Development of a novel representation of drug 3D structures and enhancement of the TSR-based method for probing drug and target interactions.

Understanding the mechanisms underlying interactions between drugs and target proteins is critical for drug discovery. In our earlier studies, we introduced the Triangular Spatial Relationship (TSR)-based algorithm, which enables the representation of a protein's 3D structure as a vector of integers (TSR keys). These TSR keys correspond to substructures of the 3D structure of a protein and are computed based on the triangles constructed by all possible triples of Cα atoms within the protein. In this study, we report on a new TSR-based algorithm for probing drug and target interactions. Specifically, we have extended the previous algorithm in three novel directions: TSR keys for representing the 3D structure of a drug or a ligand, cross TSR keys between drugs and their targets and intra-residual TSR keys for phosphorylated amino acids. The outcomes illustrate the key contributions as follows: (i) The TSR-based method, which uses the TSR keys as features, is unique in its capability to interpret hierarchical relationships of drugs as well as drug - target complexes using common and specific TSR keys. (ii) The method can distinguish not only the binding sites from the rest of the protein structures, but also the binding sites of primary targets from those of off-targets. (iii) The method has the potential to correlate the 3D structures of drugs with their functions. (iv) Representation of 3D structures by TSR keys has its unique advantage in terms of ease of making searching for similar substructures across structure datasets easier. In summary, this study presents a novel computational methodology, with significant advantages, for providing insights into the mechanism underlying drug and target interactions.

Health perceptions and attitudes on mobile health apps in China.

Since the COVID-19 pandemic, the use of mobile health apps has been increasing significantly. Many have started using mobile health apps to manage their health and well-being. People need to develop healthy habits that will increase their job productivity, life expectancy and improve their social relationships. This study tests the Health Belief Model with adults in China in influencing health motivation and in turn improving their health behavior. A mobile and online survey was developed, and 120 usable responses were collected. A structural equation modeling was used to test the relationships between perceived benefits, perceived susceptibility, perceived barriers, health motivation, and health behavior. We found perceived self-efficacy, perceived benefits, and perceived susceptibility predicts health motivation, and health motivation predicts health behavior. We did not find that perceived barrier is a predictor for health motivation. We conclude that to encourage behavioral change, it is important to enhance self-efficacy and address the benefits; in the meantime, educating people on susceptibility is also important.

Variance in Hospital Charges and in-Patient Care for Liver Transplants: Examining Non-Clinical Predictors and Implications.

The significant and apparent variance in hospital charges and inpatient care in the U.S. has perplexed the general public including many stakeholders such as the healthcare regulators and insurers. While the clinical side of inpatient care has been undergoing tremendous progress and standardization, the overall cost of healthcare has been ballooning. The purpose of this research is to conduct statistical analyses that reveal the sources of variance in hospital charges and inpatient care using the annual data from the AHRQ's (Agency for Healthcare Research and Quality) HCUP's (Hospital Cost and Utilization Project) NIS (National Inpatient Sample) database. Our focus is on non-clinical factors such as patient age, gender, income and race and hospital location data as independent variables to investigate their impact on hospital charges and inpatient care. Our research sample is the liver transplant cases in 2019 sampled in the NIS 2019 database. Our regression results show patient age and gender as well as payer affect the number of diagnoses; and hospital charges are affected by age, payer and hospital location. Number of procedures was not affected by any of these non-clinical factors except the hospital location. Implications suggest that there is more room for standardization of the number of diagnoses and procedures across regions in the US. Results also reveal that race and income do not have any effect on hospital charges and inpatient care. Our study contributes to an empirical understanding of non-clinical factors in the explanation of variance in hospital charges and inpatient care.

Introducing mirror-image discrimination capability to the TSR-based method for capturing stereo geometry and understanding hierarchical structure relationships of protein receptor family.

