Antiviral peptides inhibiting the main protease of SARS-CoV-2 investigated by computational screening and in vitro protease assay.

The main protease (Mpro) of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) plays an important role in viral replication and transcription and received great attention as a vital target for drug/peptide development. Therapeutic agents such as small-molecule drugs or peptides that interact with the Cys-His present in the catalytic site of Mpro are an efficient way to inhibit the protease. Although several emergency-approved vaccines showed good efficacy and drastically dropped the infection rate, evolving variants are still infecting and killing millions of people globally. While a small-molecule drug (Paxlovid) received emergency approval, small-molecule drugs have low target specificity and higher toxicity. Besides small-molecule drugs, peptide therapeutics are thus gaining increasing popularity as they are easy to synthesize and highly selective and have limited side effects. In this study, we investigated the therapeutic value of 67 peptides targeting Mpro using molecular docking. Subsequently, molecular dynamics (MD) simulations were implemented on eight protein-peptide complexes to obtain molecular-level information on the interaction between these peptides and the Mpro active site, which revealed that temporin L, indolicidin, and lymphocytic choriomeningitis virus (LCMV) GP1 are the best candidates in terms of stability, interaction, and structural compactness. These peptides were synthesized using the solid-phase peptide synthesis protocol, purified by reversed-phase high-performance liquid chromatography (RP-HPLC), and authenticated by mass spectrometry (MS). The in vitro fluorometric Mpro activity assay was used to validate the computational results, where temporin L and indolicidin were observed to be very active against SARS-CoV-2 Mpro with IC50 values of 38.80 and 87.23 µM, respectively. A liquid chromatography-MS (LC-MS) assay was developed, and the IC50 value of temporin L was measured at 23.8 µM. The solution-state nuclear magnetic resonance (NMR) structure of temporin L was determined in the absence of sodium dodecyl sulfate (SDS) micelles and was compared to previous temporin structures. This combined investigation provides critical insights and assists us to further develop peptide inhibitors of SARS-CoV-2 Mpro through structural guided investigation.

Expression Dynamics of CA IX Epitope in Cancer Cells under Intermittent Hypoxia Correlates with Extracellular pH Drop and Cell Killing by Ureido-Sulfonamide CA IX Inhibitors.

Carbonic anhydrase IX (CA IX) is a membrane-bound CA isozyme over-expressed in many hypoxic tumor cells, where it ensures pH homeostasis and has been implicated in tumor survival, metastasis and resistance to chemotherapy and radiotherapy. Given the functional importance of CA IX in tumor biochemistry, we investigated the expression dynamics of CA IX in normoxia, hypoxia and intermittent hypoxia, which are typical conditions experienced by tumor cells in aggressive carcinomas. We correlated the CA IX epitope expression dynamics with extracellular pH acidification and with viability of CA IX-expressing cancer cells upon treatment with CA IX inhibitors (CAIs) in colon HT-29, breast MDA-MB-231 and ovarian SKOV-3 tumor cell models. We observed that the CA IX epitope expressed under hypoxia by these cancer cells is retained in a significant amount upon reoxygenation, probably to preserve their proliferation ability. The extracellular pH drop correlated well with the level of CA IX expression, with the intermittent hypoxic cells showing a similar pH drop to fully hypoxic ones. All cancer cells showed higher sensitivity to CA IX inhibitors (CAIs) under hypoxia as compared to normoxia. The tumor cell sensitivity to CAIs under hypoxia and intermittent hypoxia were similar and higher than in normoxia and appeared to be correlated with the lipophilicity of the CAI.

Potential antiviral peptides against the nucleoprotein of SARS-CoV-2.

