The Acute Effects of the Use of Salbutamol and Ipratropium on the Heart Rates of Patients With Obstructive Airway Disease.

Background The cornerstone of pharmaceutical therapy for obstructive airway illnesses involves inhalation of bronchodilators, such as ipratropium bromide (IP) and salbutamol (SB). The heart rate regulation may be changed by ß-2 agonists and anticholinergic medications. Investigating the impact of inhaled SB and IP on the heart rate was the goal of this study. Methods A total of 304 patients were enrolled in this investigation. Baseline demographic characteristics, medical history, and adverse events were documented. Their heart rates were monitored before and after bronchodilator administration. SB and IP were selected based on historical usage. Blood pressure readings were also taken before and after each session. Results There was a significant increase in heart rates after SB from a mean of 106.69 to 117.20. Similarly, the heart rate of the patients in the IP group increased to a mean of 106.95 from 93.44, with a statistically significant p-value. Moreover, tremors were the most common adverse effect, accounting for 85.3% of the patients in the IP group and 75% in the SB group. In contrast, palpitation was more common in the SB group 25% vs. 14.7% with a significant p-value. Conclusion Frequently administered dosages of SB and IP caused a considerable increase in heart rates, as well as tremors and palpitation.

COYOTE: Sequence-derived structural descriptors-based computational identification of glycoproteins.

Glycoproteins play an important and ubiquitous role in many biological processes such as protein folding, cell-to-cell signaling, invading microorganism infection, tumor metastasis, and leukocyte trafficking. The key mechanism of glycoproteins must be revealed to model and refine glycosylated protein recognition, which will eventually assist in the design and discovery of carbohydrate-derived therapeutics. Experimental procedures involving wet-lab experiments to reveal glycoproteins are very time-consuming, laborious, and highly costly. However, costly and tedious experimental procedures can be assisted by ranking the most probable glycoproteins through computational methods with improved accuracy. In this study, we have proposed a novel machine learning-based predictive model for glycoproteins identification. Our proposed model is based on sequence-derived structural descriptors (SDSD) that fill the gap of unavailability of protein 3D structures and lack of accuracy in sequence information alone. Through a series of simulation studies, we have shown that our proposed model gives state-of-the-art generalization performance verified through various machine learning-centric and biologically relevant techniques and metrics. Through data mining in this study, we have also identified the role of descriptors in determining glycoproteins. Python-based standalone code together with a webserver implementation of our proposed model (COYOTE: identifiCation Of glYcoprOteins Through sEquences) is available at the URL: https://sites.google.com/view/wajidarshad/software.

Surficial grafting of organoimido moieties enhances the capacity performance of oxometallic clusters.

Molybdenum trioxide (MoO3) with a theoretical specific capacity of 1117 mA h g-1 is widely considered a promising anode material for lithium-ion batteries. However, the irreversible conversion reactions, low electrical conductivity, and detrimental volume expansion upon Li intercalation between the one-dimensional layered structures of MoO3 hinder its practical implementation. Herein, we report a facile synthetic protocol that allows surficial modification by replacing the terminal and bridging oxo groups of molybdenum oxide clusters. Successful organoimido functionalization resulted in a large cathodic shift in Mo(VI/V) reduction by 0.6 V, pronounced electronic communication between the organic moiety and the metal-oxide unit, and significant increase in electrical conductivity (80-100 Ω interfacial charge-transfer resistance). Combined with the enlarged active surface area due to the structural hindrance induced by the organic functionality, the steady specific capacity of the organoimido-modified molybdenum oxide clusters was greater than 1200 mA h g-1 at 900 mA g-1 at the end of 360 cycles, where the best value of 1653 mA h g-1 was achieved for the nitroaniline-substituted species. The steady capacity of 480 mA h g-1 was achieved in the fast charge-discharge process (3000 mA g-1) over 1400 cycles. The results indicate that the surficial modification of metal oxides with organo moieties using our facile synthetic method has broad application potential for metal oxides to be used as high-capacity electrode materials in the future.

Sweetening Lithium Metal Interface by High Surface and Adhesive Energy Coating of Crystalline α-d-Glucose Film to Inhibit Dendrite Growth.

