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Abstract Microorganisms are present in snow/ice of the Antarctic Plateau, but their biogeography and metabolic state under extreme local conditions are poorly understood. Here, we show the diversity and distribution of microorganisms in air (1.5 m height) and snow/ice down to 4 m depth at three distant latitudes along a 2578 km transect on the East Antarctic Plateau on board an environmentally friendly, mobile platform. Results demonstrate the widespread distribution of microorganisms in the ice down to at least 4 m depth. Data point to geochemical and bacterial geographic distribution that correlate with wind trajectory and speed, modulated by local gathering and recirculation of microorganisms through snow drifting. Reservoir effects and community selection appear to occur over time, favoring microorganisms best adapted to hypothermal and hyperarid conditions. A new cyanobacterial species (Gloeocapsopsis sp) was isolated from 3 to 4 m depth. Our findings suggest that some microorganisms could exhibit transient, basal metabolic activity when associated to high salt particles, contributing to set biodiversity patterns and biogeographic compartmentalization on Antarctic Plateau ice.

Introduction: The global rise in ageing populations poses challenges for healthcare systems. By 2030, Singapore anticipates a quarter of its population to be aged 65 or older. This study addresses the dearth of research on frailty’s impact on emergency laparotomy (EL) outcomes in this demographic, emphasising the growing significance of this surgical intervention. Method: Conducted at 2 tertiary centres in Singapore from January to December 2019, a retrospective cohort study examined EL outcomes in patients aged 65 or older. Frailty assessment, using the Clinical Frailty Scale (CFS), was integrated into demographic, diagnostic and procedural analyses. Patient data from Tan Tock Seng Hospital and Khoo Teck Puat Hospital provided a comprehensive view of frailty’s role in EL. Results: Among 233 participants, 26% were frail, revealing a higher vulnerability in the geriatric population. Frail individuals exhibited elevated preoperative risk, prolonged ICU stays, and significantly higher 90-day mortality (21.3% versus 6.4%). The study illuminated a nuanced connection between frailty and adverse outcomes, underlining the critical need for robust predictive tools in this context. Conclusion: Frailty emerged as a pivotal factor influencing the postoperative trajectory of older adults undergoing EL in Singapore. The integration of frailty assessment, particularly when combined with established metrics like P-POSSUM, showcased enhanced predictive accuracy. This finding offers valuable insights for shared decision-making and acute surgical unit practices, emphasising the imperative of considering frailty in the management of older patients undergoing emergency laparotomy.

The purposes of the present study were to examine the domain specificity of flow and its relationships with different motivation regulations in sport and academic. The sample consisted of 224 competitive student-athletes (126 males, 97 females, 1 missing) from Singapore universities with mean age of 22.73 years (SD = 2.76). Data was collected through a self-reported questionnaire to assess seven motivation types in Self-Determination Theory (SDT) and flow disposition in sport and academic domains. Findings revealed that there is a positive link between flow in sport and in academic at a cross-domain level. In addition, male student-athletes reported higher flow tendencies in both sports and academics. Male student-athletes in individual sport reported higher intrinsic motivation (IM) to accomplish and flow tendencies than male in team sport, but female student-athletes in team sport reported higher IM to accomplish and flow tendencies to accomplish than individual sport. IM to experience stimulation positively predicted flow in sport, while in academic domain, IM to know and identified regulation are positive predictors of flow. The findings of this study showed that IM to experience stimulation in sport and academic domains could positively predict flow.

Antimicrobial therapy is the most frequently used medical intervention for bovine mastitis in the dairy industry. This study aims to monitor the extent of the antimicrobial resistance (AMR) problem in Staphylococcus aureus in the dairy industry in Western Romania. Twenty farms were selected by random sampling in a transverse epidemiological study conducted across four counties in Western Romania and divided into livestock units. This study assessed the association between the resistance genes to phenotypic expression of resistance and susceptibility. Isolates of S. aureus were identified and q-PCR reactions were used to detect antibiotic resistance genes. One hundred and fifty bovine and 20 human samples were positive for S. aureus. Twenty five percent of bovine isolates (30/120) and none(0/30) of the human isolates were methicillin-resistant S. aureus (MRSA). All isolates were susceptible to fosfomycin, ciprofloxacin, netilmicin, and resistant to ampicillin and penicillin. S. aureus isolates regarded as phenotypically resistant (R) were influenced by the origin of the samples (human versus bovine, χ2 = 36.510, p = 0.013), whether they were methicillin-resistant S. aureus (χ2 = 108.891, p < 0.000), the county (χ2 = 103.282, p < 0.000) and farm of isolation (χ2 = 740.841, p < 0.000), but not by the size of the farm (χ2 = 65.036, p = 0.306). The multiple antibiotic resistance index was calculated for each sample as the number regarded as phenotypically resistant (R)/total antibiotics tested (MARI = 0.590 ± 0.023) was significantly higher (p < 0.000) inmethicillin-resistant S. aureus (0.898 ± 0.019) than non-methicillin-resistant S. aureus (0.524 ± 0.024) isolates. For the antibiotics tested, the total penetrance (P%) of the resistance genes was 59%, 83% for blaZ, 56% for cfr, 50% for erm(B), 53% for erm(C), 57% for mecA and 32% for tet(K). Penetrance can be used as a parameter for guidance towards a more accurate targeting of chemotherapy. P% in S. aureus was strongly positively correlated with the multiple antibiotic resistance index (r = +0.878, p < 0.000) with the potential to use the same limit value as an antibiotic management decision criterion. Considering cow mastitis, the penetrance value combined with the multiple antibiotic resistance index suggests that penetrance could serve as a useful parameter for more precise targeting of chemotherapy for S. aureus.