Nucleoprotein is a conserved structural protein of SARS-CoV-2, which is involved in several functions, including replication, packaging, and transcription. In this research, 21 antiviral peptides that are known to have inhibitory function against nucleoprotein in several other viruses, were screened computationally against the nucleoprotein of SARS-CoV-2. The complexes of five best performing peptides (AVP1142, AVP1145, AVP1148, AVP1150, AVP1155) with nucleoprotein were selected for subsequent screening via 5 ns molecular dynamics (MD) simulation. Two peptides, namely AVP1145 and AVP1155, came out as promising candidates and hence were selected for 200 ns MD simulation for further validation, incorporating a DMPC-based membrane environment. In the long MD simulation, both AVP1155 and AVP1145 utilized multiple residues-mainly aromatic, acidic, and nonpolar residues-as interacting points to remain in contact with the nucleoprotein and formed predominantly hydrogen bonds along with hydrophobic and electrostatic interactions. However, AVP1155 proved to be superior to AVP1145 when its complex with nucleoprotein was analyzed in terms of root-mean-square deviation, root-mean-square fluctuation, radius of gyration, solvent accessible surface area and free energy landscape. In a nutshell, the findings of this research may guide future studies in the development of selective peptide inhibitors of SARS-CoV-2 nucleoprotein. Supplementary Information: The online version contains supplementary material available at 10.1007/s11696-022-02514-4.

A Wideband Circularly Polarized Magnetoelectric Dipole Antenna for 5G Millimeter-Wave Communications.

In this paper, a wideband circularly polarized (CP) magnetoelectric (ME) dipole antenna operating at 28 GHz band was proposed for 5G millimeter-wave (mm-wave) communications. The antenna geometry included two metallic plates with extended hook-shaped strips at its principal diagonal position, and two corners of truncated metallic plates at the secondary diagonal position. The pair of metallic vias connected the modified strips to the ground plane to create the magnetic dipole. The L-shaped probe feed between the strips was used to excite the antenna. The antenna showed stable gain and wideband characteristics. The simulated and measured results showed that the proposed CP ME dipole antenna had an overlapping (|S11|< −10 dB impedance and 3 dB axial ratio) bandwidth of 18.1% (25−30 GHz), covering the frequency bands dedicated for 5G new radio communications. Moreover, an average gain of 8 dBic was achieved by the antenna throughout the operating bandwidth. The measured data verified the design concept, and the proposed antenna had a small footprint of 0.83 λo × 0.83 λo × 0.125 λo (λo is free space wavelength at the lowest operating frequency), suitable for its application in 5G smart devices and sensors.

Isolation and Gain Improvement of a Rectangular Notch UWB-MIMO Antenna.

This paper presents the performance improvement of a co-planar waveguide rectangularly notched UWB-MIMO antenna. The isolation and gain of the antenna are enhanced by using a parasitic isolator. The antenna consists of four microstrip patch antennas and an isolator. The UWB characteristic of the antenna is achieved by truncating the lower ends of the radiating patch by a semicircle. The rectangular notch characteristic is obtained by adding two electromagnetic bandgap structures on the backside of the antenna, which is attached to the radiator via shorting pin. The performance, especially the decoupling of the MIMO antenna is improved by using a novel parasitic decoupler, which is placed between the antennas to receive uncorrelated signals. The decoupling structure consists of a square shape metallic element with a circular slot inside and a half-semicircle slot edged at each corner. Four rectangular metallic stubs are extended from opposite parallel sides to improve further isolation. The simulated and measured results show that the antenna has a rectangular notch band (5.25-5.85 GHz) across the working band of 3-12.8 GHz. In addition, the antenna has a planar structure with an overall size of 60 × 60 × 1.52 mm3 and offers stable gain, reduced mutual coupling (<-21 dB), and lower envelop correlation (<0.001).

Repurposing fusion inhibitor peptide against SARS-CoV-2.

