CRISPR/Cas9 delivery by NIR-responsive biomimetic nanoparticles for targeted HBV therapy

Background Currently, there are no curative drugs for hepatitis B virus (HBV). Complete elimination of HBV covalently closed circular DNA (cccDNA) is key to the complete cure of hepatitis B virus infection. The CRISPR/Cas9 system can directly destroy HBV cccDNA. However, a CRISPR/Cas9 delivery system with low immunogenicity and high efficiency has not yet been established. Moreover, effective implementation of precise remote spatiotemporal operations in CRISPR/Cas9 is a major limitation. Results In this work, we designed NIR-responsive biomimetic nanoparticles (UCNPs-Cas9@CM), which could effectively deliver Cas9 RNP to achieve effective genome editing for HBV therapy. HBsAg, HBeAg, HBV pgRNA and HBV DNA along with cccDNA in HBV-infected cells were found to be inhibited. These findings were confirmed in HBV-Tg mice, which did not exhibit significant cytotoxicity and minimal off-target DNA damage. Conclusions The UCNPs-based biomimetic nanoplatforms achieved the inhibition of HBV replication via CRISPR therapy and it is a potential system for efficient treatment of human HBV diseases. Graphical Abstract

Nanoemulgel: a Promising Nanolipoidal-Emulsion Based Drug Delivery System in Managing Psoriasis

Nanomedicine, a novel concept, bears much hope in delivering drug candidates having low solubility and bioavailability. Nano-emulgel, one of the emerging tools, is considered as ideal carriers for the topical delivery of lipophilic drugs to overcome these challenges in the management of psoriasis and related skin problems. Psoriasis is an auto-immune and chronic inflammatory disease affecting 2-3% population of the world. Current available treatment of psoriasis has limitations such as systemic side effects and low percutaneous permeation, which evokes a dire need to develop an alternative lipoidal nanocarrier system. Nano-emulgel is basically formed by admixing nanoemulsion system with a hydrogel matrix using both high and low energy methods. Various literatures have been reported for lipoidal nanocarriers in topical treatment suggesting reduced dose, improved percutaneous absorption and better bioavailability of lipophilic drugs with nano-emulgel delivery via topical route. Several approved marketed preparations are available that strongly support the stability of these nanocarriers in respect to its efficacy and safety. This supports the fact of using topical nano-emulgel system to deliver lipophilic drugs to overcome the sufferings from oral delivery and improved patient compliance. Therefore, it is suggested as a potential system that can be used for an effective management of psoriasis via topical route in near future. Dhaka Univ. J. Pharm. Sci. 20(2): 235-246, 2021 (December)

Inequity in Cardio‐Oncology: Identifying Disparities in Cardiotoxicity and Links to Cardiac and Cancer Outcomes

Minority and underresourced communities experience disproportionately high rates of fatal cancer and cardiovascular disease. The intersection of these disparities within the multidisciplinary field of cardio‐oncology is in critical need of examination, given the risk of perpetuating health inequities in the growing vulnerable population of patients with cancer and cardiovascular disease. This review identifies 13 cohort studies and 2 meta‐analyses investigating disparate outcomes in treatment‐associated cardiotoxicity and situates these data within the context of oncologic disparities, preexisting cardiovascular disparities, and potential system‐level inequities. Black survivors of breast cancer have elevated risks of cardiotoxicity morbidity and mortality compared with White counterparts. Adolescent and young adult survivors of cancer with lower socioeconomic status experience worsened cardiovascular outcomes compared with those of higher socioeconomic status. Female patients treated with anthracyclines or radiation have higher risks of cardiotoxicity compared with male patients. Given the paucity of data, our understanding of these racial and ethnic, socioeconomic, and sex and gender disparities remains limited and large‐scale studies are needed for elucidation. Prioritizing this research while addressing clinical trial inclusion and access to specialist care is paramount to reducing health inequity.

Advanced Exergy, Exergoeconomic, and Exergoenvironmental Analyses of Integrated Solar-Assisted Gasification Cycle for Producing Power and Steam from Heavy Refinery Fuels

Integrated solar-assisted gasification cycles (ISGC) have emerged as a more flexible and environmentally friendly solution for producing power, steam, and other high-valued by-products from low-cost opportunity fuels. In this light, this paper investigates a new ISGC system for converting heavy refineries fuels into power and steam utilities while enhancing energy efficiency and economic and environmental performance indicators. In this approach, a solar energy field and a two-pressure heat recovery steam generator were integrated into the ISGC system to improve overall economic and environmental plant viability. The ISGC system was modelled in MATLAB software, and the results were validated using Thermoflex software. Conventional and advanced energy, exergy, exergoeconomic, and exergoenvironmental (4E) analyses were implemented to assess the main performance parameters and identify potential system improvements. The ISGC system produced 319.92 MW of power by feeding on 15.5 kg/s of heavy refinery fuel, with a thermal efficiency of 50% and exergy efficiency of 54%. The results also revealed an investment cost of $466 million, evaluated at a system cost rate of 446 $/min and an environmental impact rate of 72,796 pts/min. The conventional and advanced 4E analyses unveiled the process economic and environmental feasibilities, particularly for oil-rich countries with high availability of solar resources.

