Bioprinting technologies face challenges in designing bioinks that possess optimal pre- and post-bioprinting properties. In most cases, enhancing flowability to improve pre-bioprinting properties results in inferior mechanical properties of the post-printed constructs. To achieve adequate mechanical strength, physical or chemical cross-linking is used, which compromises the biocompatibility and degradability of the constructs. A xanthan gum (XG) and tamarind seed polysaccharide (TSP) blend bioink was developed to overcome these limitations. Amongst several ratios, 4XG:1TSP (4X1T) demonstrated the highest printability with accuracy >95%. Rheological investigations revealed shear thinning viscosity while the LVER and creep recovery for 4X1T (698 MPa, 97%) and 4X (624 MPa, 85%) showed suitable properties for bioprinting applications. FTIR indicated hydrogen-bonding interactions between XG and TSP, conferring structural stability to the post-printed constructs without any crosslinking agent. The constructs showed loss of microstructure after 21 days of incubation in phosphate buffer (pH 7.5) at 37°C. In vitro cytocompatibility studies with human-derived SH-SY5Y cells disclosed no significant difference between the viability of cells seeded on XG and 4X1T, or cells bioprinted with 4X1T bioinks. It is concluded that the XG-TSP blend provides a bioink with promising printability, mechanical integrity, degradability, and cytocompatibility for the fabrication of tissue engineering constructs.

This is a case study of how NorthPole, a neural inference accelerator with a highly novel architecture, was designed, verified, and fabricated successfully in first-silicon using a horizontally integrated design workflow centered on a cycle-accurate functional simulator called the NorthPole Validator. The Validator is NorthPole’s digital twin. The study demonstrates how the Validator’s scalable network-of-queues structure, consisting of approximately 26,000 nodes and 415,000 queues, let it easily pivot from architecture validation and compiler codesign, to logic and physical design verification, post-silicon testing, and software ecosystem development.

A vertically integrated, end-to-end, research prototype system combines 288 NorthPole neural inference accelerator cards, offline training algorithms, a high-performance runtime stack, and a containerized inference pipeline to deliver a scalable and efficient cloud inference service. The system delivers 115 peta-ops at 4-bit integer precision and 3.7 PB/s of memory bandwidth across 18 2U servers, while consuming only 30 kW of power and weighing 730 kg in a 0.67 m^2 42U rack footprint. The system can run 3 simultaneous instances of the 8-billion-parameter open-source IBM Granite-3.3-8b-instruct model at 2,048 context length with 28 simultaneous users and a per-user inter-token latency of 2.8 ms. The system is scalable, modular, and reconfigurable, supporting various model sizes and context lengths, and is ideal for deploying agentic workflows for enterprise AI applications in existing data center (cloud, on-prem) environments. For example, the system can support 18 instances of a 3-billion-parameter model or a single instance of a 70-billion-parameter model.

Three-dimensional (3D) bioprinting offers promising solutions to the complex challenge of vascularization in biofabrication, thereby enhancing the prospects for clinical translation of engineered tissues and organs. While existing reviews have touched upon 3D bioprinting in vascularized tissue contexts, the current review offers a more holistic perspective, encompassing recent technical advancements and spanning the entire multistage bioprinting process, with a particular emphasis on vascularization. The synergy between 3D bioprinting and vascularization strategies is crucial, as 3D bioprinting can enable the creation of personalized, tissue-specific vascular network while the vascularization enhances tissue viability and function. The review starts by providing a comprehensive overview of the entire bioprinting process, spanning from pre-bioprinting stages to post-printing processing, including perfusion and maturation. Next, recent advancements in vascularization strategies that can be seamlessly integrated with bioprinting are discussed. Further, tissue-specific examples illustrating how these vascularization approaches are customized for diverse anatomical tissues towards enhancing clinical relevance are discussed. Finally, the underexplored intraoperative bioprinting (IOB) was highlighted, which enables the direct reconstruction of tissues within defect sites, stressing on the possible synergy shaped by combining IOB with vascularization strategies for improved regeneration.

For a 3-billion-parameter LLM, a research prototype inference appliance with 16 IBM AIU NorthPole processors delivers a massive 28,356 tokens / second of system throughput and sub-1 ms / token (per-user) latency while consuming merely 672 W for 16 NorthPole cards in a compact 2U form factor. With a focus on low latency and high energy efficiency, when NorthPole (in 12 nm) is compared to a suite of GPUs (in 7 / 5 / 4 nm) at various power consumptions, at the lowest GPU latency, NorthPole provides 72.7. better energy metric (tokens / second / W) while providing better latency.