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is continuously evolving. Although several vaccines were approved, this pandemic is still a major threat to public life. Till date, no established therapies are available against SARS-CoV-2. Peptide inhibitors hold great promise for this viral pathogen due to their efficacy, safety, and specificity. In this study, seventeen antiviral peptides which were known to inhibit SARS-CoV-1 are collected and computationally screened against heptad repeat 1 (HR1) of the SARS-CoV-2 spike protein (S2). Out of 17 peptides, Fp13 and Fp14 showed better binding affinity toward HR1 compared to a control peptide EK1 (a modified pan-coronavirus fusion inhibitor) in molecular docking. To explore the time-dependent interactions of the fusion peptide with HR1, molecular dynamics simulation was performed incorporating lipid membrane. During 100 ns MD simulation, structural and energy parameters of Fp13-HR1 and Fp14-HR1 complexes demonstrated lower fluctuations compared to the control EK1-HR1 complex. Furthermore, principal component analysis and free energy landscape study revealed that these two peptides (Fp13 and Fp14) strongly bind to the HR1 with higher affinity than that of control EK1. Tyr917, Asn919, Gln926, lys933, and Gln949 residues in HR1 protein were found to be crucial residues for peptide interaction. Notably, Fp13, Fp14 showed reasonably better binding free energy and hydrogen bond contribution than that of EK1. Taken together, Fp13 and Fp14 peptides may be highly specific for HR1 which can potentially prevent the formation of the fusion core and could be further developed as therapeutics for treatment or prophylaxis of SARS-CoV-2 infection.

Enhancing prediction and analysis of UK road traffic accident severity using AI: Integration of machine learning, econometric techniques, and time series forecasting in public health research.

This research project explored into the intricacies of road traffic accidents severity in the UK, employing a potent combination of machine learning algorithms, econometric techniques, and traditional statistical methods to analyse longitudinal historical data. Our robust analysis framework includes descriptive, inferential, bivariate, multivariate methodologies, correlation analysis: Pearson's and Spearman's Rank Correlation Coefficient, multiple logistic regression models, Multicollinearity Assessment, and Model Validation. In addressing heteroscedasticity or autocorrelation in error terms, we've advanced the precision and reliability of our regression analyses using the Generalized Method of Moments (GMM). Additionally, our application of the Vector Autoregressive (VAR) model and the Autoregressive Integrated Moving Average (ARIMA) models have enabled accurate time series forecasting. With this approach, we've achieved superior predictive accuracy and marked by a Mean Absolute Scaled Error (MASE) of 0.800 and a Mean Error (ME) of -73.80 compared to a naive forecast. The project further extends its machine learning application by creating a random forest classifier model with a precision of 73%, a recall of 78%, and an F1-score of 73%. Building on this, we employed the H2O AutoML process to optimize our model selection, resulting in an XGBoost model that exhibits exceptional predictive power as evidenced by an RMSE of 0.1761205782994506 and MAE of 0.0874235576229789. Factor Analysis was leveraged to identify underlying variables or factors that explain the pattern of correlations within a set of observed variables. Scoring history, a tool to observe the model's performance throughout the training process was incorporated to ensure the highest possible performance of our machine learning models. We also incorporated Explainable AI (XAI) techniques, utilizing the SHAP (Shapley Additive Explanations) model to comprehend the contributing factors to accident severity. Features such as Driver_Home_Area_Type, Longitude, Driver_IMD_Decile, Road_Type, Casualty_Home_Area_Type, and Casualty_IMD_Decile were identified as significant influencers. Our research contributes to the nuanced understanding of traffic accident severity and demonstrates the potential of advanced statistical, econometric, machine learning techniques in informing evidence based interventions and policies for enhancing road safety.

Enhancing Clinical Validation for Early Cardiovascular Disease Prediction through Simulation, AI, and Web Technology.