We have developed a Triangular Spatial Relationship (TSR)-based computational method for protein structure comparison and motif discovery that is both sequence and structure alignment-free. A protein 3D structure is modeled by all possible triangles that are constructed with every three Cα atoms of amino acids as vertices. Every triangle is represented using an integer (a key). The keys are calculated by a rule-based formula which is a function of a representative length, a representative angle, and the vertex labels associated with amino acids. A 3D structure is thereby represented by a vector of integers (TSR keys). Global or local structure comparisons are achieved by computing all keys or a set of keys, respectively. Many enzymatic reactions and notable marketed drugs are highly stereospecific. Thus, in this paper, we propose a modified key calculation formula by including a mechanism for discriminating mirror-image keys to capture stereo geometry. We assign a positive or a negative sign to the integers representing mirror-image keys. Applying the new key calculation function provides the ability to further discriminate mirror-image keys that were previously considered identical. As the result, applying the mirror-image discrimination capability (i) significantly increases the number of distinct keys; (ii) decreases the number of common keys; (iii) decreases structural similarity; (iv) increases the opportunity to identify specific keys for each type of the receptors. The specific keys identified in this study for the cases of without (not applying) and with (applying) mirror-image discrimination can be considered as the structure signatures that exclusively belong to a certain type of receptors. Applying mirror-image discrimination introduces stereospecificity to keys for allowing more precise modeling of ligand - target interactions. The development of mirror-image TSR keys of Cα atom, in conjunction with the integration of Cα TSR keys with all-atom TSR keys for amino acids and drugs, will lead to a new and promising computational method for aiding drug design and discovery.

The specific applications of the TSR-based method in identifying Zn2+ binding sites of proteases and ACE/ACE2.

We have developed an alignment-free TSR (Triangular Spatial Relationship)-based computational method for protein structural comparison and motif identification and discovery. To demonstrate the potential applications of the method, we have generated two datasets. One dataset contains five classes: Actin/Hsp70, serine protease (chymotrypsin/trypsin/elastase), ArsC/Prdx2, PKA/PKB/PKC, and AChE/BChE at the hierarchical level 1 and twelve groups at the level 2. The other dataset includes representative proteases and ACE/ACE2. The x,y, z coordinates of the structures were obtained from PDB. We calculated the keys (or features) that represent each structure using the TSR-based method. The dataset and data presented here include additional information that help the readers become aware of specific applications of the TSR-based method in protein clustering, identification and discovery of metal ion binding sites as well as to understand the effect of amino acid grouping on protein 3D structural relationships at both global and local levels.

Examining the COVID-19 case growth rate due to visitor vs. local mobility in the United States using machine learning.

Travel patterns and mobility affect the spread of infectious diseases like COVID-19. However, we do not know to what extent local vs. visitor mobility affects the growth in the number of cases. This study evaluates the impact of state-level local vs. visitor mobility in understanding the growth with respect to the number of cases for COVID spread in the United States between March 1, 2020, and December 31, 2020. Two metrics, namely local and visitor transmission risk, were extracted from mobility data to capture the transmission potential of COVID-19 through mobility. A combination of the three factors: the current number of cases, local transmission risk, and the visitor transmission risk, are used to model the future number of cases using various machine learning models. The factors that contribute to better forecast performance are the ones that impact the number of cases. The statistical significance of the forecasts is also evaluated using the Diebold-Mariano test. Finally, the performance of models is compared for three waves across all 50 states. The results show that visitor mobility significantly impacts the case growth by improving the prediction accuracy by 33.78%. We also observe that the impact of visitor mobility is more pronounced during the first peak, i.e., March-June 2020.

Prisons and the pandemic: the panopticon plays out.

