Transnasal targeted delivery of therapeutics in central nervous system diseases: a narrative review

Currently, neurointervention, surgery, medication, and central nervous system (CNS) stimulation are the main treatments used in CNS diseases. These approaches are used to overcome the blood brain barrier (BBB), but they have limitations that necessitate the development of targeted delivery methods. Thus, recent research has focused on spatiotemporally direct and indirect targeted delivery methods because they decrease the effect on nontarget cells, thus minimizing side effects and increasing the patient’s quality of life. Methods that enable therapeutics to be directly passed through the BBB to facilitate delivery to target cells include the use of nanomedicine (nanoparticles and extracellular vesicles), and magnetic field-mediated delivery. Nanoparticles are divided into organic, inorganic types depending on their outer shell composition. Extracellular vesicles consist of apoptotic bodies, microvesicles, and exosomes. Magnetic field-mediated delivery methods include magnetic field-mediated passive/actively-assisted navigation, magnetotactic bacteria, magnetic resonance navigation, and magnetic nanobots—in developmental chronological order of when they were developed. Indirect methods increase the BBB permeability, allowing therapeutics to reach the CNS, and include chemical delivery and mechanical delivery (focused ultrasound and LASER therapy). Chemical methods (chemical permeation enhancers) include mannitol, a prevalent BBB permeabilizer, and other chemicals—bradykinin and 1-O-pentylglycerol—to resolve the limitations of mannitol. Focused ultrasound is in either high intensity or low intensity. LASER therapies includes three types: laser interstitial therapy, photodynamic therapy, and photobiomodulation therapy. The combination of direct and indirect methods is not as common as their individual use but represents an area for further research in the field. This review aims to analyze the advantages and disadvantages of these methods, describe the combined use of direct and indirect deliveries, and provide the future prospects of each targeted delivery method. We conclude that the most promising method is the nose-to-CNS delivery of hybrid nanomedicine, multiple combination of organic, inorganic nanoparticles and exosomes, via magnetic resonance navigation following preconditioning treatment with photobiomodulation therapy or focused ultrasound in low intensity as a strategy for differentiating this review from others on targeted CNS delivery; however, additional studies are needed to demonstrate the application of this approach in more complex in vivo pathways

Smart sensor systems for wearable electronic devices

Wearable human interaction devices are technologies with various applications for improving human comfort, convenience and security and for monitoring health conditions. Healthcare monitoring includes caring for the welfare of every person, which includes early diagnosis of diseases, real-time monitoring of the effects of treatment, therapy, and the general monitoring of the conditions of people's health. As a result, wearable electronic devices are receiving greater attention because of their facile interaction with the human body, such as monitoring heart rate, wrist pulse, motion, blood pressure, intraocular pressure, and other health-related conditions. In this paper, various smart sensors and wireless systems are reviewed, the current state of research related to such systems is reported, and their detection mechanisms are compared. Our focus was limited to wearable and attachable sensors. Section 1 presents the various smart sensors. In Section 2, we describe multiplexed sensors that can monitor several physiological signals simultaneously. Section 3 provides a discussion about short-range wireless systems including bluetooth, near field communication (NFC), and resonance antenna systems for wearable electronic devices

CoAPT: Context Attribute words for Prompt Tuning

We propose a novel prompt tuning method called CoAPT(Context Attribute words in Prompt Tuning) for few/zero-shot image classification. The core motivation is that attributes are descriptive words with rich information about a given concept. Thus, we aim to enrich text queries of existing prompt tuning methods, improving alignment between text and image embeddings in CLIP embedding space. To do so, CoAPT integrates attribute words as additional prompts within learnable prompt tuning and can be easily incorporated into various existing prompt tuning methods. To facilitate the incorporation of attributes into text embeddings and the alignment with image embeddings, soft prompts are trained together with an additional meta-network that generates input-image-wise feature biases from the concatenated feature encodings of the image-text combined queries. Our experiments demonstrate that CoAPT leads to considerable improvements for existing baseline methods on several few/zero-shot image classification tasks, including base-to-novel generalization, cross-dataset transfer, and domain generalization. Our findings highlight the importance of combining hard and soft prompts and pave the way for future research on the interplay between text and image latent spaces in pre-trained models.Comment: 14 pages, 4 figure

Deep Learning for Counting People from UWB Channel Impulse Response Signals

The use of higher frequency bands compared to other wireless communication protocols enhances the capability of accurately determining locations from ultra-wideband (UWB) signals. It can also be used to estimate the number of people in a room based on the waveform of the channel impulse response (CIR) from UWB transceivers. In this paper, we apply deep neural networks to UWB CIR signals for the purpose of estimating the number of people in a room. We especially focus on empirically investigating the various network architectures for classification from single UWB CIR data, as well as from various ensemble configurations. We present our processes for acquiring and preprocessing CIR data, our designs of the different network architectures and ensembles that were applied, and the comparative experimental evaluations. We demonstrate that deep neural networks can accurately classify the number of people within a Line of Sight (LoS), thereby achieving an 99% performance and efficiency with respect to both memory size and FLOPs (Floating Point Operations Per Second)

Deciphering Molecular Mechanism of Histone Assembly by DNA Curtain Technique

Chromatin is a higher-order structure that packages eukaryotic DNA. Chromatin undergoes dynamic alterations according to the cell cycle phase and in response to environmental stimuli. These changes are essential for genomic integrity, epigenetic regulation, and DNA metabolic reactions such as replication, transcription, and repair. Chromatin assembly is crucial for chromatin dynamics and is catalyzed by histone chaperones. Despite extensive studies, the mechanisms by which histone chaperones enable chromatin assembly remains elusive. Moreover, the global features of nucleosomes organized by histone chaperones are poorly understood. To address these problems, this work describes a unique single-molecule imaging technique named DNA curtain, which facilitates the investigation of the molecular details of nucleosome assembly by histone chaperones. DNA curtain is a hybrid technique that combines lipid fluidity, microfluidics, and total internal reflection fluorescence microscopy (TIRFM) to provide a universal platform for real-time imaging of diverse protein-DNA interactions.Using DNA curtain, the histone chaperone function of Abo1, the Schizosaccharomyces pombe bromodomain-containing AAA+ ATPase, is investigated, and the molecular mechanism underlying histone assembly of Abo1 is revealed. DNA curtain provides a unique approach for studying chromatin dynamics

Protein Arginine Methyltransferases in Neuromuscular Function and Diseases

Neuromuscular diseases (NMDs) are characterized by progressive loss of muscle mass and strength that leads to impaired body movement. It not only severely diminishes the quality of life of the patients, but also subjects them to increased risk of secondary medical conditions such as fall-induced injuries and various chronic diseases. However, no effective treatment is currently available to prevent or reverse the disease progression. Protein arginine methyltransferases (PRMTs) are emerging as a potential therapeutic target for diverse diseases, such as cancer and cardiovascular diseases. Their expression levels are altered in the patients and molecular mechanisms underlying the association between PRMTs and the diseases are being investigated. PRMTs have been shown to regulate development, homeostasis, and regeneration of both muscle and neurons, and their association to NMDs are emerging as well. Through inhibition of PRMT activities, a few studies have reported suppression of cytotoxic phenotypes observed in NMDs. Here, we review our current understanding of PRMTs’ involvement in the pathophysiology of NMDs and potential therapeutic strategies targeting PRMTs to address the unmet medical need.</jats:p