Hückel molecular orbital theory on a quantum computer: A scalable system-agnostic variational implementation with compact encoding.

Hückel molecular orbital (HMO) theory provides a semi-empirical treatment of the electronic structure in conjugated π-electronic systems. A scalable system-agnostic execution of HMO theory on a quantum computer is reported here based on a variational quantum deflation (VQD) algorithm for excited state quantum simulation. A compact encoding scheme is proposed here that provides an exponential advantage over the direct mapping and allows for quantum simulation of the HMO model for systems with up to 2n conjugated centers with n qubits. The transformation of the Hückel Hamiltonian to qubit space is achieved by two different strategies: an iterative refinement transformation and the Frobenius-inner-product-based transformation. These methods are tested on a series of linear, cyclic, and hetero-nuclear conjugated π-electronic systems. The molecular orbital energy levels and wavefunctions from the quantum simulation are in excellent agreement with the exact classical results. However, the higher excited states of large systems are found to suffer from error accumulation in the VQD simulation. This is mitigated by formulating a variant of VQD that exploits the symmetry of the Hamiltonian. This strategy has been successfully demonstrated for the quantum simulation of C60 fullerene containing 680 Pauli strings encoded on six qubits. The methods developed in this work are easily adaptable to similar problems of different complexity in other fields of research.

Benchmarking of different optimizers in the variational quantum algorithms for applications in quantum chemistry.

Classical optimizers play a crucial role in determining the accuracy and convergence of variational quantum algorithms; leading algorithms use a near-term quantum computer to solve the ground state properties of molecules, simulate dynamics of different quantum systems, and so on. In the literature, many optimizers, each having its own architecture, have been employed expediently for different applications. In this work, we consider a few popular and efficacious optimizers and assess their performance in variational quantum algorithms for applications in quantum chemistry in a realistic noisy setting. We benchmark the optimizers with critical analysis based on quantum simulations of simple molecules, such as hydrogen, lithium hydride, beryllium hydride, water, and hydrogen fluoride. The errors in the ground state energy, dissociation energy, and dipole moment are the parameters used as yardsticks. All the simulations were carried out with an ideal quantum circuit simulator, a noisy quantum circuit simulator, and finally a noisy simulator with noise embedded from the IBM Cairo quantum device to understand the performance of the classical optimizers in ideal and realistic quantum environments. We used the standard unitary coupled cluster ansatz for simulations, and the number of qubits varied from two starting from the hydrogen molecule to ten qubits in hydrogen fluoride. Based on the performance of these optimizers in the ideal quantum circuits, the conjugate gradient, limited-memory Broyden-Fletcher-Goldfarb-Shanno bound, and sequential least squares programming optimizers are found to be the best-performing gradient-based optimizers. While constrained optimization by linear approximation (COBYLA) and Powell's conjugate direction algorithm for unconstrained optimization (POWELL) perform most efficiently among the gradient-free methods, in noisy quantum circuit conditions, simultaneous perturbation stochastic approximation, POWELL, and COBYLA are among the best-performing optimizers.

Using a Secure, Continually Updating, Web Source Processing Pipeline to Support the Real-Time Data Synthesis and Analysis of Scientific Literature: Development and Validation Study.

BACKGROUND: The scale and quality of the global scientific response to the COVID-19 pandemic have unquestionably saved lives. However, the COVID-19 pandemic has also triggered an unprecedented "infodemic"; the velocity and volume of data production have overwhelmed many key stakeholders such as clinicians and policy makers, as they have been unable to process structured and unstructured data for evidence-based decision making. Solutions that aim to alleviate this data synthesis-related challenge are unable to capture heterogeneous web data in real time for the production of concomitant answers and are not based on the high-quality information in responses to a free-text query. OBJECTIVE: The main objective of this project is to build a generic, real-time, continuously updating curation platform that can support the data synthesis and analysis of a scientific literature framework. Our secondary objective is to validate this platform and the curation methodology for COVID-19-related medical literature by expanding the COVID-19 Open Research Dataset via the addition of new, unstructured data. METHODS: To create an infrastructure that addresses our objectives, the PanSurg Collaborative at Imperial College London has developed a unique data pipeline based on a web crawler extraction methodology. This data pipeline uses a novel curation methodology that adopts a human-in-the-loop approach for the characterization of quality, relevance, and key evidence across a range of scientific literature sources. RESULTS: REDASA (Realtime Data Synthesis and Analysis) is now one of the world's largest and most up-to-date sources of COVID-19-related evidence; it consists of 104,000 documents. By capturing curators' critical appraisal methodologies through the discrete labeling and rating of information, REDASA rapidly developed a foundational, pooled, data science data set of over 1400 articles in under 2 weeks. These articles provide COVID-19-related information and represent around 10% of all papers about COVID-19. CONCLUSIONS: This data set can act as ground truth for the future implementation of a live, automated systematic review. The three benefits of REDASA's design are as follows: (1) it adopts a user-friendly, human-in-the-loop methodology by embedding an efficient, user-friendly curation platform into a natural language processing search engine; (2) it provides a curated data set in the JavaScript Object Notation format for experienced academic reviewers' critical appraisal choices and decision-making methodologies; and (3) due to the wide scope and depth of its web crawling method, REDASA has already captured one of the world's largest COVID-19-related data corpora for searches and curation.

