Emerging 2D pnictogens for biomedical applications

Smart MXene-based microrobots for targeted drug delivery and synergistic therapies

MXenes and their composites exhibit remarkable electrical conductivity, mechanical flexibility, and biocompatibility, making them ideal candidates for microrobot fabrication. Their tunable surface chemistry allows for easy functionalization, which enhances their interaction with biological environments, thereby facilitating targeted therapies. Such smart microrobots can be engineered to navigate through complex biological systems with precision via the integration of responsive elements, such as stimuli-sensitive polymers or magnetic components. MXene-based microrobots are able to actively seek out specific tissues or cells. This capability is crucial for applications in cancer treatment, where localized drug delivery minimizes side effects and enhances therapeutic efficacy. The primary advantage of MXene-based microrobots lies in their ability to deliver therapeutic agents directly to diseased cells. Utilizing ligand-receptor interactions, these microrobots can bind to target cells and release their payload in a controlled manner. This targeted delivery system not only improves the effectiveness of the drug but also reduces the required dosage, thus mitigating potential side effects. Moreover, smart MXene-based microrobots can facilitate synergistic therapies by co-delivering multiple therapeutic agents. For instance, combining chemotherapy drugs with immunotherapeutic agents could enhance treatment outcomes in cancer therapy. The ability to simultaneously deliver different types of drugs allows for more comprehensive treatment strategies that can tackle tumor heterogeneity. Significant advancements are anticipated in synergistic therapies, particularly in chemo-photothermal, chemodynamic, and photothermal/photodynamic therapies. These strategies leverage multiple therapeutic modalities to enhance cancer treatment outcomes. Despite their outstanding potential, several challenges remain in the development of MXene-based microrobots namely matters pertaining to scalability, stability in biological environments, and associated regulatory hurdles which ought to be addressed. Future research should focus on optimizing the design and functionality of these microrobots, including enhancing their navigation capabilities and ensuring their safety and effectiveness in vivo. By presenting the innovative capabilities of MXene-based microrobots, this perspective aims to inspire additional explorations in the field of advanced targeted drug delivery systems and synergistic therapies, ultimately contributing to the future of personalized medicine and oncology.

Anion transporters based on halogen, chalcogen, and pnictogen bonds: towards biological applications

Motivated by their potential biological applications, anion receptors are increasingly explored as transmembrane transporters for anions. The vast majority of the reported anion transporters rely on hydrogen bonding to interact with the anions. However, in recent decades, halogen, chalcogen, and pnictogen bonding, collectively referred to as sigma-hole interactions, have received increasing attention. Most research efforts on these interactions have focused on crystal engineering, anion sensing, and organocatalysis. In recent years, however, these sigma-hole interactions have also been explored more widely in synthetic anion transporters. This perspective shows why synthetic transporters are promising candidates for biological applications. We provide a comprehensive review of the compounds used to transport anions across membranes, with a particular focus on how the binding atoms and molecular design affect the anion transport activity and selectivity. Few cell studies have been reported for these transporters based on sigma-hole interactions and we highlight the critical need for further biological studies on the toxicity, stability, and deliverability of these compounds to explore their full potential in biological applications, such as the treatment of cystic fibrosis.

Emerging 2D pnictogens: a novel multifunctional photonic nanoplatform for cutting-edge precision treatment

The elements of the pnictogen group, known as the 15th (VA) family in the periodic table, including phosphorus (P), arsenic (As), antimony (Sb) and bismuth (Bi), have been widely used by alchemists to treat various diseases since ancient times and hold a pivotal position in the history of medicine, owing to their diverse pharmacological activities. Recently, with the development of modern nanotechnology, pnictogen group elements appear in a more innovative form, namely two-dimensional (2D) pnictogens (i.e. phosphorene, arsenene, and bismuthene) with a unique layered crystal structure and extraordinary optoelectronic characteristics, which endow them with significant superiority as a novel multifunctional photonic nanoplatform for cutting-edge precision treatment of various diseases. The puckered layer structure with ultralarge surface area make them ideal drug and gene delivery vectors that can avoid degradation and reduce target effects. The anisotropic morphology allows their easier internalization by cells and may improve gene transfection efficiency. Tunable optoelectronic characteristics endow them with excellent phototherapy performance as well as the ability to act as an optical switch to initiate subsequent therapeutic events. This review provides a brief overview of the properties, preparation and surface modifications of 2D pnictogens, and then focuses on its applications in cutting-edge precision treatment as a novel multifunctional photonic nanoplatform, such as phototherapy, photonic medicine, photo-adjuvant immunotherapy and photo-assisted gene therapy. Finally, the challenges and future development trends for 2D pnictogens are provided. With a focus on 2D pnictogen-based multifunctional photonic nanoplatforms, this review may also provide profound insights for the next generation innovative precision therapy.

A Survey of COVID-19 Diagnosis Using Routine Blood Tests with the Aid of Artificial Intelligence Techniques

Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2), causing a disease called COVID-19, is a class of acute respiratory syndrome that has considerably affected the global economy and healthcare system. This virus is diagnosed using a traditional technique known as the Reverse Transcription Polymerase Chain Reaction (RT-PCR) test. However, RT-PCR customarily outputs a lot of false-negative and incorrect results. Current works indicate that COVID-19 can also be diagnosed using imaging resolutions, including CT scans, X-rays, and blood tests. Nevertheless, X-rays and CT scans cannot always be used for patient screening because of high costs, radiation doses, and an insufficient number of devices. Therefore, there is a requirement for a less expensive and faster diagnostic model to recognize the positive and negative cases of COVID-19. Blood tests are easily performed and cost less than RT-PCR and imaging tests. Since biochemical parameters in routine blood tests vary during the COVID-19 infection, they may supply physicians with exact information about the diagnosis of COVID-19. This study reviewed some newly emerging artificial intelligence (AI)-based methods to diagnose COVID-19 using routine blood tests. We gathered information about research resources and inspected 92 articles that were carefully chosen from a variety of publishers, such as IEEE, Springer, Elsevier, and MDPI. Then, these 92 studies are classified into two tables which contain articles that use machine Learning and deep Learning models to diagnose COVID-19 while using routine blood test datasets. In these studies, for diagnosing COVID-19, Random Forest and logistic regression are the most widely used machine learning methods and the most widely used performance metrics are accuracy, sensitivity, specificity, and AUC. Finally, we conclude by discussing and analyzing these studies which use machine learning and deep learning models and routine blood test datasets for COVID-19 detection. This survey can be the starting point for a novice-/beginner-level researcher to perform on COVID-19 classification.