the alignment and grounding feature. In VLN-related studies ,19,20,21Effective path planning and action selection strategies for reaching the target position are crucial for executing VLN tasks. Most VLN-related studies ,19,20,21A novel deep neural network model, with joint multimodal embedding and backtracking search for VLN tasks.Rich context utilization based on a transformer-based multimodal embedding module and a temporal contextualization module.A backtracking-enabled greedy local search algorithm to improve the success rate and optimize the path.A novel global scoring method to monitor task progress by comparing the partial trajectories searched thus far with a plurality of natural language instructions.State-of-the-art results on the VLN task.In an effort to overcome the limitations of previous studies for solving VLN tasks, we propose the joint multimodal embedding and backtracking search (JMEBS), a novel deep neural network model. The proposed model uses a transformer-based, joint multimodal embedding module to obtain a text context that is efficient for action selection based on natural language instructions and real-time input images. Unlike the conventional transformer-based pretrained models, the JMEBS model uses multimodal context at each time step that connects the current image to natural language instructions, and temporal context that contains the agent’s past statuses and actions. One of its salient features is that the context information extracted by the module can be integrated into various path planning and action selection strategies. It is also equipped with a backtracking-enabled local search feature designed to improve the task success rate and optimize the navigation path based on the local and global scores related to candidate actions. Additionally, a global scoring method is used to compare partial trajectories searched with various natural language instructions. In summary, our contributions include:The remainder of this article is organized as follows: In a study on VLN tasks , a relatResearchers have attempted to understand the association between natural language instructions and real-time input images in a range of studies on VLN tasks. One of these attempts resulted in an attention mechanism for co-grounding multimodal input data ,10,11. IConversely, one of the most important issues associated with the solution of a VLN task is an efficient path planning and action selection strategy. In a VLN task, the agent is not explicitly given the target position; it is given only the natural language instructions that provide directions to the target position. Hence, the agent needs to perform efficient path planning and action selection strategies to reach the target position based on the real-time input image and natural language instructions. In a VLN task, a graphic map is predefined for each environment, and the agent should move from one node to the next to navigate to the target position. Therefore, in previous studies, greedy local ,9,10 andAttempts to overcome these drawbacks of greedy local search and beam search have been made in two VLN-related studies ,14. In , an exteThe VLN is a task in which the agent navigates to an unknown target position following natural language instructions. It is assumed that the real-time input of the panoramic image of the environment is provided to the agent that performs the task. G) consisting of nodes (waypoints) and edges (connections) is defined at each position visited by the agent. The VLN server controls the agent to navigate only on G. In this way, it is not exposed to the agent that determines each navigation action by perceiving the environment. As shown in tO) acquired at the current position (state tS) at each time step. Accordingly, the panoramic image is divided into 36 partial images of equal size, each of which is a candidate action. If the agent selects and executes any of these candidate actions, the management system moves the agent to the node closest to the chosen direction on G. The agent navigates by repeating this process and performs the “stop” action at the position predicted as the goal. If the agent’s final position is within 3 m of the target position, the management system marks the task as successful.As shown in The proposed model JMEBS performs the local scoring of a candidate action based on the multimodal context extracted from the natural language instruction and panoramic image using the pretrained joint embedding module and temporal contextualization reflecting past statuses and actions. Additionally, it performs global scoring to determine the need for backtracking by comparing the natural language instructions describing the entire trajectory from the initial to the final position with the partial trajectories up to the current position.Accordingly, the JMEBS model is largely divided into a local scoring network, a global scoring network, and an action selection network, as shown in The local scoring network consists of three stages: multimodal embedding, temporal contextualization, and local scoring. In the multimodal embedding stage, panoramic images and natural language instructions are embedded to enable the use of real-time input data. The joint multimodal embedding module proposed in this study is used in the multimodal embedding process to enable information exchange between the image and natural language modes. This process involves visual information embedding based on natural language instruction, and natural language embedding based on visual information. The temporal contextualization stage is composed of a context decoding module for updating temporal context and a co-attention module for detecting the focus area from the input data using the context feature. In the context updating stage, a new context The optimal action to take at the current time step  to the existing visual feature  algorithm.BGLS, and the epoch was set to 50,000.The first experiment aimed to prove the positive effect of the joint embedding textual and visual features (JETVF) employed in the proposed model. For this experiment, the JETVF are compared in three different conditions: (i) without joint embedding features (w/o JEF), (ii) only with joint embedding visual features (JEVF), and (iii) and only with joint embedding textual features (JETF). The results presented in The second experiment was conducted to prove the positive effect of the attended visual textual action module (AvtAM), the temporal contextualizing module of the proposed model JMEBS. For this experiment, three different implementations of the temporal contextualizing module were compared, as shown in The results presented in The third experiment was conducted to prove the superiority of the dynamic time warping (DTW) with triple instructions, the global scoring method used in the proposed model JMEBS. We used the Levenshtein distance (LD) and DTW to measure the similarity of natural language instruction measurements. The partial trajectories executed thus far were checked against (i) single instruction and triple instructions. The experimental results in The fourth experiment was conducted to prove the search efficiency of the BGLS algorithm proposed in this study. In this experiment, three algorithms were compared: greedy local search, beam search, and BGLS. In the case of the beam search, the number of parallel search trajectories K was varied and took the values of 10, 20, and 30. The experimental results in The last experiment was conducted to prove the superiority of the proposed JMEBS model by comparing it with conventional VLN models. The experimental results in In this study, a qualitative analysis of the proposed JMEBS model was performed on real-life examples of several VLN tasks. However, JMEBS was observed to pay visual attention to an incorrect image area at step 4 in This study proposed the novel deep neural network model JMEBS as an efficient tool to solve VLN tasks. The proposed model was designed to use the past temporal context along with the multimodal context extracted with the joint multimodal embedding module. Additionally, JMEBS employed BGLS, a novel greedy local search algorithm with a backtracking feature that improved the task success rate and search efficiency. The proposed model also came with a new global scoring method for the comparison of all partial trajectories executed to reach the current position with various natural language instructions. Finally, we verified the superiority of the proposed model by various experiments conducted with R2R benchmark datasets.However, the current JMEBS yielded a slightly worse performance in “unseen” compared to “seen” environments. Previous studies used various methods to improve model performance in unseen environments, such as environmental dropout  and self