Fearing the spread of the COVID 19 infection in prisons, governments have released seemingly large numbers of prisoners in many countries. However, in terms of overall percentages, the releases have been less significant. In India, prisons are overcrowded with nearly 70% under trials. With very poor sanitation and healthcare facilities, there are numerous deaths in custody due to various illnesses, including heart, lung, liver and kidney-related ailments as well as HIV, tuberculosis and cancer. It is in this context that the need to take urgent steps to contain the spread of the contagion inside prisons became apparent to the prison authorities around the time the national lockdown was announced by the Government of India. Some of the measures taken by prison departments include creation of temporary prisons to admit new prisoners and keep them in quarantine for 14 days before transferring them to the regular prisons, transfer of prisoners from congested prisons to less crowded prisons, making prisoners aware of the virus, setting up Corona care centres inside or outside prisons, etc. While prison authorities claim that they were taking necessary preventive steps within existing limitations, there has been some criticism of the inadequacy of these measures and the conditions inside prisons. The Supreme Court of India took suo moto cognizance of the issue and asked state governments to constitute a High Powered Committees to identify categories of prisoners who could be released. The article highlights that the criteria for release was based on the purported seriousness of the offence rather than vulnerability to getting infected. Also, the absence of gender disaggregated data on release of prisoners indicates the lack of importance given to the needs of women prisoners. It can be said that the policies and measures taken towards preventing the spread of COVID 19 pandemic in prisons has reinforced existing biases against prisoners. The major concern of the prison and judicial authorities was to somehow manage the situation without disturbing the 'dangerousness' and risk to 'law and order' narrative about prisoners. The article concludes by drawing attention to the fact that the Foucauldian principles of segregation, segmentation and surveillance were maintained in the decision making processes relating to treatment of prisoners during the pandemic.

Association mining based approach to analyze COVID-19 response and case growth in the United States.

Containing the COVID-19 pandemic while balancing the economy has proven to be quite a challenge for the world. We still have limited understanding of which combination of policies have been most effective in flattening the curve; given the challenges of the dynamic and evolving nature of the pandemic, lack of quality data etc. This paper introduces a novel data mining-based approach to understand the effects of different non-pharmaceutical interventions in containing the COVID-19 infection rate. We used the association rule mining approach to perform descriptive data mining on publicly available data for 50 states in the United States to understand the similarity and differences among various policies and underlying conditions that led to transitions between different infection growth curve phases. We used a multi-peak logistic growth model to label the different phases of infection growth curve. The common trends in the data were analyzed with respect to lockdowns, face mask mandates, mobility, and infection growth. We observed that face mask mandates combined with mobility reduction through moderate stay-at-home orders were most effective in reducing the number of COVID-19 cases across various states.

Exploring the relationship between mobility and COVID- 19 infection rates for the second peak in the United States using phase-wise association.

Human mobility plays an important role in the dynamics of infectious disease spread. Evidence from the initial nationwide lockdowns for COVID- 19 indicates that restricting human mobility is an effective strategy to contain the spread. While a direct correlation was observed early on, it is not known how mobility impacted COVID- 19 infection growth rates once lockdowns are lifted, primarily due to modulation by other factors such as face masks, social distancing, and the non-linear patterns of both mobility and infection growth. This paper introduces a piece-wise approach to better explore the phase-wise association between state-level COVID- 19 incidence data and anonymized mobile phone data for various states in the United States. Prior literature analyzed the linear correlation between mobility and the number of cases during the early stages of the pandemic. However, it is important to capture the non-linear dynamics of case growth and mobility to be usable for both tracking and forecasting COVID- 19 infections, which is accomplished by the piece-wise approach. The associations between mobility and case growth rate varied widely for various phases of the epidemic curve when the stay-at-home orders were lifted. The mobility growth patterns had a strong positive association of 0.7 with the growth in the number of cases, with a lag of 5 to 7 weeks, for the fast-growth phase of the pandemic, for only 20 states that had a peak between July 1st and September 30, 2020. Overall though, mobility cannot be used to predict the rise in the number of cases after initial lockdowns have been lifted. Our analysis explores the gradual diminishing value of mobility associations in the later stage of the outbreak. Our analysis indicates that the relationship between mobility and the increase in the number of cases, once lockdowns have been lifted, is tenuous at best and there is no strong relationship between these signals. But we identify the remnants of the last associations in specific phases of the growth curve.

Exploring the effectiveness of the TSR-based protein 3-D structural comparison method for protein clustering, and structural motif identification and discovery of protein kinases, hydrolases, and SARS-CoV-2's protein via the application of amino acid grouping.