ABSTRACTBackground: To improve the quality of care for Indigenous patients, local Indigenous leaders in the Northwest Territories, Canada have called for more culturally responsive models for Indigenous and biomedical healthcare collaboration at Stanton Territorial Hospital.Objective: This study examined how Indigenous patients and biomedical healthcare providers envision Indigenous healing practices working successfully with biomedical hospital care at Stanton Territorial Hospital.Methods: We carried out a qualitative study from May 2018 – June 2022. The study was overseen by an Indigenous Community Advisory Committee and was made up of two methods: (1) interviews (n = 41) with Indigenous Elders, patient advocates, and healthcare providers, and (2) sharing circles with four Indigenous Elders.Results: Participants’ responses revealed three conceptual models for Indigenous and biomedical healthcare collaboration: the (1) integration; (2) independence; and (2) revisioning relationship models. In this article, we describe participants’ proposed models and examine the extent to which each model is likely to improve care for Indigenous patients at Stanton Territorial Hospital. By surfacing new models for Indigenous and biomedical healthcare collaboration, the study findings deepen and extend understandings of hospital-based Indigenous wellness services and illuminate directions for future research.

All over the world, there is a lot of patient health data in different locations such as hospitals, clinics, insurance companies, and other organizations. In this sense, global identification of the patient has emerged as an everyday healthcare challenge. Governments and institutions have to prioritize satisfactory, quick, and integrated decision-making in a wide, dispersed, and global environment because of unexpected challenges like pandemics or threats. In the current scientific literature, some of the existing challenges include support for a standard global unique identification that considers privacy issues, the combination of multiple technological biometry implementations, and personal documents. Thus, we propose a decentralized software model based on blockchain and smart contracts that includes privacy, global unique person identification supporting multiple combinations of documents, and biometric data using the Global Standards 1 - GS1 healthcare industry standard. Furthermore, we defined a methodology to evaluate a hypothetical use case of this model where an integrated and standard global health data sharing personal identification is crucial. For this, we implemented the proposed model in a global-wide continent location through cloud machines, fog computing, and blockchain considering the unique patient data identification and evaluate a use case scenario based on the top 5 most globally visited tourist destinations (France, Spain, the United States of America, China, and Italy), with an approach based on this model. The results show that using a model for a global id for healthcare can help reduce costs, time, and efforts, especially in the context of health threats, where agility and financial support must be prioritized.

The development of novel medicines to treat autoimmune diseases and SARS-CoV-2 main protease (Mpro), a virus that can cause both acute and chronic illnesses, is an ongoing necessity for the global community. The primary objective of this research is to use CoMFA methods to evaluate the quantitative structure-activity relationship (QSAR) of a select group of chemicals concerning autoimmune illnesses. By performing a molecular docking analysis, we may verify previously observed tendencies and gain insight into how receptors and ligands interact. The results of the 3D QSAR models are quite satisfactory and give significant statistical results: Q_loo∧2 = 0.5548, Q_lto∧2 = 0.5278, R∧2 = 0.9990, F-test = 3,101.141, SDEC = 0.017 for the CoMFA FFDSEL, and Q_loo∧2 = 0.7033, Q_lto∧2 = 0.6827, Q_lmo∧2 = 0.6305, R∧2 = 0.9984, F-test = 1994.0374, SDEC = 0.0216 for CoMFA UVEPLS. The success of these two models in exceeding the external validation criteria used and adhering to the Tropsha and Glorbaikh criteria’s upper and lower bounds can be noted. We report the docking simulation of the compounds as an inhibitor of the SARS-CoV-2 Mpro and an autoimmune disorder in this context. For a few chosen autoimmune disorder receptors (protein tyrosine phosphatase, nonreceptor type 22 (lymphoid) isoform 1 (PTPN22), type 1 diabetes, rheumatoid arthritis, and SARS-CoV-2 Mpro, the optimal binding characteristics of the compounds were described. According to their potential for effectiveness, the studied compounds were ranked, and those that demonstrated higher molecular docking scores than the reference drugs were suggested as potential new drug candidates for the treatment of autoimmune disease and SARS-CoV-2 Mpro. Additionally, the results of analyses of drug similarity, ADME (Absorption, Distribution, Metabolism, and Excretion), and toxicity were used to screen the best-docked compounds in which compound 4 scaled through. Finally, molecular dynamics (MD) simulation was used to verify compound 4’s stability in the complex with the chosen autoimmune diseases and SARS-CoV-2 Mpro protein. This compound showed a steady trajectory and molecular characteristics with a predictable pattern of interactions. These findings suggest that compound 4 may hold potential as a therapy for autoimmune diseases and SARS-CoV-2 Mpro.