Although dry powder inhalers (DPIs) have attracted great interest compared to nebulizers and metered-dose inhalers (MDIs), drug deposition in the deep lung is still insufficient to enhance therapeutic activity. Indeed, it is estimated that only 10–15% of the drug reaches the deep lung while 20% of the drug is lost in the oropharyngeal sphere and 65% is not released from the carrier. The potentiality of the powders to disperse in the air during the patient’s inhalation, the aerosolization, should be optimized. To do so, new strategies, in addition to classical lactose-carrier, have emerged. The lung deposition of carrier-free particles, mainly produced by spray drying, is higher due to non-interparticulate forces between the carrier and drug, as well as better powder uniformity and aerosolization. Moreover, the association of two or three active ingredients within the same powder seems easier. This review is focused on a new type of carrier-free particles which are characterized by a sugar-based core encompassed by a corrugated shell layer produced by spray drying. All excipients used to produce such particles are dissected and their physico-chemical properties  are put in relation with the lung deposition ability of powders. The importance of spray-drying parameters on powders’ properties and behaviors is also evaluated. Special attention is given to the relation between the morphology (characterized by a corrugated surface) and lung deposition performance. The understanding of the closed relation between particle material composition and spray-drying process parameters, impacting the final powder properties, could help in the development of promising DPI systems suitable for local or systemic drug delivery. Pulmonary administration of drugs is gaining much importance in the latest research as it offers several advantages for the treatment of local or systemic diseases. Three major inhalation systems have been proposed for the aerosolization of drugs: nebulizers, metered-dose inhalers (MDIs) and dry powder inhalers (DPIs). Currently, DPIs, which deliver medication to the lungs in the form of a dry powder, represent the most promising system due to higher drug deposition in the deep lung. In addition, DPI inhalation systems do not use inhalation propellant gases detrimental to the environment and do not depend on the patient’s coordination during the inhalation process ,2,3. BesDrug efficacy is dependent on drug pulmonary deposition which depends on both the inhalation device performance and the aerosolization characteristics of the powder filled within it. Thus, when using DPIs for the aerosolization of drugs, the drug–device combination must be optimal to reach maximal drug efficacy. DPIs can be either “single-dose”, which use individual drug doses inside blisters or capsules, or “multi-dose” inhalers, which are basically composed of a reservoir filled with the drug powder. The design of new devices can intimately influence the lung delivery of drugs and should not be neglected ,6,7. HowIn order to reach the deep pulmonary drug deposition goal, DPI must ideally contain a powder made of the active pharmaceutical ingredients (API) coformulated with a range of excipients, which are chosen based on their precise functions within the powder, leading to optimal aerosolization performances and, consequently, high deposition into pulmonary regions. Traditionally, a sugar-based carrier  is used in order to increase the poor aerosolization properties of API intended for lung delivery. Indeed, due to the low dosage of the API in some formulations and to the presence of cohesive fine particles which have high propensity to aggregate, pulmonary active drugs are almost never used alone but are combined with a carrier or “flow-aid”. Physical interparticulate forces between the carrier and API occur but are generally too intense, leading to poor delivery of drugs into the lung because of incomplete API–carrier dissociation . Hence, ®) or porous particles called PulmoSpheres®  . T. T39]. TOverall, the influence of sugar properties on the flowing behavior of dry particles is summarized in Gordon and Taylor proposed an equation to explain the relation between the water content and the Tg of the system :Tg=Tgm.QIf the Tg of a component is high (Tgm), the Tg of the system (Tg) is also high, which means that the amount of water that needs to be removed is lowered to reach the glassy state. Moreover, this equation explains that the solid content (Qs) impacts the water content that needs to be removed during the drying process. The influence of the solid content on powder particles will be investigated in Another property that has to be considered for sugars used in inhalation powder formulation is whether they have a reducing character or not. This character does not impact the morphology or behavior of the powder in the DPI but rather the stability of the API in the final product. Indeed, reducing sugars interact with amine groups (Maillard reaction) of API which, consequently, decrease their stability. Many APIs such as budesonide and formoterol, widely used in asthma therapeutic schemes, are involved, as well as peptides and proteins ,42. AlmoBesides sugars, other excipients can enter in inhalation powder composition, mainly to help the dispersibility of dry microparticles or to protect microparticles from moisture and therefore promote lung deposition.Some materials used to form the outer shell of the particle are amino acids, among which the most well-known is L-leucine which has been reported to be present in many formulations to improve drug aerosolization performance ,43. Due The drying kinetics of a single droplet can be divided into two stages A 45,46],4645,46]D . In. In43]. Another important parameter which is, most of the time, neglected is the electrostatic charges of powder particles. The contact between particles or with the device induces an exchange or donation of electrons during the production or inhalation process. The intensity of particle charges is dependent on many factors such as the particle size, morphology, surface, or water content . To the The importance of excipient characteristics has been largely discussed in previous sections which explained how this considerably impacts the hygroscopicity, the amorphous content or the morphology of final DPI.Overall, it is clear that all parameters are closely related. While the atomization process influences the size of the particles, the size influences the aerodynamic diameter, itself influenced by the particle density or shape. Moreover, the choice of excipients determines the particle shape, the surface texture or the hygroscopicity. It seems very complicated to give numerical criteria that must be respected to produce DPI with high deposition performance even if trends can be found. Some physical parameters such as the size (0.5–5 µm), or technical factors such as solid content of the feed solution are well known to impact DPI performance. In this review, we have highlighted the importance of excipients’ characteristics in powder formulations in terms of Tg and hygroscopy, all parameters influencing the stickiness of the final powder. Moreover, it appears clearly that the morphology and surface texture of such small particles determine the aerosolization performance of powder particles.This review describes the composition and the related lung deposition performance of carrier-free powder particles and more precisely of composite-corrugated particles. We have noticed that these particles are characterized by a solid sugar core surrounded by a corrugated outer layer with the drug directly embedded in it. The requirement of amino acids to form a hydrophobic layer around the core of the particle is not always necessary. Indeed, due to different physicochemical properties , the migration of material within the atomized droplet is different, leading to either a smooth or a wrinkled structure with a rough surface. The interest in cyclodextrins combined with other sugars has also been highlighted. A big improvement of these new formulations could be the fact that two or more active ingredients can be combined and consequently reach the same lung deposition. Moreover, with non-reducing sugars, having a high transition temperature (Tg), the amorphous state at room temperature is stable, which increases the product stability.Regarding the morphology, the corrugated surface seems to be determinant in observing high deposition performance due to the reduction of interparticular forces, although more evidence is needed. Furthermore, additional studies are required to fully understand the importance of other particle properties such as the electrostatic charges. In the future, efforts should be made to ensure that a single excipient formulation can be used for different drugs, leading to the same pulmonary deposition.