Development of protein 3-D structural comparison methods is essential for understanding protein functions. Some amino acids share structural similarities while others vary considerably. These structures determine the chemical and physical properties of amino acids. Grouping amino acids with similar structures potentially improves the ability to identify structurally conserved regions and increases the global structural similarity between proteins. We systematically studied the effects of amino acid grouping on the numbers of Specific/specific, Common/common, and statistically different keys to achieve a better understanding of protein structure relations. Common keys represent substructures found in all types of proteins and Specific keys represent substructures exclusively belonging to a certain type of proteins in a data set. Our results show that applying amino acid grouping to the Triangular Spatial Relationship (TSR)-based method, while computing structural similarity among proteins, improves the accuracy of protein clustering in certain cases. In addition, applying amino acid grouping facilitates the process of identification or discovery of conserved structural motifs. The results from the principal component analysis (PCA) demonstrate that applying amino acid grouping captures slightly more structural variation than when amino acid grouping is not used, indicating that amino acid grouping reduces structure diversity as predicted. The TSR-based method uniquely identifies and discovers binding sites for drugs or interacting proteins. The binding sites of nsp16 of SARS-CoV-2, SARS-CoV and MERS-CoV that we have defined will aid future antiviral drug design for improving therapeutic outcome. This approach for incorporating the amino acid grouping feature into our structural comparison method is promising and provides a deeper insight into understanding of structural relations of proteins.

Frontline Healthcare Workers' Knowledge, Perception and Risk Prevention Practices Regarding COVID-19 in Afghanistan: A Cross-Sectional Study.

(1) Background: As of 13 December 2020, Afghanistan reported around 48,952 confirmed COVID-19 cases and 1960 deaths. Lack of knowledge and perceptions among healthcare workers (HCWs) can pose challenges to disease control. Therefore, targeted, timely assessment of knowledge and perceptions are needed to address practices that might hinder efforts to stop the spreading of COVID-19 in Afghanistan. This study aimed to assess COVID-19-related knowledge, perceptions, and risk prevention practices (KPP) among frontline HCWs in Afghanistan; (2) Methods: A cross-sectional study was conducted with the support of field teams who were deployed in Afghanistan, surveyed from 14 to 22 April 2020 in eight provinces in Afghanistan with varying cumulative incidence of COVID-19 cases. A 28-item KPP survey instrument was adapted from other internationally validated questionnaires related to COVID-19. (3) Results: The survey was conducted among 213 frontline HCWs engaged in screening and treating COVID-19 patients. Survey results indicated that basic awareness of COVID-19 was 100% across all the participants. Knowledge and understanding of COVID-19 transmission, symptoms, incubation period and complications associated with COVID-19 are comprehensive and high (>90%), except available treatment for COVID-19 (84%). HCWs' perceptions towards the prevention and control of COVID-19 were positive. However, only 63% believed that the use of N-95 face masks and disposable and fluid-resistant gowns (76%) could prevent COVID-19 transmission. This survey showed high knowledge and positive perception (72%), and only 48% of frontline HCWs had shown risk prevention practices. Addressing their perceptions and placing additional focus on practices across all health facilities is recommended as a preparedness measure.

Enhancement of the chiroptical response of α-amino acids via N-substitution for molecular structure determination using vibrational circular dichroism.

The chiroptical response in the form of vibrational circular dichroism (VCD) in the midinfrared region is found to be enhanced when a hydrogen of amino group of l-tryptophan is substituted with acetyl, acryloyl, or maleyl group. The order of preference for VCD enhancement is found to be acryloyl > acetyl > maleyl group. The resulting experimental VCD spectra are also found to be satisfactorily reproduced by the quantum mechanical (QM) predicted spectra. The QM predicted spectra were simulated using the conformer populations, (a) predicted by Gibbs energies and (b) optimized to maximize the similarity between experimental and predicted VCD spectra. It is found that the conformer populations predicted by Gibbs energies do not yield the maximum possible similarity between experimental and the QM predicted spectra. This work identifies the N-substitution of α-amino acids and determining the conformer populations that best reproduce the experimental spectra as two new approaches for molecular structure determination.

Development of a TSR-Based Method for Protein 3-D Structural Comparison With Its Applications to Protein Classification and Motif Discovery.