Research and application of Stochastic Resonance in Quad-stable Potential System

To solve the problem of low weak signal enhancement performance in the quad-stable system, a new Quad-stable potential Stochastic Resonance (QSR) is proposed. Firstly, under the condition of adiabatic approximation theory, the Stationary Probability Distribution (SPD), the Mean First Passage Time (MFPT), the Work (W) and the power Spectrum Amplification Factor (SAF) are derived, and the impacts of system parameters on them are also deeply analyzed. Secondly, numerical simulations are performed to compare QSR with the Classical Tri-stable Stochastic Resonance (CTSR) by using the Genetic Algorithm (GA) and the fourth-order Runge-Kutta algorithm. It shows that the Signal-to-Noise Ratio (SNR) and Mean Signal-to-Noise Increase (MSNRI) of QSR are higher than CTSR, which indicates that QSR has superior noise immunity than CTSR. Finally, the two systems are applied in the detection on real bearing faults. The experimental results show that QSR is superior to CTSR, which provides a better theoretical significance and reference value for practical engineering application.

A Data-Driven Iterative Approach for Semi-automatically Assessing the Correctness of Medication Value Sets: A Proof of Concept Based on Opioids

Background Value sets are lists of terms (e.g., opioid medication names) and their corresponding codes from standard clinical vocabularies (e.g., RxNorm) created with the intent of supporting health information exchange and research. Value sets are manually-created and often exhibit errors. Objectives The aim of the study is to develop a semi-automatic, data-centric natural language processing (NLP) method to assess medication-related value set correctness and evaluate it on a set of opioid medication value sets. Methods We developed an NLP algorithm that utilizes value sets containing mostly true positives and true negatives to learn lexical patterns associated with the true positives, and then employs these patterns to identify potential errors in unseen value sets. We evaluated the algorithm on a set of opioid medication value sets, using the recall, precision and F1-score metrics. We applied the trained model to assess the correctness of unseen opioid value sets based on recall. To replicate the application of the algorithm in real-world settings, a domain expert manually conducted error analysis to identify potential system and value set errors. Results Thirty-eight value sets were retrieved from the Value Set Authority Center, and six (two opioid, four non-opioid) were used to develop and evaluate the system. Average precision, recall, and F1-score were 0.932, 0.904, and 0.909, respectively on uncorrected value sets; and 0.958, 0.953, and 0.953, respectively after manual correction of the same value sets. On 20 unseen opioid value sets, the algorithm obtained average recall of 0.89. Error analyses revealed that the main sources of system misclassifications were differences in how opioids were coded in the value sets—while the training value sets had generic names mostly, some of the unseen value sets had new trade names and ingredients. Conclusion The proposed approach is data-centric, reusable, customizable, and not resource intensive. It may help domain experts to easily validate value sets.

A formal notion of genericity and term-by-term vanishing superpotentials at supersymmetric vacua from R-symmetric Wess-Zumino models

It is known in previous literature that if a Wess-Zumino model with an R-symmetry gives a supersymmetric vacuum, the superpotential vanishes at the vacuum. In this work, we establish a formal notion of genericity, and show that if the R-symmetric superpotential has generic coefficients, the superpotential vanishes term-by-term at a supersymmetric vacuum. This result constrains the form of the superpotential which leads to a supersymmetric vacuum. It may contribute to a refined classification of R-symmetric Wess-Zumino models, and find applications in string constructions of vacua with small superpotentials. A similar result for a scalar potential system with a scaling symmetry is discussed.

Techno-Economic Assessment of a Hybrid Gas Tank Hot Water Combined Heat and Power System

Combined heat and power (CHP) systems with an integrated solid oxide fuel cell (SOFC) is a promising technology to increase overall efficiency of traditional residential combustion systems. One potential system is gas tank hot water heaters where partial oxidation of the fuel serves as a means of fuel reforming for SOFCs while producing thermal energy for heating water. In this study, a residential hybrid gas tank hot water heater with an integrated SOFC model was developed and a thorough techno-economic analysis was performed. Fuel-rich combustion characterization was performed at equivalence ratios 1.1 to 1.6 to assess synthesis gas production for the SOFC. The effect of fuel utilization and operating voltage of the model SOFC stack were analyzed to provide an in-depth characterization of the potential of the system. CHP and electrical efficiencies over >90% and >16% were achieved, respectively. The techno-economic analysis considers the four major census regions of the United States to evaluate regional savings based on respective utility costs and hot water demand. The results show the hybrid system is economically feasible for replacement of an electrical water heater with the longest payback period being approximately six years.

Ionized Gas Thermoelectric Generator: Theoretical Evaluation and Efficiency Estimation

The novel Ionized Gas Thermoelectric Generator (IG-TEG) system that has been studied theoretically showing capabilities to continually extracting energy from the thermal energy of the ambient air, at low temperatures within the standard room temperature and below. This system does not need a temperature gradient in order to work, unlike the other TEGs that use Seebeck effect, and therefore this new system can be utilized for cooling purposes, by extracting energy instead of wasting energy in compressing the gas for cooling. This novel system was designed based on Static Ratchet Potential (SRP), which is known as a spatially asymmetric electric potential produced by an array of positive and negative electrodes. The ratchet potential produces electrical current from random Brownian Motion of charged particles that are driven by thermal energy. Ratchet potential was studied and investigated by several researches in the fields of sensing and energy harvesting. The main ratchet potential system parameter is the particles transportation, this parameter was studied under the condition of flashing ratchet potentials, and was analyzed based on several methods. In this study, a different approach is pursued to estimate particles transportation, by evaluating the charged particles distribution, and applying the other conditions of the SRP.