Bodily self-perception is an important concept for several neurological disorders, including spinal cord injury (SCI). Changing one's bodily self-perception, e.g., via rubber hand illusion (RHI), induces alterations of bottom-up and top-down pathways and with this the connectivity between involved brain areas. We aim to examine whether (1) this process can be manipulated by changing cortical excitability, (2) connectivity between relevant brain areas differ when the RHI cannot be evoked, and (3) how this projection differs in a patient with SCI.  We applied RHI and facilitatory theta burst stimulation (TBS) on the right primary somatosensory cortex (S1) of 18 healthy participants and one patient with incomplete, cervical SCI. During RHI, we recorded high-density electroencephalography (HD-EEG) and extracted directed and nondirected connectivity measures.  There is no difference in connectivity between sham and real TBS or in the effectivity of RHI. We observed a higher laterality in the patient, i.e., higher connectivity of the right and lower of the left hemisphere. Besides this, connectivity patterns do not differ between healthy participants and the patient.  This connectivity pattern might represent a neuroplastic response in the attempt to overcome the functional impairment of the patient resulting in a similar overall connectivity pattern to the healthy participants, yet with a higher sensitivity towards RHI and a higher laterality. The cortico-cortical communication was not altered depending on whether the illusion was provoked or not; hence, the perceptory illusion could not be observed in the EEG analysis. One illusion approaching the visuo-tactile perception and the feeling of embodiment is the rubber hand illusion (RHI) [Humans understand an object rather as a whole structure, not as the sum of its single parts , 2. Thison (RHI) . In ordeon (RHI) . The diron (RHI) . The topon (RHI) . Still, on (RHI) . Some ston (RHI) , i.e., aon (RHI) , 9. For on (RHI) . This reon (RHI) . Pazzaglon (RHI) .Recent studies conclude that the multisensory signals induced by the RHI are being processed by a range of brain areas of the frontal, occipital, and parietal lobes , 13–15: It is important to gain a better understanding of the connectivity during RHI and its possible manipulation of the bottom-up and top-down processes. This could trigger new therapeutic approaches, which influence neuroplastic changes in patients with SCI and improve their sensory perception, as demonstrated in our earlier behavioral study . In ordeIn this study, we combined the RHI with facilitating TMS of the S1 and analyzed functional connectivity between the relevant brain areas in healthy participants as well as in one patient with SCI. Our aim was to investigate the effects of repetitive transcranial magnetic stimulation (rTMS) on bottom-up and top-down projections in healthy subjects and a patient with SCI. RTMS allows both for facilitating and inhibiting neural activity . We deciTwenty-one healthy participants were recruited, of which one terminated the study for personal reasons. Two further participants had to be excluded from data analysis as the quality of the EEG recording was not sufficient. In the final sample, 10 men and 8 women, with a mean age of 30 years (SD 9.2 years) were included. In three healthy participants, the illusion could not be elicited. Three patients with SCI were recruited, of which one had to be excluded as the TMS intensity had to be decreased to a minimum due to a hypersensitivity of the patient. In another patient, no MEPs could be elicited by the TMS; hence, the patient had to be excluded in the first session. One 64-year-old patient with an incomplete, nontraumatic SCI (AIS C) at a level sub-C4 was included in the study. The injury was caused by a Staphylococcus aureus sepsis, 6 months earlier.The study was approved by the local ethics committee , and all participants signed an informed consent form.The experiment consisted of four sessions, with intervals of at least one week in-between to avoid carry-over effects. In every session, the participants received rTMS stimulation, either real or sham, followed by a RHI, either sham or real during which HD-EEG was recorded . For theThe participants received visuotactile stimulation by timely synchronous brushing of the own hidden hand and a visible rubber hand that was placed in an anatomically reasonable position. The stimulation was performed for two minutes with a frequency of approximately 1 Hz, while in the sham condition, the real hand and the rubber hand were stimulated asynchronously with a delay of approx. 500 ms. During a successful RHI, the participant feels the brush touching his hand, while seeing the rubber hand being touched, causing the feeling of ownership of the fake hand. The effectivity of the illusion was tested via a standardized 9 items questionnaire filled out by the participant right after the illusion of every session. If at least 8 out of 9 items were rated with the lowest score in all four sessions, the illusion was regarded as not being successfully induced.The procedure of the RHI and the questionnaires were adopted from Botvinick and Cohens .We used a Power Mag research device by Mag &More GmbH and the software rTMS interface to program the TMS protocol. To test the individual resting motor threshold (RMT) and detect the motor hot spot of the first dorsal interosseous (FDI) of the right hemisphere, single TMS pulses were applied following the procedure described by Rossini et al. .We used iTBS to reach the maximum facilitating effect, consisting in a higher frequency delivered as three pulses with 50 Hz, given in ten trains for 2 seconds following 8 seconds of break. This is repeated 20 times, summing up to 600 pulses in 200 seconds [Ω. EEG data was first filtered between 0.1 Hz and 80 Hz using an FIR Bandpass filter. In addition, a 50 Hz notch filter was applied to account for line noise corrupting the data. Next, the data was segmented for each experimental condition into equally sized segments of 500 ms.EEG was recorded with 256-channel HydroCel geodesic sensor nets and a GES 400 amplifier . The data was recorded at 1000 Hz sampling rate using Philips' NetStation 4.5.6 software. Adhering to the proposed guidelines –23, impeArtifact rejection was performed on these segments using the artifact rejection tool within NetStation. Segments that were too much corrupted by artifacts were excluded from further analyses.Within Matlab , EEG chat-statistics for the original observations and for 10.000 permutation samples. Since the sample size was too small to achieve adequate power when correcting the p values for such a high number of tests, we chose to conduct global tests and only investigate further if those were significant. From this, we computed an individual p value for each set of variables, which we then combined using the Nonparametric Combination Methodology (NPC) described by Pesarin and Salmaso [We compared the sTBS with the rTBS condition and subjects in whom the RHI could be elicited (RHI) with those in whom it did not work (noRHI). For both comparisons, we took the mean overall segments of one condition within each person for every region × region × frequency combination. We then conducted permutation tests as follows: for the RHI vs. noRHI comparison, we computed absolute values of  Salmaso . As a co Salmaso . Statist Salmaso . The disp values show no difference of directed (ffDTF) and nondirected (COH) connectivity between real and sham TBS within the participants. Neither are there significant differences between the participants in whom the RHI could be elicited and the ones in whom it was not (The global  was not .Looking at the distribution of the patient's data within the data of the healthy control group, we observed rather distinctive trends. After sTBS, the patients' connectivity output is higher in the brain regions of the right hemisphere: M1, S1, PMC, and IPS, compared to the healthy control group, while the left M1 and S1 show lower connectivity (for values see The coherence analysis does not reveal any remarkable differences after sTBS. Yet after rTBS, the PMC and M1 show higher connectivity in the patient compared to the control group (for values see Supplementary Figures In this pilot study, we investigated the effect of RHI and repetitive TMS in healthy participants and one patient with cervical SCI on HD-EEG. In our earlier work , we demoReferring to the results discussed so far, the group of healthy participants in whom the RHI could not be elicited does not stand out. This suggests that the processes taking place during visuotactile stimulation are independent of the final outcome of body perception. The intensity and pattern of directed and nondirected connectivity that we recorded do not correlate with the final perception of whether the rubber hand is being integrated or not. It might rather reflect the mechanisms taking place when receiving multisensory inputs that generate contradictory information. Hence, the connectivity observed might reflect the mechanism of processing a prediction error, without influencing the bodily self-perception yet. This matches with the behavioral data of our earlier work  as the sThe fact that there is no significant difference in connectivity between the sham and real TBS stimulation is rather surprising. On the one hand, there might be an overlapping effect of the RHI over the TMS regarding communication of the brain areas. On the other hand, TMS does have an influence on the behavioral data of the study . Hence, Even though the patient shows similar patterns of connectivity as the healthy participants, some differences were found when plotting the data sets in percentiles. The patient shows lower connectivity in the left hemisphere and higher connectivity in the right hemisphere, compared with the data pool of all participants. Interestingly, lateralization has been also observed in a motor imagery study by Athanasiou and colleagues . They foA possible explanation for the lower connectivity of the left hemisphere might be that the right hand is not included in the RHI. It is placed on the lap and does not receive any sensory input, while the attention lies on the left hand. For these reasons, the right hand might have been rather neglected provoking a decrease in connectivity within the left hemisphere.However, the overall difference between the patient and the healthy control group seems to be small, pointing to a possible adjustment of connectivity and communication output after deafferentation. It was shown before that during movement preparation, there is a possible compensation in patients with SCI regarding the efficiency , represeMethodological limitations and technical restrictions of this study have to be considered when interpreting the results. First, the sample size of the group in whom the RHI could not be elicited is rather small, and the assessment of a single case of SCI is also not sufficient to draw definitive conclusions. The control group and the patient are not age matched which might have influenced the results. In SCI, the etiology varies a lot from patient to patient such that the sample would need to be very homogeneous—a general challenge to studies examining neuroplasticity in SCI. Due to the rather extensive study design and strict exclusion criteria, it was not possible in this case. Second, the relationship between TBS and the effectivity of the RHI can only be estimated according to behavioral data. Third, the data recorded during the RHI could not be compared with a resting condition. Finally, it cannot be excluded that HD-EEG is simply not suited to detect the fine-grained differences between brain activity in people who experience RHI and those who do not.In this study, the patient with SCI showed a more intense phenomenon of lateralization than the healthy participants, which might be due to a higher effectivity of RHI in the patient and consequently to a neglect of the right hand. Still, the overall intensity of connectivity and the patterns do not seem to differ, pointing to an equalization of overall brain activity in the patient. We could not show any difference in connectivity between sham and real TBS, and the connectivity of the investigated brain areas does not seem to correlate with the behavioral report of whether the rubber hand is integrated into the bodily self-perception or not. This leads to the conclusion that the observed cortico-cortical communication is not influenced by—and does not influence—the effectivity of the RHI.The results of this study shed new light on the processes taking place during visuo-sensory stimulation and raise further questions, such as how the illusion of integrating a fake body part is represented by brain activity, as assessed by using HD-EEG. Hence, it is still not clear to which extent bottom-up and top-down pathways contribute to the illusion. Further studies need to be conducted within larger samples of patients and healthy participants. One focus of future research could be functional connectivity patterns as biomarkers in patients with SCI, allowing for a better understanding of maladaptive neuroplasticity and individual rehabilitation approaches.