The notorious growth of lithium (Li) dendrites and the instability of the solid electrolyte interface (SEI) during cycling make Li metal anodes unsuitable for use in commercial Li-ion batteries. Herein, the use of simple sugar coating (α-d-glucose) is demonstrated on top of Li metal to halt the growth of Li dendrites and stabilize the SEI. The α-d-glucose layer possesses high surface and adhesive energies toward Li, which promote the homogenous stripping and plating of Li ions on top of the Li metal. Density functional theory reveals that Li-ion diffusion within the α-d-glucose layer is governed by hopping around the bare sides of the O atoms and along the apparent passages formed by the glucose molecules. Stable cycling performance is achieved when combining α-d-glucose-coated Li (G|Li) anodes with sulfur- and LiFePO4 -based cathodes in both LiTFSI (ether) and LiPF6 (carbonate) electrolyte systems. A G|Li-based symmetrical cell operates at a current density of 1 mA cm-2 and areal capacity of 1 mAh cm-2 displays a stable overpotential profile for over 9 months (7000 h) of continuous charge/discharge cycling.

SIP: A computational prediction of S-Adenosyl methionine (SAM) interacting proteins and their interaction sites through primary structures.

S-Adenosyl methionine (SAM), a universal methyl group donor, plays a vital role in biosynthesis and acts as an inhibitor to many enzymes. Due to protein interaction-dependent biological role, SAM has become a favorite target in various therapeutical and clinical studies such as treating cancer, Alzheimer's, epilepsy, and neurological disorders. Therefore, the identification of the SAM interacting proteins and their interaction sites is a biologically significant problem. However, wet-lab techniques, though accurate, to identify SAM interactions and interaction sites are tedious and costly. Therefore, efficient and accurate computational methods for this purpose are vital to the design and assist such wet-lab experiments. In this study, we present machine learning-based models to predict SAM interacting proteins and their interaction sites by using only primary structures of proteins. Here we modeled SAM interaction prediction through whole protein sequence features along with different classifiers. Whereas, we modeled SAM interaction site prediction through overlapping sequence windows and ranking with multiple instance learning that allows handling imprecisely annotated SAM interaction sites. Through a series of simulation studies along with biological significant evaluation, we showed that our proposed models give a state-of-the-art performance for both SAM interaction and interaction site prediction. Through data mining in this study, we have also identified various characteristics of amino acid sub-sequences and their relative position to effectively locate interaction sites in a SAM interacting protein. Python code for training and evaluating our proposed models together with a webserver implementation as SIP (Sam Interaction Predictor) is available at the URL: https://sites.google.com/view/wajidarshad/software.

PANDA: Predicting the change in proteins binding affinity upon mutations by finding a signal in primary structures.

Accurately determining a change in protein binding affinity upon mutations is important to find novel therapeutics and to assist mutagenesis studies. Determination of change in binding affinity upon mutations requires sophisticated, expensive, and time-consuming wet-lab experiments that can be supported with computational methods. Most of the available computational prediction techniques depend upon protein structures that bound their applicability to only protein complexes with recognized 3D structures. In this work, we explore the sequence-based prediction of change in protein binding affinity upon mutation and question the effectiveness of [Formula: see text]-fold cross-validation (CV) across mutations adopted in previous studies to assess the generalization ability of such predictors with no known mutation during training. We have used protein sequence information instead of protein structures along with machine learning techniques to accurately predict the change in protein binding affinity upon mutation. Our proposed sequence-based novel change in protein binding affinity predictor called PANDA performs comparably to the existing methods gauged through an appropriate CV scheme and an external independent test dataset. On an external test dataset, our proposed method gives a maximum Pearson correlation coefficient of 0.52 in comparison to the state-of-the-art existing protein structure-based method called MutaBind which gives a maximum Pearson correlation coefficient of 0.59. Our proposed protein sequence-based method, to predict a change in binding affinity upon mutations, has wide applicability and comparable performance in comparison to existing protein structure-based methods. We made PANDA easily accessible through a cloud-based webserver and python code available at https://sites.google.com/view/wajidarshad/software and https://github.com/wajidarshad/panda, respectively.

COVIDC: An expert system to diagnose COVID-19 and predict its severity using chest CT scans: Application in radiology.