Cardiovascular diseases (CVDs) remain a major global health challenge and a leading cause of mortality, highlighting the need for improved predictive models. We introduce an innovative agent-based dynamic simulation technique that enhances our AI models' capacity to predict CVD progression. This method simulates individual patient responses to various cardiovascular risk factors, improving prediction accuracy and detail. Also, by incorporating an ensemble learning model and interface of web application in the context of CVD prediction, we developed an AI dashboard-based model to enhance the accuracy of disease prediction and provide a user-friendly app. The performance of traditional algorithms was notable, with Ensemble learning and XGBoost achieving accuracies of 91% and 95%, respectively. A significant aspect of our research was the integration of these models into a streamlit-based interface, enhancing user accessibility and experience. The streamlit application achieved a predictive accuracy of 97%, demonstrating the efficacy of combining advanced AI techniques with user-centered web applications in medical prediction scenarios. This 97% confidence level was evaluated by Brier score and calibration curve. The design of the streamlit application facilitates seamless interaction between complex ML models and end-users, including clinicians and patients, supporting its use in real-time clinical settings. While the study offers new insights into AI-driven CVD prediction, we acknowledge limitations such as the dataset size. In our research, we have successfully validated our predictive proposed methodology against an external clinical setting, demonstrating its robustness and accuracy in a real-world fixture. The validation process confirmed the model's efficacy in the early detection of CVDs, reinforcing its potential for integration into clinical workflows to aid in proactive patient care and management. Future research directions include expanding the dataset, exploring additional algorithms, and conducting clinical trials to validate our findings. This research provides a valuable foundation for future studies, aiming to make significant strides against CVDs.

An Electronically Reconfigurable Highly Selective Stop-Band Ultra-Wideband Antenna Applying Electromagnetic Bandgaps and Positive-Intrinsic-Negative Diodes.

In this article, an ultra-wideband (UWB) antenna featuring two reconfigurable quasi-perfect stop bands at WLAN (5.25-5.75 GHz) and lower 5G (3.4-3.8 GHz) utilizing electromagnetic bandgaps (EBGs) and positive-intrinsic-negative (P-I-N) diodes is proposed. A pair of EBG structures are applied to generate sharp notch bands in the targeted frequency spectrum. Each EBG creates a traditional notch, while two regular notches are combined to make a quasi-perfect, sharp, notch band. Four P-I-N diodes are engraved into the EBG structures to enable notch band reconfigurability. By switching the operational condition of the four diodes, the UWB antenna can dynamically adjust its notching characteristics to enhance its adaptability to various communication standards and applications. The antenna can be reconfigured as a UWB (3-11.6 GHz) without any notch band, a UWB with a single sharp notch (either at WLAN or 5G), or a UWB with two quasi-perfect notch bands. Moreover, the antenna's notch bands can also be switched from a traditional notch to a quasi-perfect notch and vice versa. To confirm the validity of the simulated outcomes, the proposed reconfigurable UWB antenna is fabricated and measured. The experimental findings are aligned closely with simulation results, and the antenna offers notch band reconfigurability. The antenna shows a consistently favorable radiation pattern and gain. The dimension of the presented antenna is 20 × 27 × 1.52 mm3 (0.45 λc × 0.33 λc × 0.025 λc, where λc is the wavelength in free space).

Animal, human, and environmental perspectives on anthrax in Bangladesh.

Background: In Bangladesh, the zoonotic transmission of anthrax from animals to humans poses substantial challenges for prevention and control programs, especially in resource-constrained settings. A comprehensive literature review was conducted focusing on anthrax infections in animals, humans, and the environment to enable better design of prevention and control strategies. Materials and methods: We followed PRISMA guidelines to collect data on anthrax infection in animals and humans from reports between 1980 and January 2023. We used a standardized data extraction template to collect data on study location, year, hosts, deaths and risk factors responsible for anthrax occurrences at the animal, human and environmental sectors. Subsequently, we conducted a thorough analysis of the data gathered to identify the factors responsible for anthrax occurrences and to propose updated strategies for anthrax prevention and control. Results: Of the 27 articles analyzed, 20 focused on animal or human anthrax, while seven addressed environmental contaminations. A total of 6354 cases of anthrax infection in animals were recorded, with 998 fatalities and an overall case fatality of 15.7 %. In humans, inadequate knowledge about anthrax and its transmission was a significant factor. Risk factors for human cutaneous anthrax included activities such as slaughtering diseased animals and contact with contaminated raw meat or blood. Risky practices such as disposal of animal carcasses in floodwaters or water bodies were observed in some areas, contributing to the persistence of the anthrax pathogen in the environment. Conclusions: Our study highlights the necessity of a multisectoral One Health approach to effectively control and prevent anthrax outbreaks in both animals and humans. This approach should include comprehensive vaccination programs, social and behavioral change activities, environmental management, and the establishment of surveillance systems. Implementing these recommendations will be crucial in addressing the complex challenges posed by anthrax in low-resource settings.