Herein, we present a patient who was diagnosed with trigeminal nerve injury following a pontine hemorrhage. A 38-year-old male was diagnosed with a left pontine hemorrhage and underwent conservative management at the neurosurgery department of a university hospital. After hemorrhage onset, he felt facial pain on the right side. After seven years, he visited the rehabilitation department of another hospital for evaluation of his right facial pain. He complained of somatosensory impairment and facial pain (tingling and cold sensation) on the right side as well as difficulty chewing and gait disturbance. On neurological examination, decreased touch sensation (approximately 30%) was observed on the right side of the face, in the oral cavity, and on the tongue (anterior two-thirds) as well as weakness of the right-sided masseter muscles. He also exhibitedallodynia without dysesthesia on the right side of the face. Diffusion tensor tractography showed the right trigeminal nerve to be discontinued at the anterior margin of the pons (arrow) compared to the state of the left trigeminal nerve.


While considering the deflagration regime, the thermal theory of combustion proposes that the mechanism of heat transfer from the flame exothermic zone to the front neighborhood reactants layer dominates the flame behavior. The introduction of the Fourier law allows a closed solution of the continuity and energy conservation equations to yield the burning velocity. It is, however, clear that this classical solution does not conform to the momentum equation. In the present work, instead of introducing the Fourier law, we suggest the introduction of a simplified version of the Onsager relationship, which accounts for the entropy increase due to the heat transfer process from the front layer to its successive layer. Solving for the burning velocity yields a closed solution that also definitely conforms to the momentum equation. While it is realized that the pressure difference across the flame front in the deflagration regime is very small, we believe that violating the momentum equation is intolerable. Quite a good fitting, similarly to the classic theory predictions, has been obtained between our predictions and some experimentally observed values for the propagation flame deflagration velocity, while here, the momentum equation is strictly conserved. Flame propagation in a flammable quiescent homogeneous medium under steady-state, steady-flow (SSSF) conditions may occur under two different discrete regimes: deflagration (sub-sonic), or detonation (super-sonic). In 1886 Mallard and Le-Chateliet were the first to observe how a flammable gas/air mixture that is ignited at the open end of a long tube propagates and develops along the tube. Since then, a large body of experimental observations has been collected for a number of fuels under a range of initial conditions. The two different regimes were clearly noted and a number of successful attempts were made to explain them. Some useful expressions have also been developed to enable reasonable predictions.−1 coordinate system. The intersections of these two curves define the different regimes. It can be shown that the slope of the Rankine–Rayleigh equation (linear equation) depends on the flame propagation velocity, which is an unknown parameter. It was suggested by Chapman and Jouguet [When the three conservation equations  are applied to a one-dimensional SSSF flame propagation process in a tube, it may soon be realized that in addition to a suitable equation of state, one more equation is needed to solve them . The fiv Jouguet  further  Jouguet . Some plIn the present work, we suggest a new approach to bridge this classic gap. Instead of introducing the Fourier law, we suggest introducing the Onsager relationship , which aConsider a combustion wave propagating in SSSF conditions through a combustible mixture in a two open-sided, open-ended adiabatic stationary tube. The wave is opposed by the reactants (unburned gases) flowing at a velocity exactly equal to the wave propagation velocity, so that the wave is fixed with respect to the tube. Following the classical theory, while neglecting any transport phenomena in the fluid , we may apply the following conservation equations between the tube entrance (reactants) and the tube exit (products):Continuity:Momentum:R and P indicate reactant and product, respectively. Further we assume that both the reactants and products follow the ideal gas law, thus:Energy:We have now four equations with five unknowns: the temperature, pressure, density and velocity of the products, and the reactants’ velocity. The classical theory suggests that if we further assume invariant gas constant across the flame and identical heat capacity for the reactants and products, Equations 1)–(5) may be easily combined to yield the following two relationships [–(5) may The Rayleigh equation:The Hugoniot equation:It was proposed by Mallard and Le Chatelier that the structure of the deflagration wave  suggests that the flame propagation under the deflagration regime is controlled mainly by the heat transfer from the flame front exothermic layer to the fresh reactants’ layer ahead of it . SemenovWhile assuming a linear temperature profile and ignoring the mass diffusion, the classical thermal theory  suggestsExperimental observations confirmed the velocity of the deflagration wave as predicted by Equation (8). It can be shown however that this classic simple solution does not conform to the momentum equation [, p. 150 −1, concentration, velocity, etc.). Thus:As an alternative to the Semenov group’s methodology, we propose in the present work a different approach to bridge this gap. Instead of introducing the Fourier law, we suggest introducing the Onsager relationship, which accounts for the entropy increase due to the heat transfer process from the front layer to its successive layer  represents the entropy increase from reactants to products. This term is obtained while integrating the thermodynamic relationship Solving for δ. Following the classic thermal theory, in which the heat generation is compared to the heat convection, the flame thickness can be easily expressed in terms of the rate of heat generation, which include an Arrhenius-type expression [Equation 15) correlates the flame laminar velocity with three unknowns: 5 correlaSome important results of the present calculations are depicted in For typical values of a stoichiometric hydrocarbon/air mixture, the right term of Equation (15), is in the order of one. Moreover, since While the present approach ensures that the momentum basic law is conserved, Equation (16) suggests that the end results for the burning velocity do not noticeably differ from the value as predicted by the classical theory. For a typical hydrocarbon flame, the ratio is close to one.The thermal theory of combustion proposes that for deflagrating flames the mechanism of heat transfer from the flame exothermic zone to the front neighborhood reactants layer dominates the flame behavior. The introduction of the Fourier law provides the additional required equation that after some plausible assumptions allows a closed solution to yield the burning velocity. It is however clear that this classical solution does not conform to the momentum equation. In the present work, instead of introducing the Fourier law, we suggest introducing a simplified version of the Onsager relationship, which accounts for the entropy increase due to the heat transfer process from the front layer to its successive layer. In this way, a closed solution that definitely conforms to the momentum equation has been obtained. It is anticipated that the pressure difference across the flame front in the deflagration regime is very small, but still, violating the momentum equation can hardly be accepted. Unsurprisingly, quite a good fitting has been obtained between the predicted and the experimentally observed values of the propagation flame deflagration velocity, while the momentum equation is strictly conserved.