Achieving rapid clotting and clot stability are important unmet goals of clinical management of noncompressible hemorrhage. This study reports the development of a spatiotemporally controlled release system of an antihemorrhagic drug, etamsylate, in the management of internal hemorrhage. Gly-Arg-Gly-Asp-Ser (GRGDS) peptide-functionalized chitosan nanoparticles, with high affinity to bind with the GPIIa/IIIb receptor of activated platelets, were loaded with the drug etamsylate (etamsylate-loaded GRGDS peptide-functionalized chitosan nanoparticles; EGCSNP). Peptide conjugation was confirmed by LCMS, and the delivery system was characterized by DLS, SEM, XRD, and FTIR. In vitro study exhibited 90% drug release till 48 h fitting into the Weibull model. Plasma recalcification time and prothrombin time tests of GRGDS-functionalized nanoparticles proved that clot formation was 1.5 times faster than nonfunctionalized chitosan nanoparticles. The whole blood clotting time was increased by 2.5 times over clot formed under nonfunctionalized chitosan nanoparticles. Furthermore, the application of rheometric analysis revealed a 1.2 times stiffer clot over chitosan nanoparticles. In an in vivo liver laceration rabbit model, EGCSNP spatially localized at the internal injury site within 5 min of intravenous administration, and no rebleeding was recorded up to 3 h. The animals survived for 3 weeks after the injury, indicating the strong potential of the system for the management of noncompressible hemorrhage.

We present preliminary results demonstrating AI (artificial intelligence) inference using the IBM AIU NorthPole Chip [1], [2] incorporated into a compact, rugged 3U VPX form factor module (NP-VPX) [3]. NP-VPX allows NorthPole to be used in edge applications with stringent cooling requirements, high-speed switch fabrics, and rugged environments. NP-VPX processes 965 frames per second (fps) with a Yolo-v4 network with 640x640 pixel images at 73.5 W at full-precision accuracy, achieving 13.2 frames / J (fps / W). NP-VPX processes over 40,300 fps with a ResNet-50 network with 224x224 pixel images at 65.9 W at full-precision accuracy, achieving 611 frames / J.

The fracture strength of the soft biological tissue aimed by the surgical needles acts as a serious character in clinical processes, for example, robotic-controlled needle steering, catheter insert, suturing, biopsy, etc. Despite the several investigational mechanisms on the fracture toughness of hard tissues, for example, dental tissue, bone, etc., only a very restricted number of investigations have concentrated on soft biological tissues, wherever the effects do not display any constancy mostly because of the casualness of the inserting/puncturing needle geometry. Tissue deformation during needle insertion is one potential source of inaccuracy. In modern medical science, the application of minimally invasive (MI) interventional tools for soft tissue surgery potentially reduced trauma, less blood loss, and less recovery times for the patient. One of the biggest challenges is tissue deformation affected by needle penetrating force, leading the undesired target engagements. The flexibility of soft tissue can create inaccurate needle insertions. With the purpose of the report on this matter, we experimented on the surgical needle insertion investigations on ex vivo duck livers with different diameters of the needle. A distinctive value for fracture toughness was attained for the ex vivo duck liver tissue by appropriately a link to the stiffness values assessed from the set of insertion experimentations. To confirm the investigational outcomes, a finite element model (FEM) of the ex vivo liver was established, and its hyper-viscoelastic properties were projected through an energy-based fracture mechanics method. The perforation forces assessed from the FEM simulations display an outstanding agreement with those developed from the physical investigations for all surgical needle geometries.

Bioprinting using cell-laden bioink is a rapidly emerging additive manufacturing method to fabricate engineered tissue constructs and in vitro models of disease biology. Amongst different bioprinting modalities, extrusion-based bioprinting is the most conveniently adopted technique due to its affordability. Bioinks consisting of living cells are suspended in hydrogels and extruded through syringe-needle assemblies, which subsequently undergo gelation at the collector plate. During the process, pressure is exerted on living cells which may cause cell deaths. Thus, for selected combination of cell and hydrogel, exerted pressure and the extrusion play key roles in determining the cell viability. Experimental evaluation to characterise stresses experienced by the cells in a bioink during bioprinting is a tedious exercise. Herein, computational modelling can be applied efficiently for rapid screening of bioinks. In the present study, a smoothed particle hydrodynamics model is developed for the analysis of stresses exerted on the cells during bioprinting process. Cells are modelled by assigning different mechanical properties to nucleus, cytoskeleton and cell membrane regions of the cell to get a more realistic understanding of cell deformation. The cytoplasm and nucleus are modelled as finite element meshes and a spring model of the cell membrane is coupled to the finite element model to develop a three-compartment model of the cell. Cell deformation is taken as a potential indicator of cell death. Effect of different process parameters such as flow rate, syringe-nozzle geometry and cell density are investigated. A submodeling approach is further introduced to predict deformation with higher resolution in a unit volume containing 104 to 108 cells. Results suggest that the generated bioink flow dynamic model can be a useful tool for the computational study of fluid flow involving cell suspensions during a bioprinting process.