Assessment of risk of obstructive sleep apnea with thyroid eye disease and its activity.

PURPOSE: To evaluate the association between obstructive sleep apnea (OSA) and thyroid eye disease (TED) and its effect on disease activity. METHODS: A prospective case-control study was conducted from January 2020 to March 2022. All TED patients (group A) were clinically evaluated. The activity of thyroid eye disease was calculated based on the clinical activity score (CAS), and grading of severity was done according to the EUGOGO classification. All TED patients (group A) were screened for OSA using the Snoring Tired Observed Pressure (STOP)-Bang survey. Age- and gender-matched control group patients (group B) without TED were screened for OSA. RESULTS: One hundred TED patients and 138 control patients without TED were included in the respective groups. Sixty-two (62%) patients in group A and 48 (34.78%) patients in group B were having high risk of OSA, and this difference was statistically significant (P = 0.001). Further, in group A patients, on univariate analysis, TED activity was significantly associated with a high risk of OSA (P = 0.009). On multivariate logistic regression analysis, OSA also showed significant association with TED activity (odds ratio [OR]: 4.14, 95% confidence interval [CI]: 1.11-18.85 at 10% level; P = 0.05). CONCLUSION: Our study showed that OSA is significantly associated with TED disease and its activity. However, no significant association was found between OSA and severity of the disease.

Implication of Covid-19 on Neurological Complications with Specific Emphasis on Alzheimer's and Parkinson's Disease.

It is noticeable how the novel coronavirus has spread from the Wuhan region of China to the whole world, devastating the lives of people worldwide. All the data related to the precautionary measures, diagnosis, treatment, and even the epidemiological data are being made freely accessible and reachable in a very little time as well as being rapidly published to save humankind from this pandemic. There might be neurological complications of COVID-19 and patients suffering from neurodegenerative conditions like Alzheimer's disease and Parkinson's disease might have repercussions as a result of the pandemic. In this review article, we have discussed the effect of SARS-CoV-2 viral infection on the people affected with neurodegenerative disorders such as Parkinson's and Alzheimer's. It primarily emphasizes two issues, i.e., vulnerability to infection and modifications of course of the disease concerning the clinical neurological manifestations, the advancement of the disease and novel approaches to support health care professionals in disease management, the susceptibility to these diseases, and impact on the severity of disease and management.

BSA Nanoparticles for siRNA Delivery: Coating Effects on Nanoparticle Properties, Plasma Protein Adsorption, and In Vitro siRNA Delivery.

Developing vehicles for the delivery of therapeutic molecules, like siRNA, is an area of active research. Nanoparticles composed of bovine serum albumin, stabilized via the adsorption of poly-L-lysine (PLL), have been shown to be potentially inert drug-delivery vehicles. With the primary goal of reducing nonspecific protein adsorption, the effect of using comb-type structures of poly(ethylene glycol) (1 kDa, PEG) units conjugated to PLL (4.2 and 24 kDa) on BSA-NP properties, apparent siRNA release rate, cell viability, and cell uptake were evaluated. PEGylated PLL coatings resulted in NPs with ζ-potentials close to neutral. Incubation with platelet-poor plasma showed the composition of the adsorbed proteome was similar for all systems. siRNA was effectively encapsulated and released in a sustained manner from all NPs. With 4.2 kDa PLL, cellular uptake was not affected by the presence of PEG, but PEG coating inhibited uptake with 24 kDa PLL NPs. Moreover, 24 kDa PLL systems were cytotoxic and this cytotoxicity was diminished upon PEG incorporation. The overall results identified a BSA-NP coating structure that provided effective siRNA encapsulation while reducing ζ-potential, protein adsorption, and cytotoxicity, necessary attributes for in vivo application of drug-delivery vehicles.