Chitosan is a polymer that is extensively used to prepare nanoparticles (NPs) with tailored properties for applications in many fields of human activities. Among them, targeted drug delivery, especially when cancer therapy is the main interest, is a major application of chitosan-based NPs. Due to its positive charges, chitosan is used to produce the core of the NPs or to cover NPs made from other types of polymers, both strategies aiming to protect the carried drug until NPs reach the target sites and to facilitate the uptake and drug delivery into these cells. A major challenge in the design of these chitosan-based NPs is the formation of a protein corona (PC) upon contact with biological fluids. The composition of the PC can, to some extent, be modulated depending on the size, shape, electrical charge and hydrophobic/hydrophilic characteristics of the NPs. According to the composition of the biological fluids that have to be crossed during the journey of the drug-loaded NPs towards the target cells, the surface of these particles can be changed by covering their core with various types of polymers or with functionalized polymers carrying some special molecules, that will preferentially adsorb some proteins in their PC. The PC’s composition may change by continuous processes of adsorption and desorption, depending on the affinity of these proteins for the chemical structure of the surface of NPs. Beside these, in designing the targeted drug delivery NPs one can take into account their toxicity, initiation of an immune response, participation (enhancement or inhibition) in certain metabolic pathways or chemical processes like reactive oxygen species, type of endocytosis of target cells, and many others. There are cases in which these processes seem to require antagonistic properties of nanoparticles. Products that show good behavior in cell cultures may lead to poor in vivo results, when the composition of the formed PC is totally different. This paper reviews the physico-chemical properties, cellular uptake and drug delivery applications of chitosan-based nanoparticles, specifying the factors that contribute to the success of the targeted drug delivery. Furthermore, we highlight the role of the protein corona formed around the NP in its intercellular fate. Chitosan is a naturally occurring biopolysaccharide obtained by N-deacetylation of chitin. Chitosan has been widely studied in the pharmaceutical and biomedical fields due to its excellent biological effects including anti-inflammatory , antifunChitosan nanoparticles (NPs) have multiple roles in the pharmaceutical and medical areas, such as wound healing , drug, gNPs have outstanding features such as prolonged systemic circulation, high preferential retention at tumor sites and the ability to overcome P-glycoprotein-mediated multidrug resistance of cancer cells . There aNanoparticles entering a biological environment undergo surface modification owing to dynamic physicochemical interactions with proteins and other biomolecules in the extracellular fluids. The adsorption of plasma proteins, lipids and other molecules on the surface of NPs leads to the formation of a stratum of these molecules, commonly called ‘protein corona’ (PC), as the majority of the adsorbed molecules are proteins ,22 see .The chemical structure of the PC and the kinetics of changes of its configuration depend on the affinities of proteins towards the functional groups located on the surface of NPs, but also on the proteins’ concentration. Proteins that adsorb with high affinity form the “hard” corona—which consists of tightly bound proteins that are not easily desorbed—and proteins that adsorb with low affinity form the ‘soft’ corona, which consists of poorly bound proteins ,22,23 s.In fact, the proteins that construct the “hard” corona will form the first layer on the surface of NPs in several hours and will have a small desorption rate. The proteins from the “soft” corona interact mostly with the proteins from the “hard” corona, forming the second layer. The “hard” corona does not completely cover the NP’s surface, hence even proteins with low affinity have access to some functional groups located on the NP surface. These “soft” proteins have a higher exchange rate, as their affinities toward the functional groups of NPs surface is low. Even if the proteins’ concentration in biological fluid is very low, the proteins will tend to cover the NP’s surface as much as possible. Virtually, a complete surface coverage of the NP will take place. As “soft” proteins interact with the “hard” corona via weak protein-protein interactions, it may be possible for these interactions to take place even in the absence of NPs in a biological fluid . There aThe thickness of the “hard” and “soft” protein corona layers depends on particle size, surface properties, but also on protein concentration and composition. As the hydrodynamic diameter of most proteins present in plasma ranges between 3 and 15 nm, the thickness of the “real” NPs covered with protein corona is too large to be composed of only a single layer. In fact, even the “hard” corona—made by “primary binders” that are supposed to be proteins adsorbed directly on NP surface— and the “soft” corona—made by “secondary binders” that associate with the primary binders via protein-protein interactions—are made from several layers of proteins, depending on the proteins’ concentration in the biological fluid . The exaThe lifespan of the NP-proteins complex ranges from microseconds to days, depending on the kinetics of association/dissociation which are influenced by the protein concentration and composition of the physiological fluid. In biological fluids with lower protein concentration  the NP surface will be covered chiefly with lower affinity proteins, while in blood—where the protein concentration is very high—the composition of the protein coating may suffer changes in time due to differences in concentration and affinities of proteins. Shortly after the introduction of a NP in blood, the surface will be covered mostly by serum albumin and fibrinogen, which are present in higher concentration in blood. These proteins may be switched in time with proteins present in a lower concentration but having a higher affinity toward the NP material .The formation of PC on NPs increases their hydrodynamic diameter. If the core of a NP is made from one type of polymer and the shell (cover surface) from another, the increase of the hydrodynamic diameter is more noticeable. For instance, the hydrodynamic diameter of NPs made from PLGA alone increases with 50% due to PC formation and with more than 150 % when the PLGA particle is covered with chitosan before being put in contact with plasma proteins . The PC Understanding of the PC formation mechanism is essential in predicting the behavior of NPs in biological environments, for nanotoxicology applications and development of drug delivery nanosystems ,32. The The critical physico-chemical parameters that influence the formation of the protein corona are the type, size, shape, surface curvature, surface charge, hydrophobicity, polydispersity index and zeta potential. An overview of these parameters and the type of the target cells is presented in The nanoparticle size is an important parameter for all NPs designed for medical applications. Tahara et al.  presenteThe size of chitosan-based NPs prepared by various methods presented in The use of negatively charged polymers to cover/interact with chitosan-based NPs can alter both the size of the particles and their general charge . For example, when chitosan-based NPs were coated with hyaluronic acid (HA) the size of particles increased from 170 nm to 270 nm and the positive charges of the chitosan polymer were shielded by the negatively charged HA. This effect is even more evident when chitosan-based NPs adsorb alginate (Alg) due to higher charge density of Alg in comparison with HA. At the same time, the size of chitosan-based NPs coated with Alg increased three times in comparison with particles coated with HA .Dramatic changes are observed when the size of the NP is approaching the size of proteins. In this case the formation of a second layer of proteins is almost overlooked . As the For NPs larger than 30 nm there is a direct correlation between their specific surface area and the amount of adsorbed proteins. It was shown that at