The authors Bianca Cornelia Lungu and Ioan Hutu did not state contributed equally [...]

Graves' disease (GD) is an autoimmune condition that frequently causes hyperthyroidism and thyrotoxicosis. Protein tyrosine phosphatase, non-receptor type 22 (lymphoid) isoform 1 (PTPN22), is a promising therapeutic candidate for treating Graves' disease, rheumatoid arthritis, type 1 diabetes, and other autoimmune disorders. In this dataset, 31 molecular compounds and two standard drugs were optimized using the semi-empirical PM7 theory method via MOPAC v22.0.4 to reveal the key influencing factors contributing to their grave's disease inhibition activity and selectivity. Using QSARIN software, the acquired properties/descriptors were used to create a quantitative structural activities relationship (QSAR) model, and the similarities between the observed and predicted pIC50 values were examined. A molecular docking simulation study also uncovers non-covalent interactions between the investigated compounds and the receptors. The observed ligand-protein interactions with grave's disease proteins (PDB ID: 2XPG and 4QT5) and protein tyrosine phosphatase, non-receptor type 22 (lymphoid) isoform 1 (PTPN22) (PDB ID: 3BRH) were investigated. The pharmacokinetics (ADMET) properties were also investigated. Finally, molecular dynamics (MD) simulation and MMGBSA studies that demonstrated stable trajectory and molecular properties with a consistent interaction profile were used to validate the stability of the compounds in the complex with PTPN22.

Abstract Background Influenza virus disease remains one of the most contagious diseases that aided the deaths of many patients, especially in this COVID-19 pandemic era. Recent discoveries have shown that the high prevalence of influenza and SARS-CoV-2 coinfection can rapidly increase the death rate of patients. Hence, it became necessary to search for more potent inhibitors for influenza disease therapy. The present study utilized some computational modeling concepts such as 2D-QSAR, 3D-QSAR, molecular docking simulation, and ADMET predictions of some 1,3-thiazine derivatives as inhibitors of influenza neuraminidase (NA). Results The 2D-QSAR modeling results showed GFA-MLR ( $$R_{{\text{train }}}^{2}$$ R train 2 = 0.9192, Q 2 = 0.8767, R 2 adj = 0.8991, RMSE = 0.0959, $$R_{{{\text{test}}}}^{2}$$ R test 2 = 0.8943, $$R_{{{\text{pred}}}}^{2}$$ R pred 2 = 0.7745) and GFA-ANN ( $$R_{{\text{train }}}^{2}$$ R train 2 = 0.9227, Q 2 = 0.9212, RMSE = 0.0940, $$R_{{{\text{test}}}}^{2}$$ R test 2 = 0.8831, $$R_{{{\text{pred}}}}^{2}$$ R pred 2 = 0.7763) models with the computed descriptors as ATS7s, SpMax5_Bhv, nHBint6, and TDB9m for predicting the NA inhibitory activities of compounds which have passed the global criteria of accepting QSAR model. The 3D-QSAR modeling was carried out based on the comparative molecular field analysis (CoMFA) and comparative similarity indices analysis (CoMSIA). The CoMFA_ES ( $$R_{{\text{train }}}^{2}$$ R train 2 = 0.9620, Q 2 = 0.643) and CoMSIA_SED ( $$R_{{\text{train }}}^{2}$$ R train 2 = 0.8770, Q 2 = 0.702) models were found to also have good and reliable predicting ability. The compounds were also virtually screened based on their binding scores via molecular docking simulations with the active site of the NA (H1N1) target receptor which also confirms their resilient potency. Four potential lead compounds (4, 7, 14, and 15) with the relatively high inhibitory rate (> 50%) and docking (> − 6.3 kcal/mol) scores were identified as the possible lead candidates for in silico exploration of improved anti-influenza agents. Conclusion The drug-likeness and ADMET predictions of the lead compounds revealed non-violation of Lipinski’s rule and good pharmacokinetic profiles as important guidelines for rational drug design. Hence, the outcome of this research set a course for the in silico design and exploration of novel NA inhibitors with improved potency.