Early diagnosis of Coronavirus disease 2019 (COVID-19) is significantly important, especially in the absence or inadequate provision of a specific vaccine, to stop the surge of this lethal infection by advising quarantine. This diagnosis is challenging as most of the patients having COVID-19 infection stay asymptomatic while others showing symptoms are hard to distinguish from patients having different respiratory infections such as severe flu and Pneumonia. Due to cost and time-consuming wet-lab diagnostic tests for COVID-19, there is an utmost requirement for some alternate, non-invasive, rapid, and discounted automatic screening system. A chest CT scan can effectively be used as an alternative modality to detect and diagnose the COVID-19 infection. In this study, we present an automatic COVID-19 diagnostic and severity prediction system called COVIDC (COVID-19 detection using CT scans) that uses deep feature maps from the chest CT scans for this purpose. Our newly proposed system not only detects COVID-19 but also predicts its severity by using a two-phase classification approach (COVID vs non-COVID, and COVID-19 severity) with deep feature maps and different shallow supervised classification algorithms such as SVMs and random forest to handle data scarcity. We performed a stringent COVIDC performance evaluation not only through 10-fold cross-validation and an external validation dataset but also in a real setting under the supervision of an experienced radiologist. In all the evaluation settings, COVIDC outperformed all the existing state-of-the-art methods designed to detect COVID-19 with an F1 score of 0.94 on the validation dataset and justified its use to diagnose COVID-19 effectively in the real setting by classifying correctly 9 out of 10 COVID-19 CT scans. We made COVIDC openly accessible through a cloud-based webserver and python code available at https://sites.google.com/view/wajidarshad/software and https://github.com/wajidarshad/covidc.

Multiscale based nonlinear dynamics analysis of heart rate variability signals.

Acceleration change index (ACI) is a fast and easy to understand heart rate variability (HRV) analysis approach used for assessing cardiac autonomic control of the nervous systems. The cardiac autonomic control of the nervous system is an example of highly integrated systems operating at multiple time scales. Traditional single scale based ACI did not take into account multiple time scales and has limited capability to classify normal and pathological subjects. In this study, a novel approach multiscale ACI (MACI) is proposed by incorporating multiple time scales for improving the classification ability of ACI. We evaluated the performance of MACI for classifying, normal sinus rhythm (NSR), congestive heart failure (CHF) and atrial fibrillation subjects. The findings reveal that MACI provided better classification between healthy and pathological subjects compared to ACI. We also compared MACI with other scale-based techniques such as multiscale entropy, multiscale permutation entropy (MPE), multiscale normalized corrected Shannon entropy (MNCSE) and multiscale permutation entropy (IMPE). The preliminary results show that MACI values are more stable and reliable than IMPE and MNCSE. The results show that MACI based features lead to higher classification accuracy.

Intracranial hemorrhage complicating anticoagulant prophylactic therapy in three hospitalized COVID-19 patients.

SARS-CoV2 has led to a global pandemic affecting almost 3 million people in almost over 3 months. Various clinical presentations have been reported so far and no definite therapy is established. Anticoagulation is recommended by several experts to address the potential prothrombotic complications from COVID-19, but its safety and regimen need further clinical trials and safety and efficacy profile. Here, we present three cases of intracranial hemorrhage in three critically ill patients with COVID-19 and discuss their course in relation to various regimens of anticoagulation used.

An Index Case of Diffuse Alveolar Hemorrhage Secondary to Chikungunya.

Chikungunya virus (CHIKV) is an alphavirus transmitted by mosquitoes, mostly by Aedes aegypti and Aedes albopictus. It is starting to become a very common entity in Pakistan, with a wide range of clinical manifestations. Here we report a case of a young male with CHIKV who presented with a clinical manifestation of diffuse alveolar hemorrhage which has not been observed so far in a patient suffering from this illness.

A lithium passivated MoO3 nanobelt decorated polypropylene separator for fast-charging long-life Li-S batteries.