Innovative Machine Learning Strategies for Early Detection and Prevention of Pregnancy Loss: The Vitamin D Connection and Gestational Health.

Early pregnancy loss (EPL) is a prevalent health concern with significant implications globally for gestational health. This research leverages machine learning to enhance the prediction of EPL and to differentiate between typical pregnancies and those at elevated risk during the initial trimester. We employed different machine learning methodologies, from conventional models to more advanced ones such as deep learning and multilayer perceptron models. Results from both classical and advanced machine learning models were evaluated using confusion matrices, cross-validation techniques, and analysis of feature significance to obtain correct decisions among algorithmic strategies on early pregnancy loss and the vitamin D serum connection in gestational health. The results demonstrated that machine learning is a powerful tool for accurately predicting EPL, with advanced models such as deep learning and multilayer perceptron outperforming classical ones. Linear discriminant analysis and quadratic discriminant analysis algorithms were shown to have 98 % accuracy in predicting pregnancy loss outcomes. Key determinants of EPL were identified, including levels of maternal serum vitamin D. In addition, prior pregnancy outcomes and maternal age are crucial factors in gestational health. This study's findings highlight the potential of machine learning in enhancing predictions related to EPL that can contribute to improved gestational health outcomes for mothers and infants.

Lipid-based nucleic acid therapeutics with in vivo efficacy.

Synthetic vectors for therapeutic nucleic acid delivery are currently competing significantly with their viral counter parts due to their reduced immunogenicity, large payload capacity, and ease of manufacture under GMP-compliant norms. The approval of Onpattro, a lipid-based siRNA therapeutic, and the proven clinical success of two lipid-based COVID-19 vaccines from Pfizer-BioNTech, and Moderna heralded the specific advantages of lipid-based systems among all other synthetic nucleic acid carriers. Lipid-based systems with diverse payloads-plasmid DNA (pDNA), antisense oligonucleotide (ASO), small interfering RNA (siRNA), microRNA (miRNA), small activating RNA (saRNA), and messenger RNA (mRNA)-are now becoming a mature technology, with growing impact in the clinic. Research over four decades identified the key factors determining the therapeutic success of these multi-component systems. Here, we discuss the main nucleic acid-based technologies, presenting their mechanism of action, delivery barriers facing them, the structural properties of the payload as well as the component lipids that regulate physicochemical properties, pharmacokinetics and biodistribution, efficacy, and toxicity of the resultant nanoparticles. We further detail on the formulation parameters, evolution of the manufacturing techniques that generate reproducible and scalable outputs, and key manufacturing aspects that enable control over physicochemical properties of the resultant particles. Preclinical applications of some of these formulations that were successfully translated from in vitro studies to animal models are subsequently discussed. Finally, clinical success and failure of these systems starting from 1993 to present are highlighted, in a holistic literature review focused on lipid-based nucleic acid delivery systems. This article is categorized under: Therapeutic Approaches and Drug Discovery > Emerging Technologies Therapeutic Approaches and Drug Discovery > Nanomedicine for Oncologic Disease Toxicology and Regulatory Issues in Nanomedicine > Toxicology of Nanomaterials.

Intraspecific phenotypic differences in climbing perch Anabas testudineus (Bloch, 1792) populations may be linked to habitat adaptations.