Additive manufacturing (AM) make simpler the manufacturing of difficult geometric structures. Its possibility has quickly prolonged from the manufacture of pre-fabrication conception replicas to the making of finish practice portions driving the essential for superior part feature guarantee in the additively fabricated products. Machine learning (ML) is one of the encouraging methods that can be practiced to succeed in this aim. A modern study in this arena contains the procedure of managed and unconfirmed ML algorithms for excellent control and forecast of mechanical characteristics of AM products. This chapter describes the development of applying machine learning (ML) to numerous aspects of the additive manufacturing whole chain, counting model design, and quality evaluation. Present challenges in applying machine learning (ML) to additive manufacturing and possible solutions for these problems are then defined. Upcoming trends are planned in order to deliver a general discussion of this additive manufacturing area.

Biofabricated tissues have found numerous applications in tissue engineering and regenerative medicine in addition to the promotion of disease modeling and drug development and screening. Although three-dimensional (3D) printing strategies for designing and developing customized tissue constructs have made significant progress, the complexity of innate multicellular tissues hinders the accurate evaluation of physiological responses in vitro. Cellular aggregates, such as spheroids, are 3D structures where multiple types of cells are co-cultured and organized with endogenously secreted extracellular matrix and are designed to recapitulate the key features of native tissues more realistically. 3D Bioprinting has emerged as a crucial tool for positioning of these spheroids to assemble and organize them into physiologically- and histologically-relevant tissues, mimicking their native counterparts. This has triggered the convergence of spheroid fabrication and bioprinting, leading to the investigation of novel engineering methods for successful assembly of spheroids while simultaneously enhancing tissue repair. This review provides an overview of the current state-of-the-art in spheroid bioprinting methods and elucidates the involved technologies, intensively discusses the recent tissue fabrication applications, outlines the crucial properties that influence the bioprinting of these spheroids and bioprinted tissue characteristics, and finally details the current challenges and future perspectives of spheroid bioprinting efforts in the growing field of biofabrication.

Extracellular vesicles (EVs) are small lipid bilayer-delimited particles that are naturally released from cells into body fluids, and therefore can travel and convey regulatory functions in the distal parts of the body. EVs can transmit paracrine signaling by carrying over cytokines, chemokines, growth factors, interleukins (ILs), transcription factors, and nucleic acids such as DNA, mRNAs, microRNAs, piRNAs, lncRNAs, sn/snoRNAs, mtRNAs and circRNAs; these EVs travel to predecided destinations to perform their functions. While mesenchymal stem cells (MSCs) have been shown to improve healing and facilitate treatments of various diseases, the allogenic use of these cells is often accompanied by serious adverse effects after transplantation. MSC-produced EVs are less immunogenic and can serve as an alternative to cellular therapies by transmitting signaling or delivering biomaterials to diseased areas of the body. This review article is focused on understanding the properties of EVs derived from different types of MSCs and MSC–EV-based therapeutic options. The potential of modern technologies such as 3D bioprinting to advance EV-based therapies is also discussed.

Composite materials are made of two different materials that have distinct physical and chemical properties. Composites find applications in various engineering domains as they offer improved material properties, such as better strength and lower stiffness, over their constituent materials. The use of composites in biomedical engineering has grown rapidly over the last few decades. To replace and/or repair damaged tissues and/or organs, various implants and grafting/substitute materials are required that should ideally have unique mechanical characteristics matching the concerned host tissue. Composite materials are preferred over traditional biomaterials such as metals, ceramics, and polymers in many healthcare-related applications. In this article, an overview of composite materials in various biomedical engineering applications is discussed, highlighting the latest developments and future trends. First of all, the structure and composition of natural composite materials in the human body are presented along with cellular responses observed in composite materials when implanted. Characterization and fabrication techniques of composite materials are highlighted next. Thereafter, the use of composite materials in several hard- and soft-tissue applications are elaborated.