Dissolution of lithium polysulfide (LiPS) into the electrolyte during discharging, causing shuttling of LiPS from the cathode to the lithium (Li) metal, is mainly responsible for the capacity decay and short battery life of lithium-sulfur batteries (LSBs). Herein, we designed a separator comprising polypropylene (PP) coated with MoO3 nanobelts (MNBs), prepared through facile grinding of commercial MoO3 powder. The formation of Li2Sn-MoO3 during discharging inhibited the polysulfide shuttling; during charging, Li passivated LixMoO3 facilitated ionic transfer during the redox reaction by decreasing the charge transfer resistance. This dual-interaction mechanism of LiPS-with both Mo and the formation of LixMoO3-resulted in a substantially high initial discharge capacity at a very high current density of 5C, with 29.4% of the capacity retained after 5000 cycles. The simple fabrication approach and extraordinary cycle life observed when using this MNB-coated separator suggest a scalable solution for future commercialization of LSBs.

Mitigating Metal Dendrite Formation in Lithium-Sulfur Batteries via Morphology-Tunable Graphene Oxide Interfaces.

Despite issues related to dendrite formation, research on Li metal anodes has resurged because of their high energy density. In this study, graphene oxide (GO) layers are decorated onto Li metal anodes through a simple process of drop-casting and spray-coating. The self-assembly of GO is exploited to synthesize coatings having compact, mesoporous, and macroporous morphologies. The abilities of the GO coatings to suppress dendrite formation are compared through Li|Li symmetrical cell charging at a current density of 5 mA cm-2 for 2000 cycles-a particularly abusive test. The macroporous structure possesses the lowest impedance, whereas the compact structure excels in terms of stability. Moreover, GO exhibits a low nucleation overpotential and is transformed into reduced GO with enhanced conductivity during the operation of the cells; both factors synergistically mitigate the issue of dendrite formation. Li-S batteries incorporating the GO-decorated Li anodes exhibit an initial capacity of 850 mA h g-1 and maintain their stability for 800 cycles at a C-rate of 1 C (1675 mA h g-1), suggesting the applicability of GO in future rechargeable batteries.

Modified Separator Performing Dual Physical/Chemical Roles to Inhibit Polysulfide Shuttle Resulting in Ultrastable Li-S Batteries.

In this paper we describe a modified (AEG/CH) coated separator for Li-S batteries in which the shuttling phenomenon of the lithium polysulfides is restrained through two types of interactions: activated expanded graphite (AEG) flakes interacted physically with the lithium polysulfides, while chitosan (CH), used to bind the AEG flakes on the separator, interacted chemically through its abundance of amino and hydroxyl functional groups. Moreover, the AEG flakes facilitated ionic and electronic transfer during the redox reaction. Live H-cell discharging experiments revealed that the modified separator was effective at curbing polysulfide shuttling; moreover, X-ray photoelectron spectroscopy analysis of the cycled separator confirmed the presence of lithium polysulfides in the AEG/CH matrix. Using this dual functional interaction approach, the lifetime of the pure sulfur-based cathode was extended to 3000 cycles at 1C-rate (1C = 1670 mA/g), decreasing the decay rate to 0.021% per cycle, a value that is among the best reported to date. A flexible battery based on this modified separator exhibited stable performance and could turn on multiple light-emitting diodes. Such modified membranes with good mechanical strength, high electronic conductivity, and anti-self-discharging shield appear to be a scalable solution for future high-energy battery systems.

Inverse Correlation between Heart Rate Variability and Heart Rate Demonstrated by Linear and Nonlinear Analysis.

The dynamical fluctuations in the rhythms of biological systems provide valuable information about the underlying functioning of these systems. During the past few decades analysis of cardiac function based on the heart rate variability (HRV; variation in R wave to R wave intervals) has attracted great attention, resulting in more than 17000-publications (PubMed list). However, it is still controversial about the underling mechanisms of HRV. In this study, we performed both linear (time domain and frequency domain) and nonlinear analysis of HRV data acquired from humans and animals to identify the relationship between HRV and heart rate (HR). The HRV data consists of the following groups: (a) human normal sinus rhythm (n = 72); (b) human congestive heart failure (n = 44); (c) rabbit sinoatrial node cells (SANC; n = 67); (d) conscious rat (n = 11). In both human and animal data at variant pathological conditions, both linear and nonlinear analysis techniques showed an inverse correlation between HRV and HR, supporting the concept that HRV is dependent on HR, and therefore, HRV cannot be used in an ordinary manner to analyse autonomic nerve activity of a heart.