The climbing perch, Anabas testudineus, is a nutritionally and economically significant food fish. The present study reveals the first comprehensive description of the life-history traits of A. testudineus scooped up through different traditional fishing gears from July 2020 to December 2020. Among the 120 collected specimens, the smallest and largest specimens were 8.5 cm-14.6 cm TL in Nilphamari and Patuakhali, respectively. The estimated b values for LLRs indicated positive allometric growth in all sampling points (b > 1.0). The LWRs of A. testudineus indicated positive allometric growth in the Gazipur and Nilphamari districts (b > 3.00) and negative allometric growth in the Patuakhali and Khulna districts (b < 3.00). A Wilcoxon sign-ranked test for WR showed no significant dissimilarity from 100, signifying the balanced habitat for A. testudineus. The estimated a3.0 was minimum in Khulna (0.0110) and maximum in Nilphamari (0.0825). "The Lm was estimated at 7.4032 (7.4) cm TL in Nilphamari and 8.86 (8.9) cm TL in Patuakhali". Nineteen of twenty morphometric measurements and ten of twelve meristic characters showed substantial variations (p < 0.0001). The principal component analysis indicated shape variation and explained 85.361% of the total variance and showed differences in TL, SL, HL, LBD, LE1, D1D2, A1A2, and VV2. The cluster heatmap demonstrates that the other stocks segregated Gazipur stock. Our findings reveal a significant dataset about intraspecific phenotypic differentiation, which will aid the long-term exploration and management of A. testudineus species in Bangladesh and its neighboring countries.

Risk mapping and risk factors analysis of rabies in livestock in Bangladesh using national-level passive surveillance data.

Rabies is a major zoonotic disease around the world, causing significant mortality to both humans and animals, especially in low- and middle-income countries. In Bangladesh, rabies is transmitted mostly by the bite of infected dogs and jackals to humans and domestic livestock, causing severe economic losses and public health hazards. Our study analyzed national passive surveillance data of veterinary hospital-reported rabies cases in cattle, buffalo, sheep, and goats from 2015 to 2017 in all 64 districts of Bangladesh. We used a zero-inflated negative binomial regression model to identify the main environmental and socio-economic risk factors associated with rabies occurrence in livestock, and we used model results to generate risk maps. Our study revealed that monsoon precipitation (RR=1.28, p-value=0.043) was positively associated with rabies cases in livestock, and the percentage of adults who have completed university education was also a significant predictor (RR=0.58, p-value<0.001) likely suggesting that districts with higher education levels tended to have a lower reporting of rabies cases in livestock. The standardized incidence ratio maps and predicted relative risk maps revealed a high risk of rabies cases in southeast areas in Bangladesh. We recommend implementing risk-based vaccination strategies in dogs and jackals in those high-risk areas before monsoon to reduce the burden of rabies cases in domestic ruminants and humans in Bangladesh.

Bandwidth and Gain Enhancement of a CPW Antenna Using Frequency Selective Surface for UWB Applications.

In this article, a single-layer frequency selective surface (FSS)-loaded compact coplanar waveguide (CPW)-fed antenna is proposed for very high-gain and ultra-wideband applications. At the initial stage, a geometrically simple ultra-wideband (UWB) antenna is designed which contains CPW feed lines and a multi-stub-loaded hexagonal patch. The various stubs are inserted to improve the bandwidth of the radiator. The antenna operates at 5-17 GHz and offers 6.5 dBi peak gain. Subsequently, the proposed FSS structure is designed and loaded beneath the proposed UWB antenna to improve bandwidth and enhance gain. The antenna loaded with FSS operates at an ultra-wideband of 3-18 GHz and offers a peak gain of 10.5 dBi. The FSS layer contains 5 × 5 unit cells with a total dimension of 50 mm × 50 mm. The gap between the FSS layer and UWB antenna is 9 mm, which is fixed to obtain maximum gain. The proposed UWB antenna and its results are compared with the fabricated prototype to verify the results. Moreover, the performance parameters such as bandwidth, gain, operational frequency, and the number of FSS layers used in the proposed antenna are compared with existing literature to show the significance of the proposed work. Overall, the proposed antenna is easy to fabricate and has a low profile and simple geometry with a compact size while offering a very wide bandwidth and high gain. Due to all of its performance properties, the proposed antenna system is a strong candidate for upcoming wideband and high-gain applications.