Abstract Background In seek of potent and non-toxic iminoguanidine derivatives formerly assessed as active Pseudomonas aeruginosa inhibitors, a combined mathematical approach of quantitative structure-activity relationship (QSAR), homology modeling, docking simulation, ADMET, and molecular dynamics simulations were executed on iminoguanidine derivatives. Results The QSAR method was employed to statistically analyze the structure-activity relationships (SAR) and had conceded good statistical significance for eminent predictive model; (GA-MLR: Q2 LOO = 0.8027; R 2 = 0.8735; R 2 ext = 0.7536). Thorough scrutiny of the predictive models disclosed that the Centered Broto-Moreau autocorrelation - lag 1/weighted by I-state and 3D topological distance-based autocorrelation—lag 9/weighted by I-state oversee the biological activity and rendered much useful information to realize the properties required to develop new potent Pseudomonas aeruginosa inhibitors. The next mathematical model work accomplished here emphasizes finding a potential drug that could aid in curing Pseudomonas aeruginosa and SARS-CoV-2 as the drug targets Pseudomonas aeruginosa. This involves homology modeling of RNA polymerase-binding transcription factor DksA and COVID-19 main protease receptors, docking simulations, and pharmacokinetic screening studies of hits compounds against the receptor to identify potential inhibitors that can serve to regulate the modeled enzymes. The modeled protein exhibits the most favorable regions more than 90% with a minimum disallowed region less than 5% and is simulated under a hydrophilic environment. The docking simulations of all the series to the binding pocket of the built protein model were done to demonstrate their binding style and to recognize critical interacting residues inside the binding site. Their binding constancy for the modeled receptors has been assessed through RMSD, RMSF, and SASA analysis from 1-ns molecular dynamics simulations (MDS) run. Conclusion Our acknowledged drugs could be a proficient cure for SARS-CoV-2 and Pseudomonas aeruginosa drug discovery, having said that extra testing (in vitro and in vivo) is essential to explain their latent as novel drugs and manner of action.

Abstract Background Dengue virus (DENV) infection is spreading rapidly, especially in the subtropical and tropical regions, placing a huge percentage of the global population at risk and causing repeated outbreaks. DENV protease inhibition has been suggested as a viable therapeutic strategy. Using a computer-aided design approach and the structure-based drug design approach, ten 1, 2, 4-oxadiazole derivatives were designed based on the lead template (34) from our prior study. The design involved the substitution at the phenyl pharmacophore of the lead with methylamine, hydroxyl, and methoxy groups. To compare the anti-DENV efficacy of the optimized designed compounds to the template and other DENV referenced inhibitors targeting the NS-5 protease (PDB ID: 5K5M), they were docked with the DENV NS-5 protease. In silico, ADME characteristics and drug-likeness were also assessed for the compounds. Results The molecular docking scores of the designed 1, 2, 4-oxadiazole derivatives varied from − 19.091 to − 29.61 kcal/mol, with excellent hydrogen bond energies in the range of − 3.402 to − 9.0128 kcal/mol, compared to the lead with a score of − 19.10 kcal/mol, and the hydrogen bond energy is − 3.10 kcal/mol, both of which are lower than those of the proposed compounds. Ferentinide, S-adenosyl-l-homocysteine, and Ribavirin were found to have lower binding scores of − 15.8137, − 16.5362, and − 12.446 kcal/mol, respectively, with hydrogen bond energies of − 4.2659, − 10.4372, and − 7.2995 kcal/mol. The developed compounds all followed Lipinski's criteria, meaning they were highly bioavailable, had no potential carcinogenic or mutagenic properties, and posed no concern of cardiovascular toxicity based on the ADMET profile. Conclusion The proposed oxadiazole derivative interacted better with DENV protease (NS-5) than the lead inhibitor as well as the conventional inhibitors. Compounds 34a and 34b had the best ligand-protease interaction and gave the lowest free energy at − 26.54 and − 29.612 kcal/mol, respectively. Hence, they could be suggested as potential therapeutic candidates to inhibit NS-5 RdRp protease. This study has revealed the anti-DENV action of the designed compounds, indicating that synthesis and in vivo studies into their efficacy and mechanism are warranted.

Influenza A virus (IAV) infection is a contagious respiratory disease that causes many deaths due to the advent of drug-resistant strains in recent times. This led to the quest for the in-silico identification of potential hit scaffolds as anti-influenza A agents. In this research, two quantitative structure-activity relationships (QSAR) modelling approaches were performed to relate the molecular descriptors of some phenolic diterpenoids based on their 2D and 3D structural representations with their anti-IAV activities. Subsequently, molecular docking simulation and ADMET evaluations of the compounds were performed to virtually screen and identify the best hits accordingly. The genetic function approximation (GFA) based linear and non-linear regression such as multiple linear regression (MLR) and artificial neural network (ANN) regression models were built in the 2D-QSAR modelling, and the results showed GFA-MLR (R2train = 0.9102, Q2 = 0.8701) and GFA-ANN (R2train=0.9215, Q2=0.9216) models for predicting the anti-IAV activities of the compounds which have passed the global criteria of accepting QSAR models. The 3D-QSAR modelling was carried out based on the comparative molecular field analysis (CoMFA) and comparative similarity indices analysis (CoMSIA), and the results revealed CoMFA_ES (R2train = 0.948, Q2 = 0.590) and CoMSIA_EDH (R2train = 0.980, Q2 = 0.754) models for reliable predictions of anti-IAV activities. The compounds were also virtually screened based on their binding scores through molecular docking with an active site of human hemagglutinin (HA) target which confirms their resilient potency. Furthermore, the drug-likeness and ADMET predictions of the compounds showed the non-violation of Lipinski's rule and good ADMET profiles as part of the rational strategy for future in-silico drug design and discovery. The outcome of this research provides theoretical support to affirm the relevance of totarol as a promising scaffold and set a route for the in-silico design of new diterpenoid inhibitors with improved potency.