Development of protein 3-D structural comparison methods is important in understanding protein functions. At the same time, developing such a method is very challenging. In the last 40 years, ever since the development of the first automated structural method, ~200 papers were published using different representations of structures. The existing methods can be divided into five categories: sequence-, distance-, secondary structure-, geometry-based, and network-based structural comparisons. Each has its uniqueness, but also limitations. We have developed a novel method where the 3-D structure of a protein is modeled using the concept of Triangular Spatial Relationship (TSR), where triangles are constructed with the Cα atoms of a protein as vertices. Every triangle is represented using an integer, which we denote as "key," A key is computed using the length, angle, and vertex labels based on a rule-based formula, which ensures assignment of the same key to identical TSRs across proteins. A structure is thereby represented by a vector of integers. Our method is able to accurately quantify similarity of structure or substructure by matching numbers of identical keys between two proteins. The uniqueness of our method includes: (i) a unique way to represent structures to avoid performing structural superimposition; (ii) use of triangles to represent substructures as it is the simplest primitive to capture shape; (iii) complex structure comparison is achieved by matching integers corresponding to multiple TSRs. Every substructure of one protein is compared to every other substructure in a different protein. The method is used in the studies of proteases and kinases because they play essential roles in cell signaling, and a majority of these constitute drug targets. The new motifs or substructures we identified specifically for proteases and kinases provide a deeper insight into their structural relations. Furthermore, the method provides a unique way to study protein conformational changes. In addition, the results from CATH and SCOP data sets clearly demonstrate that our method can distinguish alpha helices from beta pleated sheets and vice versa. Our method has the potential to be developed into a powerful tool for efficient structure-BLAST search and comparison, just as BLAST is for sequence search and alignment.

Peyssonnosides A-B, Unusual Diterpene Glycosides with a Sterically Encumbered Cyclopropane Motif: Structure Elucidation Using an Integrated Spectroscopic and Computational Workflow.

Two sulfated diterpene glycosides featuring a highly substituted and sterically encumbered cyclopropane ring have been isolated from the marine red alga Peyssonnelia sp. Combination of a wide array of 2D NMR spectroscopic experiments, in a systematic structure elucidation workflow, revealed that peyssonnosides A-B (1-2) represent a new class of diterpene glycosides with a tetracyclo [7.5.0.01,10.05,9] tetradecane architecture. A salient feature of this workflow is the unique application of quantitative interproton distances obtained from the rotating frame Overhauser effect spectroscopy (ROESY) NMR experiment, wherein the ß-d-glucose moiety of 1 was used as an internal probe to unequivocally determine the absolute configuration, which was also supported by optical rotatory dispersion (ORD). Peyssonnoside A (1) exhibited promising activity against liver stage Plasmodium berghei and moderate antimethicillin-resistant Staphylococcus aureus (MRSA) activity, with no cytotoxicity against human keratinocytes. Additionally, 1 showed strong growth inhibition of the marine fungus Dendryphiella salina indicating an antifungal ecological role in its natural environment. The high natural abundance and novel carbon skeleton of 1 suggests a rare terpene cyclase machinery, exemplifying the chemical diversity in this phylogenetically distinct marine red alga.

Absolute Configurations of Naturally Occurring [5]- and [3]-Ladderanoic Acids: Isolation, Chiroptical Spectroscopy, and Crystallography.

We have isolated mixtures of [5]- and [3]-ladderanoic acids 1a and 2a from the biomass of an anammox bioreactor and have separated the acids and their phenacyl esters for the first time by HPLC. The absolute configurations of the naturally occurring acids and their phenacyl esters are assigned as R at the site of side-chain attachment by comparison of experimental specific rotations with corresponding values predicted using quantum chemical (QC) methods. The absolute configurations for 1a and 2a were independently verified by comparison of experimental Raman optical activity spectra with corresponding spectra predicted using QC methods. The configurational assignments of 1a and 2a and of the phenacyl ester of 1a were also confirmed by X-ray crystallography.

Reaching for the Stars in the Brain: Polymer-Mediated Gene Delivery to Human Astrocytes.