Exploring therapeutic potential of Woodfordia fruticosa (L.) Kurz leaf and bark focusing on antioxidant, antithrombotic, antimicrobial, anti-inflammatory, analgesic, and antidiarrheal properties.

Background and Aims: The study aimed to evaluate the pharmacological properties of methanolic extracts of leaves and barks of Woodfordia fruticosa (L.) Kurz (family: Lythraceae) focusing on antioxidant, thrombolytic, anti-inflammatory, antibacterial, analgesic, and antidiarrheal effects. Methods: 1,1-Diphenyl-2-picrylhydrazyl (DPPH) free radical scavenging assay, clot lysis, disc diffusion, and membrane stabilizing methods were employed to assess in vitro antioxidant, thrombolytic, antibacterial, and anti-inflammatory properties of the leaf and bark methanolic extracts (ME) of W. fruticosa and different organic solvents, that is, petroleum ether (PE), dichloromethane (DCM), chloroform (CL), and aqueous (AQ) fractions. In addition, in vivo central and peripheral analgesic and antidiarrheal activities of both crude extracts were evaluated at two doses (200 and 400 mg/kg of body weight [bw]). Results: All the extracts and fractions showed promising antioxidant properties by scavenging DDPH free radicals with IC50 of 6.11-20.79 µg/mL. AQ fraction (41.24%) of leaves and ME (44.90%) of bark exerted notable in vitro thrombolytic activity. The CL fraction of leaves and AQ fraction of the bark showed 43.16% and 45.37% inhibition of RBC hemolysis, respectively, compared to the inhibition of RBC hemolysis by aspirin in a hypotonic-induced membrane stabilizing assay. Besides, both extracts were observed to provide significant (p < 0.001) central and peripheral analgesic responses at both doses of 200 and 400 mg/kg bw. Furthermore, both doses of bark extract (p < 0.001) and the 400 mg/kg bw of leaf extract (p < 0.05) were observed to possess statistically significant antidiarrheal activity. Additionally, in an in vivo acute toxicity investigation, both extracts had a median lethal dose (LD50) greater than 5000 mg/kg bw, indicating their safety level. Conclusion: The current study proves the ethnomedicinal uses of W. fruticosa; however, further studies are required for phytochemical screening to isolate the responsible bioactive compounds and discover the lead molecules from the plant species.

A Compact MIMO Antenna with Improved Isolation for ISM, Sub-6 GHz, and WLAN Application.

This paper presents a compact two-element MIMO antenna with improved isolation for triple-band applications. The antenna consists of two radiating elements with the shared ground plane and a novel decoupling structure. Each antenna element has three stubs with different lengths, which work as quarter-wavelength monopoles to give a triple-band operation. The decoupling system is made by etching various slots in an inverted H-shape stub attached to two quarter-circles at its lower ends. The simulated and measured results show that the antenna operates (|S11| < −10 dB) at the key frequency bands of 2.4 GHz (2.29−2.47 GHz), 3.5 GHz (3.34−3.73 GHz), and 5.5 GHz (4.57−6.75 GHz) with a stable gain and radiation patterns. Moreover, the MIMO antenna shows good isolation characteristics. The isolation is more than 20 dB, the envelope correlation coefficient is <0.003, and diversity gain is 9.98 dB, within the frequency band of interest. Furthermore, the MIMO antenna has a compact size of 48 mm × 31 mm × 1.6 mm. These features of the proposed antenna make it a suitable candidate for I.S.M., 5G sub-6 GHz, and WLAN applications.

Computational screening of 645 antiviral peptides against the receptor-binding domain of the spike protein in SARS-CoV-2.