Abstract Mountain glacier systems are decreasing in volume worldwide yet relatively little is known about their upper reaches (>5000 m). Here we show, based on the world’s highest ice core and highest automatic weather stations, the significant and increasing role that melting and sublimation have on the mass loss of even Mt. Everest’s highest glacier (South Col Glacier, 8020 m). Estimated contemporary thinning rates approaching ~2 m a−1 water equivalent (w.e.) indicate several decades of accumulation may be lost on an annual basis now that glacier ice has been exposed. These results identify extreme sensitivity to glacier surface type for high altitude Himalayan ice masses and forewarn of rapidly emerging impacts as Mt. Everest’s highest glacier appears destined for rapid retreat.

Antibiotics are widely used for prophylaxis and therapy, reducing morbidity and mortality produced by bacterial pathogensin pigs, including infections caused by Escherichia coli. The aim of this study was to characterise antibiotic resistance phenotypes and genotypes in E. coli isolates in pigs in West Romanian grower farms. Differential phenotypic susceptibility profiles and the contribution of resistance genes to phenotypic expression of susceptibility or resistance were evaluated. A total of 76 E. coli isolates were identified and confirmed by the MicroScan Walk Away System. The occurrence of four resistance genes, ampC, blaZ, blaTEM and tetK in strains resistant to 13 antibiotics was assessed. Of the E. coli isolates, 0% showed resistance to meropenem, 3.9% to tigecycline and 10.5% to piperacillin/tazobactam, whereas, in contrast, 100% were resistant to ampicillin and mezlocillin, 76.31% to piperacillin and 59.3% to tetracycline. The prevalence of resistance genes in resistant isolates detected by q-PCR analysis was 97.0% for ampC, 96% for blaZ, 32.9% for blaTEM and 58.8% for tetK. Penetrance (the proportion of individuals carrying a particular variant of a gene that also expresses an associated trait) was 50% for ampC (32% for amoxicillin/clavulanate, 62% for cefazolin, 32% for cefepime, 100% for cefotaxime, 56% for cefuroxime and 99% for ampicillin), 65% for blaZ (32% for amoxicillin/clavulanate and 99% for ampicillin), 51% for blaTEM (81% for piperacillin) and 44% for the tetK gene (83% for tetracycline). The result of phenotypic antibiotic resistance testing may indicate the presence of plasmid-borne resistance, with a diagnostic odds ratio of a positive phenotypic resistance for tetK being 4.52. As a management decision, the maximum penetrance admitted for using a specific antibiotic for E. coli infections in pigs is recommended to be less than 20%.

Abstract Background The most frequent bacterial sexually transmitted disease is Chlamydia trachomatis (STD). In 2010, the Centers for Disease Control and Prevention (CDC) received 1.3 million reports of cases (CDC). Human chlamydial infections are linked to a variety of clinical symptoms. Inclusion (IncA) membranes are a promising drug target for the treatment of Chlamydia trachomatis. In the present study, molecular docking, ADMET, golden triangle, and molecular dynamics (MD) simulation studies were performed on a series of salicylidene acylhydrazides derivatives against Chlamydia trachomatis. Three types of docking software with different algorithms were used to screen the potential candidate against Chlamydia trachomatis. Results The results obtained from the docking analysis succeeded in screening nine novel hit compounds with high affinity to IncA membranes. Then, pharmacokinetics properties were calculated to spot out the drug-likeness of the selected compounds. Also, golden triangles were performed on the selected compounds. Compounds outside the golden triangle indicate that they would have clearance problems. Out of the nine novel hits drugs, four compounds pass the golden triangle screening and virtually all the quality assurance tests proposed by the model and were used for further analysis. One-ns molecular dynamics simulations on the docked complex of compound 44 (one of the highly active selected compounds of the dataset) aided in the further exploration of the binding interactions. Some crucial residues such as Ser111, Gln114, Asn107, Leu142, Gly144, Gln143, Lys104, Tyr149, Phe108, Phe145, and Arg146 were identified. Conventional and carbon–hydrogen bond interactions with amino residues Arg146, Asn107, Phe145, and Ser111 were critical for the binding of inclusion (IncA) membranes inhibitors. Conclusion Outcomes of the study can further be exploited to develop potent inclusion (IncA) membranes inhibitors.