Astrocytes, the "star-shaped" glial cells, are appealing gene-delivery targets to treat neurological diseases due to their diverse roles in brain homeostasis and disease. Cationic polymers have successfully delivered genes to mammalian cells and hence present a viable, non-immunogenic alternative to widely used viral vectors. In this study, we investigated the gene delivery potential of a series of arginine- and polyethylene glycol-modified, siloxane-based polyethylenimine analogs in primary cultured human neural cells (neurons and astrocytes) and in mice. Plasmid DNAs encoding luciferase reporter were used to measure gene expression. We hypothesized that polyplexes with arginine would help in cellular transport of the DNA, including across the blood-brain barrier; polyethylene glycol will stabilize polyethylenimine and reduce its toxicity while maintaining its DNA-condensing ability. Polyplexes were non-toxic to human neural cells and red blood cells. Cellular uptake of polyplexes and sustained gene expression were seen in human astrocytes as well as in mouse brains post-intravenous-injections. The polyplexes also delivered and expressed genes driven by astrocyte-restricted glial fibrillary acidic protein promoters, which are weaker than viral promoters. To our knowledge, the presented work validates a biocompatible and effective polymer-facilitated gene-delivery system for both human brain cells and mice for the first time.

Role of Bisphosphonates as Adjuvants of Surgery in Giant Cell Tumor of Spine.

BACKGROUND: The role of bisphosphonates is well established in giant cell tumor of bone (GCTB) of extremities, but its role in spine GCTB is still not established. Our main purpose was to evaluate the role of bisphosphonates in spinal GCTB with the help of radiologic assessment. METHODS: A retrospective analysis of all spine GCTB patients who underwent an operation from July 2005 to January 2014 was done. Patients of spine GCTB in whom bisphosphonates were given constituted the study group. This group was compared to patients in whom bisphosphonates were not given. Preoperative and postoperative radiographs and CT scans were studied. A thorough evaluation of the presence of sclerosis was done on them. Bisphosphonates were considered to be effective if either sclerosis or new bone formation was present. RESULTS: A total of 13 cases of spine GCT underwent operation from July 2005 to January 2014. All patients of GCTB spine who underwent an operation after 2008 at our institute were given bisphosphonates postoperatively. Of 13 cases, bisphosphonates were given postoperatively in 6 patients: 5 patients were female and 1 patient was male. Of these 6 patients, 3 patients had sacrum GCTB and 1 patient each had T9, T11, and L5 vertebrae GCTB. Average follow-up period was 39.33 months (minimum follow-up was 18 months and maximum follow-up was 72 months). Postoperative sclerosis was present in all 6 patients. No recurrence of the tumor was present in the bisphosphonate group, but 2 patients had a recurrence in the group that did not receive bisphosphonates. CONCLUSIONS: Bisphosphonates are effective and safe adjuvant therapy along with appropriate surgical intervention in spinal GCTBs and may have a role in decreasing the recurrence of this tumor.

Specific optical rotation is a versatile tool for the identification of critical micelle concentration and micellar growth of tartaric acid-based diastereomeric amphiphiles.

Four novel tartaric acid-based diastereomeric chiral amphiphiles, two being enantiomers of the other two, have been synthesized and investigated using chiroptical spectroscopic methods, along with tensiometry and dynamic light scattering experiments. We found that an inflection point in specific optical rotation (SOR) values at ~0.32 mM corresponds to the critical micelle concentration (CMC). The increase in magnitude of SOR values beyond CMC corresponds to the growth of aggregates. For enantiomers, oppositely signed SOR values were observed, ruling out the possibility for the presence of aggregation size mediated artefacts. SOR values did not exhibit concentration dependence for a chiral tartaric acid based non-aggregating analogue further establishing the absence of artefacts or anomalous interaction of tartaric acid based head group with solvent. Electronic circular dichroism spectra showed no significant changes in band positions or intensities with concentration. Due to the requirement for higher concentrations (~200 mM) needed to obtain vibrational circular dichroism spectra, these measurements are not found to be useful for studying concentration dependent properties of chiral amphiphiles.