a constant particle weight, the amount of adsorbed plasma proteins increases with the surface area when the same type of NPs—varying in size between 70 and 700 nm—were used . On the The surface curvature is directly affected by the NP size, which affects the structure of the adsorbed proteins or peptides . NPs of The surface curvature facilitates the adsorption and conformation change of proteins and therefore the composition of the PC . The binNanoparticle shape is another important parameter which controls composition and overall PC formation . The shaThe properties of NPs depend on surface charge. Methods to modify this parameter are used to tailor the final properties of NPs. These modifications may produce also alterations in stiffness that can raise supplementary problems in reproducibility and predictability of properties of final product.The interactions between the protein molecules and the NP charges have a pronounced influence on the adsorption kinetics and the composition of the PC. The kinetics of the adsorption/desorption process depend on various properties of the NPs and on the chemical composition of the blood or other biological fluids. Roughly, in the initial stage, the molecules having the highest concentration in the biological fluid will be adsorbed on the surface of NP, but later they may be replaced by those molecules (proteins) with higher affinity towards NP surface functional groups .It has been shown that negatively charged NPs with a high surface charge density can enhance adsorption of plasma proteins . ParticlAfter the contact of NPs with plasma, a decrease of charge density with immediate binding of proteins with opposite charges occurs . The corThe heterogeneity of a sample based on size is measured by the polydispersity index (PDI) . PolydisHydrophobic and hydrophilic interactions significantly affect and attenuate the adsorption and desorption of proteins over a NP surface. As hydrophobic interactions are always attractive, hydrophobic NPs adsorb more proteins than hydrophilic NPs and are often involved in the denaturation and conformational change of surface-adsorbed proteins . ApolipoAnother essential factor in the interaction of NPs with proteins is the pH. The protein binding affinity of the NP is affected by the change of the environmental pH, and it can lead to a different pattern of protein adsorption. In the cellular uptake pathway nanomaterials may encounter various biological fluids with different pH values, such as blood (pH 7.4), exposure media (pH 6.9–7.4), intracellular fluids (pH 6.8), and lysosomes (pH 4.5–5). Moreover, the acidic environment of tumors contains specific types of proteins which could modify the PC, changing the bioavailability and therapeutic effect of NPs . ChitosaOne important parameter that characterizes NPs  is the zeta potential. Its value can be positive or negative and represents an appreciation of the stability of the NPs in the given environment. At low values of zeta potential the system is unstable and the NPs can cluster and precipitate. As a rule, when the zeta potential is larger than 20 (either positive or negative) there is a good chance that the system is stable, i.e., the NPs are in suspension in the liquid microenvironment . Figure Considering the interaction of NPs with cells or cell components, the sign of the zeta potential becomes very important as the binding of NPs to cells can be accomplished by electrostatic interactions. For example, Park, Han  reportedThe types of proteins that will be adsorbed on NPs surface depend on the NP charge and this can be changed if a specific target application is desired. Kim et al. presents an example when PLGA/PVA NPs having a negative zeta potential (−30.1 ± 0.6 mV) were coated with chitosan that led to a positive zeta potential (+26 ± 1.2 mV). The positive charge enhanced the uptake of NPs coated with chitosan by H157 cell types .Is has to be taken into account that when the zeta potential of chitosan-based NPs is measured in phosphate buffers, a shift toward neutral values takes place due to the interaction between positive charges of the chitosan polymer with the negatively charged phosphate ions . In thesThe PC composition changes over time depending on the chemical structure and other NP properties. Due to the complexity of interactions between NPs and proteins in the biological fluid, there is no universal PC available for all types of NP and no predictions can be made about the PC structure, even if the chemical composition of the biological fluid is known. We can say that the composition of the protein corona is unique for each type of NP and its determination is an experimental trial and error process. The composition depends on the physicochemical properties of NPs , on the nature and chemical composition of the physiological environment  and the time of exposure and interaction .The “hard” corona structure can last for many hours, enough to influence many biological and physiological cellular processes , althougUpon entering the body, the NPs are recognized as external units and are removed from the blood circulation. After the chitosan-based NPs enter in the blood system the following events occur: (1) fast formation of PC by association of NPs with plasmatic proteins; (2) rearrangement of proteins in “hard” and “soft” corona layers; (3) possible activation or inactivation on metabolic enzyme cascades; (4) induction of immune response and induction of recognition process of PC by immune cells; (5) initiation of macrophage uptake of NPs coated with PC and their elimination from the bloodstream; (6) subsequent accumulation of NPs coated with PC in some specialized cells (excluding macrophages); (7) eventually initiation of some signaling pathways and apoptosis processes ,76,77.Among the mechanisms that assist the entrance of NPs into the cells are pinocytosis, caveolae or clathrin assisted endocytosis and caveolae/clathrin-independent endocytosis, phagocytosis. The process of entrance of NPs in the cells depends on the properties of NPs, among them the size being the most important. It is believed that NPs smaller than 120 nm adhere to endocytic uptake, while NPs bigger than 500 nm enter in the cells by phagocytosis. Sometimes, the NPs can agglomerate and are therefore capable of being phagocytized. Moreover, the “real” size of the particle is modified due to protein adsorption . Table 2Although for biological derived entities  the entrance in the cell is generally governed by energy-dependent saturable endocytic pathways (like receptor-mediated endocytosis), the entrance of artificial entities (like NPs) is mostly an adsorptive type of endocytosis. For NPs with a chitosan polymer on their surface (either the NPs are made from chitosan or the NPs are only coated with chitosan) the interaction with cell membranes is mainly based on nonspecific attractive electrostatic forces  as the eIn the research stage of chitosan-based NPs, it is necessary to label the NPs in order to track their trafficking, usually with a fluorochrome. This introduces an additional interference in the normal process of uptake and drug delivery .The fate of NPs in the body depends on the type of tissues where these NPs end their journey. In the gastrointestinal tract (GI), the fate on the NPs depends on: (1) diffusion through the mucus, which may be considered as the first barrier of ingested NPs to their effective entrance in the body, (2) the way the NPs interface with the epithelium, that may be considered as the second barrier and (3) the process of translocation of NPs into the cells. These processes are governed by the diffusion of NPs through agglomerated media, consisting of mucin like proteins with gel-forming characteristics that tend to reduce the rate of diffusion of NPs. As in all diffusion cases, the velocity of NPs depends on the size of particles and on the density of the matrix. An additional factor that may influence the diffusion rate of chitosan-based NPs (or NPs coated with chitosan) into the GI tract is the negative charge of highly glycosylated extracellular proteins that form this matrix, which supports the dispersion of positively charged particles . The fatThe surface charge of NPs can determine the process of entrance in the cell and their fate into the cell. Yue  performeAnother example of changing the fate of NPs is presented by Amoozgar et al. who