ABSTRACT The interaction between the HIV-1 capsid and human nucleoporin 153 (NUP153) is vital for delivering the HIV-1 preintegration complex into the nucleus via the nuclear pore complex. The interaction with the capsid requires a phenylalanine/glycine-containing motif in the C-terminus of NUP153 (NUP153C). This study used molecular modeling and biochemical assays to comprehensively determine the amino acids in NUP153 that are important for capsid interaction. Molecular dynamics, FoldX, and PyRosetta simulations delineated the minimal capsid binding motif of NUP153 based on the known structure of NUP153 bound to the HIV-1 capsid hexamer. Computational predictions were experimentally validated by testing the interaction of NUP153 with capsid using an in vitro binding assay and a cell-based TRIM-NUP153C restriction assay. This work identified eight amino acids from P1411 to G1418 that stably engage with capsid, with significant correlations between the interactions predicted by molecular models and empirical experiments. This validated the usefulness of this multidisciplinary approach to rapidly characterize the interaction between human proteins and the HIV-1 capsid. IMPORTANCE The human immunodeficiency virus (HIV) can infect nondividing cells by interacting with the host nuclear pore complex. The host nuclear pore protein NUP153 directly interacts with the HIV capsid to promote viral nuclear entry. This study used a multidisciplinary approach combining computational and experimental techniques to comprehensively map the effect of mutating the amino acids of NUP153 on HIV capsid interaction. This work showed a significant correlation between computational and empirical data sets, revealing that the HIV capsid interacted specifically with only six amino acids of NUP153. The simplicity of the interaction motif suggested other FG-containing motifs could also interact with the HIV-1 capsid. Furthermore, it was predicted that naturally occurring polymorphisms in human and nonhuman primates would disrupt NUP153 interaction with capsid, potentially protecting certain populations from HIV-1 infection.

Production of nitric oxide (NO) has been demonstrated in several malignancies, however its role remains not fully understood, specifically in relation to the metabolic and functional implications that it may have on immune cells participating in tumorigenesis. Here, we show that inducible NO synthase (iNOS) is expressed in cancers of the colon and the prostate, mainly by tumour cells, and NO generation is evidenced by widespread nitrotyrosine (NT) staining in tumour tissue. Furthermore, presence of NT is observed in the majority of tumour-associated macrophages (TAMs), despite low iNOS expression by these cells, suggesting that NO from the tumour microenvironment affects TAMs. Indeed, using a co-culture model, we demonstrate that NO produced by colon and prostate cancer cells is sufficient to induce NT formation in neighbouring macrophages. Moreover, exposure to exogenous NO promotes mitochondria-dependent and -independent changes in macrophages, which orientate their polarity towards an enhanced pro-inflammatory phenotype, whilst decreasing antigen-presenting function and wound healing capacity. Abrogating endogenous NO generation in murine macrophages, on the other hand, decreases their pro-inflammatory phenotype. These results suggest that the presence of NO in cancer may regulate TAM metabolism and function, favouring the persistence of inflammation, impairing healing and subverting adaptive immunity responses.

Influenza virus disease is one of the most infectious diseases responsible for many human deaths, and the high mutability of the virus causes drug resistance effects in recent times. As such, it became necessary to explore more inhibitors that could avert future influenza pandemics. The present research utilized some in-silico modelling concepts such as 2D-QSAR, 3D-QSAR, molecular docking simulation, and ADMET predictions on some 5-benzyl-4-thiazolinone derivatives as influenza neuraminidase (NA) inhibitors. The 2D-QSAR modelling results revealed GFA-MLR (Rtrain2 =0.8414, Q2 = 0.7680) and GFA-ANN (Rtrain ​2 =0.8754, Q2 = 0.8753) models with the most relevant descriptors (MATS3i, SpMax5_Bhe, minsOH and VE3_D) for predicting the inhibitory activities of the molecules which has passed the global criteria of accepting QSAR models. The results of the 3D-QSAR modelling results showed that CoMFA_ES (Rtrain ​2 =0.9030, Q2 = 0.5390) and CoMSIA_EA (Rtrain2 =0.880, Q2 = 0.547) models are having good predicting ability among other developed models. The molecules were virtually screened via molecular docking simulation with the active site of NA protein receptor (pH1N1) which confirms their resilient potency when compared with zanamivir standard drug. Molecule 11 as the most potent molecule formed more H-bond interactions with the key residues such as TRP178, ARG152, ARG292, ARG371, and TYR406 that triggered the catalytic reactions for NA inhibition. Furthermore, six (6) molecules (9, 10, 11, 17, 22, and 31) with relatively high inhibitory activities and docking scores were identified as the possible leads for in-silico exploration of novel NA inhibitors. The drug-likeness and ADMET predictions of the lead molecules revealed non-violation of Lipinski’s rule and good pharmacokinetic profiles respectively, which are important guidelines for rational drug design. Hence, the outcome of this study overlaid a solid foundation for the in-silico design and exploration of novel NA inhibitors with improved potency.