3D printing technology has influenced the healthcare and medical sector during the COVID-19 pandemic. 3D printer machines fabricated numerous medical kits and accessories. These include face shields, specimen collectors, personalized face masks, ventilators, protective eyewear, personal protection equipment (PPE), and isolation wards/chambers. All these were fabricated in a short period as requirements were increasing expressively. The chapter describes several of these applications of 3D printing, which assisted in looking after numerous lives throughout the COVID-19 pandemic. The cooperation of the 3D-printing knowledge with the worldwide healthcare community will develop innovative and essential prospects in the upcoming days. Also, numerous important proposals are described that were applied to fight against the COVID-19 outbreak, monitored by a discussion about the upcoming trend of how additive manufacturing can help people worldwide to control any upcoming pandemic situation.

In December 2019, there was a health emergency worldwide named novel coronavirus or COVID-19 by the world health organization (WHO). It originated from the Wuhan seafood market, Hubei Province, China. Till now Severe Acute Respiratory Syndrome Coronavirus-2 or SARS-CoV-2 spread over 216 countries with 177,108,695 confirmed cases and 3,840,223 confirmed death cases has been reported (5:31 pm CEST, 18 June 2021; WHO). Analyzing the risk factor of this pandemic situation, different government health organizations of all the countries including WHO are taking several preventive measures with ongoing research works, even the vaccination process started. In this study, we tried to analyze all the available information on pandemic COVID-19, which includes the origin of COVID-19, pathogenic mechanism, transmission, diagnosis, treatment, and control-preventive measures, also the additional treatment and prevention taken by the Indian government is being studied here.

We report two novel fluorescent probes viz.BPQ1 and BPQ2, to detect selectively cysteine (Cys) in dual reporter mode based upon the specific position of the reactive acrylate group.

Deep networks are now able to achieve human-level performance on a broad spectrum of recognition tasks. Independently, neuromorphic computing has now demonstrated unprecedented energy-efficiency through a new chip architecture based on spiking neurons, low precision synapses, and a scalable communication network. Here, we demonstrate that neuromorphic computing, despite its novel architectural primitives, can implement deep convolution networks that i) approach state-of-the-art classification accuracy across 8 standard datasets, encompassing vision and speech, ii) perform inference while preserving the hardware's underlying energy-efficiency and high throughput, running on the aforementioned datasets at between 1200 and 2600 frames per second and using between 25 and 275 mW (effectively > 6000 frames / sec / W) and iii) can be specified and trained using backpropagation with the same ease-of-use as contemporary deep learning. For the first time, the algorithmic power of deep learning can be merged with the efficiency of neuromorphic processors, bringing the promise of embedded, intelligent, brain-inspired computing one step closer.

The new era of cognitive computing brings forth the grand challenge of developing systems capable of processing massive amounts of noisy multisensory data. This type of intelligent computing poses a set of constraints, including real-time operation, low-power consumption and scalability, which require a radical departure from conventional system design. Brain-inspired architectures offer tremendous promise in this area. To this end, we developed TrueNorth, a 65 mW real-time neurosynaptic processor that implements a non-von Neumann, low-power, highly-parallel, scalable, and defect-tolerant architecture. With 4096 neurosynaptic cores, the TrueNorth chip contains 1 million digital neurons and 256 million synapses tightly interconnected by an event-driven routing infrastructure. The fully digital 5.4 billion transistor implementation leverages existing CMOS scaling trends, while ensuring one-to-one correspondence between hardware and software. With such aggressive design metrics and the TrueNorth architecture breaking path with prevailing architectures, it is clear that conventional computer-aided design (CAD) tools could not be used for the design. As a result, we developed a novel design methodology that includes mixed asynchronous-synchronous circuits and a complete tool flow for building an event-driven, low-power neurosynaptic chip. The TrueNorth chip is fully configurable in terms of connectivity and neural parameters to allow custom configurations for a wide range of cognitive and sensory perception applications. To reduce the system's communication energy, we have adapted existing application-agnostic very large-scale integration CAD placement tools for mapping logical neural networks to the physical neurosynaptic core locations on the TrueNorth chips. With that, we have successfully demonstrated the use of TrueNorth-based systems in multiple applications, including visual object recognition, with higher performance and orders of magnitude lower power consumption than the same algorithms run on von Neumann architectures. The TrueNorth chip and its tool flow serve as building blocks for future cognitive systems, and give designers an opportunity to develop novel brain-inspired architectures and systems based on the knowledge obtained from this paper.