covered polyethylene glycol (PEG) NPs with low molecular weight chitosan (LMWC) . The chiSome studies have stated that NPs can induce cell death as consequence of disturbance of cellular, subcellular and genetic behavior, i.e., the disruption of integrity of the plasma membrane, damage of mitochondria and nucleus. The key factor in cytotoxicity of NPs seems to be the oxidant and antioxidant cellular processes initiated by NP interaction with enzymes involved in these processes. More exactly, NPs can induce the formation of reactive oxygen species (ROS). Oxidative stress is caused by an imbalance between the process of forming ROSs and the processes by which ROSs are destroyed or their effects annihilated. All organisms that obtain their metabolic energy by respiration have mechanisms of repair of destruction caused by ROS presence and mechanisms that maintain a reducing cellular environment. Imbalances of normal redox state can lead to toxic effects by producing peroxides and free radicals that affect all cell components, including proteins, lipids and nucleic acids. The main cellular substructures and metabolic pathways affected are: (1) all biological membranes (2) the flow of electrons in mitochondria and leakage from the inner mitochondrial membrane (3) calcium ion levels in the endoplasmic reticulum. The production of ROS by NPs depends on their chemical reactivity and on the NP interaction with the cellular structures involved in reparative processes ,82.NPs smaller than 100 nm are potentially the most dangerous because of their surface/volume ratio, relative ease of penetration into cells, and relatively high content of substances that can participate in oxidation-reduction cycles. The NPs interfere with the ability of macrophages to remove foreign particles, as phagosomes are incapable to engulf long and stiff structures. To eliminate them, the cells produce and excrete larger quantities of hydrolytic enzymes and oxygen radicals, which will install and maintain a chronic inflammation. This situation is similar with that of asbestos fibers, when chronic inflammation led to mutagenesis .Beside the structure of NPs, including the possible presence of a different polymer used to coat the core of NPs, the structure of the PC greatly affects the cellular uptake and internalization processes. Covering NPs with physiological proteins can shield the charge of the plain NPs and can influence the fate of these particles in relation with different types of cells. In some cases an enhancement of cellular uptake was reported and in others an inhibition was observed ,84,85. FAs described in previous sections, the formation of PC changes the NPs’ properties and behavior. Controlling the behavior of the NP in the body can be done by controlling the formation of the PC. If the interactions between NP and proteins from the biological fluids are not controlled, then NPs can be toxic or dangerous, as such a nanosystem can enhance phagocytosis processes, activate enzymatic cascades in an unpredictable way, can accumulate in the body or can be expelled. In order to use NPs in medicine , one should control the properties of NPs after entrance in the body, the formation of PC, the physiological response toward modified NPs . Most ofAs the NPs introduced in biological fluids interact with molecules presented in this microenvironment—chiefly with proteins—and form PCs with properties closely depending on the type of the adsorbed molecules, the surface of the NPs can be, to some extent, tailored to adsorb only those proteins that will meet some requirements . For example, Almalik et al. made chitosan-based NPs and particles with HA or Alg on their surfaces and compared the behavior of these three types of NPs in the presence of biological fluids. The presence of HA conducted to a reduced adsorption of proteins and to a meagre production of proinflammatory proteins. The HA present on the surface of chitosan-based NPs promote the differential adsorption of anti-inflammatory proteins ITIH4 and AGT, which were not detected on the PC of the other two types of particles. In counterpart, the PC of particles without HA, chitosan NPs and Alg-chitosan NPs selectively contained the proinflammatory protein clustrin .A similar strategy of modifying the surface of the NP was applied to particles having the core formed by polyethylene glycol and, this time, the surface characteristics were modified by adding low molecular weight chitosan (LMWC), which made the resulting NPs positively charged. The reason for such alterations was to change the interaction between NPs and the target cells for the purpose of drug delivery. In the mildly acidic pH in the microenvironment of cancer cells the presence of LMWC permits the rapid uptake of NPs into cells and release of the drug due to the presence of some factors or conditions that disorganize the structure of the particle surface or hydrolyze the polymer. Amoozgar et al. present the case where fragments of 2 to 22 kDa of chitosan were released from the surface of NPs (the core made from poly(lactic-co-glycolic acid) by hydrogen peroxide digestion with subsequent release of the drug contained in the structure of the particle .Changing the chemical structure of NPs or at least of their surface is demanded by the targeted application and in close correlation with the fate of the transformed NP once the target tissue is located. In drug delivery—one of the most studied applications—NPs are designed to reach cancer cells without affecting normal cells. NPs having a core made from PLGA—used to load and retain drugs—were coated with LMWC in order to have a positively charged surface to the uptake into tumor cells. This ensures that benefits are provided by both polymers. PLGA’s hydrophilicity leads to a better control of adsorption and release of the drug while the positive electric charge of LMWC ensures the control of uptake by tumor cells in their mildly acidic environment .In experiments on murine ascites hepatoma H22, the use of NPs to deliver doxorubicin as an antitumor agent has been proven to reduce the toxicity of the drug and to increase the survivability of the mice. In this case the chitosan-based NPs were conjugated with BSA-dextran and loaded with doxorubicin, adsorption favored at pH 7.4 by the presence of BSA on the surface of NPs .Nam et al. reported the use of hydrophobically modified glycol chitosan NPs carrying 5ß-cholanic acid, an antitumor agent. These particles, having an average diameter of 359 nm and a zeta potential of 22 mV, manifested an enhanced distribution in the whole cells compared with parent hydrophilic NPs. Furthermore, the modified NPs presented better biocompatibility and lower toxicity .In a recent study, chitosan-based NPs were modified by covalent linking to chitosan chain cysteine and folic acid and loaded with methotrexate, an anticancer agent. The linked molecules granted redox responsiveness and active targeting of folate receptors properties to the NPs. When used in HeLa cancer cell cultures these modified NPs presented an elevated level of reductive agents in the microenvironment of tumor cells and a controlled methotrexate release directly to this target site due to a possible overexpression of folate receptors. These properties are somehow related as in the reduced microenvironment of the cancer cells the encapsulated methotrexate was rapidly released (free methotrexate has poor water solubility and low bioavailability) .SN-38—an antineoplastic drug 1000 times more active than the irinotecan from which it derives—conjugated with HA was used as shell of chitosan-based NPs decorated with MUC1  aptamer. NPs bearing MUC1 were taken up with increased efficiency in a HT29 cell culture. Unlike those lacking the drug, NPs bearing SN-38 exerted cytotoxicity through apoptosis. When both types were preincubated with bovine serum albumin that formed a PC, none of the particle types showed cytotoxicity .Chitosan was added in various ratios to PLGA (poly(lactic-co-glycolic acid)) NPs to fine-tune the delivery of the carried drug. Lu et al. prepared chitosan—PLGA particles, mixing these components in various proportions by using a high-gravity rotating packed bed method. The addition of chitosan increased the size of the particles (from 132 to 172 nm), zeta potential (from -20 to 25 mV) and encapsulation efficiency (from 65% to 87%). In the meantime