Abstract Background Dengue fever is a key public health unease in various tropical and sub-tropical regions. The improvement of existing agents that can inhibit the dengue virus is therefore of utmost importance. In this work, the QSAR study was carried out on 25 molecules of phthalazinone derivatives which have been reported to possess excellent dengue virus inhibitory activity. Density functional computational technique was used in the optimisation of the molecules with the basis set at theory level (B3LYP, 6-31G*) respectively. The multiple linear regression (MLR) model was built using genetic function approximation (GFA) in the material studio software package. Also, in this study, molecular docking simulation was carried between dengue virus serotype 2 protease (PDB CODE: 6mol) and some selected phthalazinone derivatives (compounds 1, 2, 7, 11, and 21). Results The model was robust as evidenced by validation and robustness statistical parameter which include predicted R 2 pred., adjusted R 2 adj., cross-validated Q 2 and R 2 regression coefficient, etc (R 2 pred. = 0.71922, R 2 adj. = 0.939699, Q 2 CV = 0.905909, R 2 = 0.955567) respectively. The molecular docking studies conducted in this study have outlined the binding affinities of the selected compounds (1, 2, 7 11, and 21) which are all in good correlation with their respective pIC50 values. The free binding affinities of the selected compounds were found to be (− 8.7, − 8.8, − 8.7, − 8.3, and − 8.9 kcal/mol) respectively, compound 21 with the binding affinity of − 8.9 kcal/mol had the best binding free energy with the protease relative to other compounds under consideration. Conclusion The MLR-GFA model study alongside with the molecular docking analysis has essentially provided a valuable and in-depth understanding as well as knowledge for the development of novel chemical compounds with enhanced inhibitory potential against the dengue virus serotype 2 (DNV-2). Hence, the developed model can be applicable in predicting the anti-dengue activity of a new set of chemical compounds that fall within its applicability domain.

Abstract Background Enteric infections caused by Salmonella spp. remain a major public health burden worldwide. Chickens are known to be a major reservoir for this zoonotic pathogen. The presence of Salmonella in poultry farms and abattoirs is associated with financial costs of treatment and a serious risk to human health. The use of bacteriophages as a biocontrol is one possible intervention by which Salmonella colonization of chickens could be reduced. In a prior study, phages Eϕ151 and Tϕ7 significantly reduced broiler chicken caecal colonization by S. Enteritidis and S. Typhimurium respectively. Methods Salmonella-free Ross broiler chickens were orally infected with S. Enteritidis P125109 or S. Typhimurium 4/74. After 7 days of infection, the animals were euthanased, and 25cm2 sections of skin were collected. The skin samples were sprayed with a phage suspension of either Eϕ151 (S. Enteritidis), Tϕ7 phage suspension (S. Typhimurium) or SM buffer (Control). After incubation, the number of surviving Salmonellas was determined by direct plating and Most Probable Number (MPN). To determine the rate of reduction of Salmonella numbers on the skin surface, a bioluminescent S. Typhimurium DT104 strain was cultured, spread on sections of chicken breast skin, and after spraying with a Tϕ11 phage suspension, skin samples were monitored using photon counting for up to 24 h. Results The median levels of Salmonella reduction following phage treatment were 1.38 log10 MPN (Enteritidis) and 1.83 log10 MPN (Typhimurium) per skin section. Treatment reductions were significant when compared with Salmonella recovery from control skin sections treated with buffer (p

Abstract Understanding and quantifying populations' adaptive genetic variation and their response to climate change are critical to reforestation's seed source selection, forest management decisions, and gene conservation. Landscape genomics combined with geographic and environmental information provide an opportunity to interrogate forest populations' genome‐wide variation for understanding the extent to which evolutionary forces shape past and contemporary populations' genetic structure, and identify those populations that may be most at risk under future climate change. Here, we used genotyping by sequencing to generate over 11,000 high‐quality variants from Platycladus orientalis range‐wide collection to evaluate its diversity and to predict genetic offset under future climate scenarios. Platycladus orientalis is a widespread conifer in China with significant ecological, timber, and medicinal values. We found population structure and evidences of isolation by environment, indicative of adaptation to local conditions. Gradient forest modeling identified temperature‐related variables as the most important environmental factors influencing genetic variation and predicted areas with higher risk under future climate change. This study provides an important reference for forest resource management and conservation for P. orientalis.