The receptor-binding domain (RBD) of SARS-CoV-2 spike (S) protein plays a vital role in binding and internalization through the alpha-helix (AH) of human angiotensin-converting enzyme 2 (hACE2). Thus, it is a potential target for designing and developing antiviral agents. Inhibition of RBD activity of the S protein may be achieved by blocking RBD interaction with hACE2. In this context, inhibitors with large contact surface area are preferable as they can form a potentially stable complex with RBD of S protein and would not allow RBD to come in contact with hACE2. Peptides represent excellent features as potential anti-RBD agents due to better efficacy, safety, and tolerability in humans compared to that of small molecules. The present study has selected 645 antiviral peptides known to inhibit various viruses and computationally screened them against the RBD of SARS-CoV-2 S protein. In primary screening, 27 out of 645 peptides exhibited higher affinity for the RBD of S protein compared to that of AH of the hACE2 receptor. Subsequently, AVP1795 appeared as the most promising candidate that could inhibit hACE2 recognition by SARS-CoV 2 as was predicted by the molecular dynamics simulation. The critical residues in RBD found for protein-peptide interactions are TYR 489, GLY 485, TYR 505, and GLU 484. Peptide-protein interactions were substantially influenced by hydrogen bonding and hydrophobic interactions. This comprehensive computational screening may provide a guideline to design the most effective peptides targeting the spike protein, which could be studied further in vitro and in vivo for assessing their anti-SARS CoV-2 activity.

Peste des Petits Ruminants Risk Factors and Space-Time Clusters in Bangladesh.

Peste des Petits Ruminants (PPR) is endemic in Bangladesh, but its spatial distribution and risk factors have not yet been reported. Using four years of national-level, passive surveillance data (2014 to 2017), in this study we aimed to identify risk factors, create PPR risk maps and describe PPR time-space clusters. We selected PPR case records-mainly based on presumptive diagnosis of small ruminants in subdistrict veterinary hospitals-and sheep and goat population data from all 64 districts of Bangladesh. Peste des Petits Ruminants cumulative incidence per 10,000 animals at risk per district was used to conduct cluster and hotspot analysis and create predictive maps for each year and all 4 years combined. The association between PPR cumulative incidence and hypothesized risk factors-including climatic variables, elevation, road length, river length, railroad length, land cover, and water bodies-was analyzed using a geographically weighted regression model. The total number of PPR cases reported during the study period was 5.2 million. We found that most PPR cases (27.6%) were reported in the monsoon season. The highest and lowest proportions of cases were reported from Rajshahi (36.1%) and Barisal divisions (2.1%), respectively. We identified five space-time clusters, 9 high-high clusters, and 9 hotspots. The predicted cumulative incidences of PPR were persistently higher in north-east, north-west, and south-east parts of Bangladesh. Road length (P = 0.03) was positively associated with PPR incidence in Bangladesh. Results suggest that movement of animals (road length) plays an important role in the epidemiology of PPR in Bangladesh. Along with restriction of animal movement, hotspots and high-high clusters should be targeted first for immunization coverage in Bangladesh and similar PPR endemic countries to achieve eradication.

A possible alternative therapy for type 2 diabetes using Myristica fragrans Houtt in combination with glimepiride: in vivo evaluation and in silico support.

The current study aimed to evaluate the in vivo hypoglycemic potential of Myristica fragrans seed extract co-administered with glimepiride in Swiss albino mice. Computational tools were used to further verify the in vivo findings and to help compare this combination to the glimepiride-pioglitazone combination in terms of the binding affinity of the ligands to their respective target protein receptors and the relative stability of the drug-protein complexes. The effect of the combined therapy was observed both in alloxan- and glucose-induced hyperglycemic Swiss albino mice. The mean fasting blood glucose level of the test groups was measured and statistically evaluated using Student's t test. The combined therapy significantly reduced the blood glucose level in a time-dependent manner compared to glimepiride alone. The binding affinity of glimepiride was found to be -7.6 kcal/mol with sulfonylurea receptor 1 in molecular docking. Conversely, macelignan-peroxisome proliferator-activated receptor (PPAR) α and macelignan-PPAR γ complexes were stabilized with -9.2 and -8.3 kcal/mol, respectively. Molecular dynamic simulation revealed that macelignan-PPAR α and γ complexes were more stable than pioglitazone complexes. The combination shows promise in animal and computer models and requires further trials to provide evidence of its activity in humans.