the drug release was slowed down from 66% to 14%, measured after 2 h. Particles with chitosan released the drug faster at pH 5.5 than 7.4 .Another example of tuning the properties of NPs used as a carrier for drug delivery was presented by Aldawsari et al., who prepared PLGA NPs coated with chitosan. The carried drug was resveratrol, a well-known polyphenolic compound with many applications and anti-inflammatory, antioxidant and anti-cancer activities. Elevating the chitosan concentration led to an increase in particle size (up to 340 nm), zeta potential , entrapment efficiency (up to 75%), aid in the stability of the drug in NPs. Compared with free resveratrol, the PLGA chitosan-coated NPs carrying this drug presented higher cytotoxicity and apoptotic activities .Similar NPs, with PLGA core and coated with chitosan, but obtained by another technique (water-oil-water emulsion of poly(DL-lactic-co-glycolic acid) solvent evaporation, followed by surface-modified by adsorption of chitosan) were used as a drug carrier for pulmonary administration (human lung adenocarcinoma cells A549). Compared with NPs made only from PLGA, the particles coated with chitosan manifested low cytotoxicity and were a better carrier of the drug, the uptake was higher and the release of the drug was slower. At least in the case of A549 cells, both types of NPs were taken up in an energy-dependent manner by a clathrin-mediated endocytic process .Aiming to enhance the biocompatibility of curcumin towards A549 cells, the drug was loaded on NPs made from biotin-chitosan-dithiodipropionic acid-curcumin. The addition to these NPs of a type of phycocyanin-functionalized corona gives them the potential to avoid protein adsorption in blood circulation. The release of curcumin from the redox responsive shells of the NPs was sensitive to a high concentration of glutathione. The enhanced intracellular uptake of curcumin increased the inhibitory activity on the proliferation of A549 cells .Reduction-responsive HA–chitosan–lipoic acid nanoparticles (HACSLA-NPs) were designed and synthesized for effective treatment of breast cancer. The nanodelivery system was used to target CD44-overexpressing cells and release of reduction-triggered 17-methyltestosterone (MT) for systemic delivery. In vitro experiments showed that MT/HACSLA-NPs presented a fast drug release in the presence of glutathione, while the absence of glutathione led to a sustained MT release. HACSLA-NPs showed higher cellular uptake via CD44 receptors, rapid release of the drug inside the cells, and increased cytotoxicity against BT-20 cell lines as opposed to MCF-7 cell lines. Both MT/CSLA-NPs and MT/HACSLA-NPs were highly efficient in targeting breast cancer cell lines, but MT/HACSLA-NPs presented higher selectivity, cytotoxicity and apoptotic effect on BT-20 cells compared to MT/CSLA-NPs .An additional application of chitosan-based NPs is the use of these particles as vectors to transfect the cells. Douglas et al. have shown the transfection efficiency of fluorescently labeled alginate-chitosan NPs complexes, at least in the case of some cell lines (293T and COS7) that possess the clathrin-mediated endocytosis pathway .Robles-Planells et al. evaluated the effects of the expression of a fusogenic protein from the infectious salmon anemia virus (ISAV-F) on a murine B16 melanoma model, in vitro as well as in vivo, using chitosan NPs as transfection vectors. The transfection of B16 murine tumor cells with ISAV-F-loaded chitosan NPs allowed the expression of the ISAV-F protein and lowered cell viability. The in vivo transfection slightly delayed tumor growth. Expression of ISAV fusion protein using chitosan NPs induced fusion of melanoma cells and slight in vivo antitumoral effect in comparison to chitosan treatment .Another interesting application of chitosan-based NPs is their use as a core that was covered with pluronic F127 and finally used to transport insulin. Pluronic F127 is composed of triblock PEO–PPO–PEO copolymers of poly(ethylene oxide) (PEO) and poly(propylene oxide) (PPO). In an aqueous environment, these block copolymers self-assemble into micelles with a hydrophobic PPO oriented toward the interior and a hydrophilic PEO oriented toward the water. In a study of the applications of these special NPs insulin was nested inside, where it was protected by enzymatic degradation—which drugs carried by orally administrated NPs may suffer on their way to the target tissue. The cellular level of insulin after its administration into the chitosan-pluronic particles was 36-fold higher compared with free insulin and 10-fold higher compared with particles made only from chitosan. Several other properties have also been improved by carrying insulin with chitosan-pluronic particles: efficiency of mucosal penetration, cellular internalization of insulin in mucus-secreting E12 cells, permeation of insulin across the ileum epithelia, hypoglycemic effects in diabetic rats, and so on .H. pylori, based on the antimicrobial properties of the polylysine peptide. NPs having the core of BSA and surface of chitosan have been proven to present the best antimicrobial activity, mucoadhesion and to better release the polylysine peptide, at least in simulated gastric conditions. Considering the special conditions of the gastrointestinal cavity, the “reversed” NPs proposed by Niaz, i.e., with the core of protein (BSA), covered with chitosan and on top with polylysine adsorbed on chitosan surface, although atypical, seems to give good results . There are published results based on typical layer disposition, i.e., core of chitosan-base (more precisely thiolated chitosan/PMLA) having adsorbed antibiotics (amoxicillin) on the surface of NPs used for the delivery of the drug into the gastrointestinal cavity to treat H. pylori [Another well documented application of NPs for drug delivery is the use of NPs for antibiotic delivery as near as possible to the target cells. For this, the core of NPs or at least the coverage of the core is tailored to interact in a predictive matter with the most abundant proteins located in the same place as the target cells. The adsorption and delivery of the drug was then studied mimicking the conditions from the near environment of the target cells. For example, Niaz et al.  reported. pylori ,91.Chitosan was used in an NP drug delivery system for the controlled release of hydrophobic amiodarone (AMD) along with the cyclic oligosaccharide β-cyclodextrin, which increases the solubility of hydrophobic molecules in water. Amiodarone-loaded chitosan nanoparticles were prepared using the ionic gelation method with a reaction yield of 11–15%, an encapsulation efficiency of 33–36% and a loading capacity of 8–9%. In this in vitro release study, all of the AMD was released after 14 days, 38% at the end of day 1 and 50% at the end of day 5. The authors concluded that the AMD-loaded chitosan NPs might be used for long-term treatment with AMD and could be a model for controlled delivery of other antiarrhythmic drugs .Depending on the application of the NPs, their core, surface and to a small extent, the composition of the PC in biological media may be tailored. For most drug-carrying NPs, the size and the particle charge are the main parameters that have to be tuned to obtain a good uptake of the carried drug to the target cells.The type of the target cells is important due to the differences between the proteins, polysaccharides and other types of molecules carrying electric charges located in plasma membranes of the target cells and due to different types of endocytosis mechanisms. The smaller particles travel faster through mucus than larger ones due to diffusion. When enterocytes are the target cells, the smaller the size of NPs, the quicker is their transport through the intestinal mucosa.The PC can be formed by adsorption on the NPs surface of proteins (and other types of molecules) found in the biological fluids through which the drug-carrying particles travel towards the target cell. The type and the concentration of proteins in the PC are constantly changing depending not only on the size, composition and charge of NPs but also on the duration of the trip.Chitosan-based NPs are mainly used for the delivery of anticancer drugs to tumor cells. Other uses include delivering antibiotics closer to the target tissue, insulin transport, DNA transfection and others.