Rheumatoid arthritis (RA) is an autoimmune condition in the world, affecting about 1% of the population. It is characterized by a cartilage attack unique to the tissue in the peripheral joints. Different types of drugs are used to treat the disease are aspirin, ibuprofen, naproxen and celecoxib, but their use is less effective due to increased drug resistance and the side effects of these drugs. The need for new anti-rheumatoid arthritis drugs with improved activities and a new mechanism of action along diverse mechanisms is therefore necessary. In this study, 30 compounds from PubChem database with accession number 435,024 were carefully chosen as innovative RA inhibitors to battle autoimmune deficiency and were laid open to quantitative structure-activity relationship (QSAR), comparative molecular field analysis (CoMFA), molecular docking, molecular dynamic simulations, ADMET, Frontier Molecular Orbitals, and golden triangle studies. In the QSAR study, multiple linear regression was used for descriptor selection. The results showed that QSAR (Ntraining = 18; R2 = 0.796; R2adjusted = 0.7523; F = 18.209; Q2 = 0.67999) take good stability and predictability. In the CoMFA fractional factorial design (FFD) and CoMFA smart region definition (SRD) study, based on the Open3DALIGN-based arrangement used, results have shown that the CoMFA (FFD) model with reliable predictive ability was chosen as the final model to perform modeling study. The CoMFA (FFD) model, which shows comparatively good execution, was applied to search the necessary structural areas where the adjustment was essential to design a novel compound with bettered bioactivity. The molecular docking study showed that compound 4 has the least binding energy of -8.0 kcal/mol using PyRx software and compound 17 has the least binding energy of -8.55 kcal/mol using AutoDock4.2 software, respectively. Based on docking results on compounds 4 and 17 have good predictive behavior and relatively good ADMET exposure. MD simulation analysis of 1000 ps shows that one hydrogen bond with the residues was formed by compound 17 (Lys11) and binds distantly to the protein. Compound 4 has a smaller energy gap (-6.5 eV) have great penetrating power, optimized clearance and oral absorption compared to compound 17. These studies offer valuable insights into the discovery and design of RA inhibitors in a modern age.

Microoxygenation (MOX) is used to improve wine colour and sensory quality; however, limited information is available for Pinot noir wines and wines with different initial phenolic content. In this study, MOX was applied to two Pinot noir wines, with either a low or a high phenolic content, at two doses (0.50 and 2.11 mg/L/day) for 14 days. With the sterile filtration applied, acetaldehyde formation during MOX was very low, supporting the influence of yeast on acetaldehyde production during MOX. The MOX dosage rate did not significantly affect colour development, while the Pinot noir wine with higher phenolics benefited more from MOX, significantly increasing colour intensity and SO2 resistant (polymeric) pigments. However, these changes did not guarantee colour stability, as a final SO2 addition (100 mg/L) largely erased the improvement to colour in all wines. This could be due to the lower acetaldehyde formation, thus less ethyl-bridged stable pigments resistant to SO2 bleaching. MOX also decreased the flavan-3-ols and anthocyanin monomers, which differed between the two Pinot noir wines, reflecting the initial phenolic content. Lastly, MOX generally increased the measured tannin concentration and affected the proportion of tannin subunits, with a decrease in tannin mass conversion and proportion of (-)-epigallocatechin extension units. Some of these changes in phenolic compounds could potentially increase astringency, suggesting that MOX should be applied to Pinot noir and other low phenolic wines with caution.

Flaviviruses are essential human pathogens including the dengue virus, though there is no effective antiviral drug for the treatment of the viral infections caused by these organisms currently. The NS-5 protease which is much conserved across the family of the flavivirus and necessary for viral replication represents a druggable target. The inhibition of dengue protease has been considered to be a druggable target for the treatment of dengue virus infection. The present study employed cheminformatics modeling, molecular docking analysis, and ADME (absorption, distribution, metabolism, excretion) prediction approach on some potent inhibitors of dengue viral RNA-Dependent RNA polymerase. A statistically valid quantitative structure-activity relationship (Q-SAR) model was developed. The model was validated on an independent external test set and has demonstrated statistically satisfactory performance (R2train= 0.9717, R2test = 0.80235, and cR2p= 0.8824) which proved that the built model was good, robust, reliable, and stable. Also, five molecular descriptors (MATS5c, MATS2m, GATS4c, VE1_Dzv, and ZMIC1) were shown to be meaningfully correlated with the dengue virus inhibitory activity of these compounds. The molecular docking calculations revealed the significant interactions between the amino acid residues of the active sites of the receptor with the ligands. The selected compounds (15, 28, and 29) were found to possess significant binding affinity values of -7.3, -7.7, and -8.3 kcal/mol respectively, with compound 29 having a better binding affinity better than fenretinide and S-adenosyl-L-homocysteine (SAH) with a binding affinity of -8.0 and -6.9 kcal/mol as well as low RMSD value. All the selected compounds obeyed Lipinski's rule of 5, thus entailing oral bioavailability and pharmacological activeness. The ADME predictions showed that all the selected compounds including fenretinide were inhibitors of CYP2D6 except compound 15, and non-inhibitors of CYP3A4 and cytochrome P450 enzymes with attributes of good gastrointestinal (GI) absorption, with compound 15 having excellent gastrointestinal (GI) absorption. As a consequence, the approach employed in this study and the obtained information could serve as a reliable framework to further design novel anti-dengue inhibitors.

Abstract Background Rapid sequence intubation (RSI) is used to secure the airway of stroke patients. Randomized controlled trial evidence exists to support the use of paramedic RSI for traumatic brain injury (TBI), but cannot necessarily be applied to stroke RSI because of differences between the stroke and TBI patient. To understand if the TBI evidence can be used for stroke RSI, we analysed a retrospective cohort of TBI and strokes to compare how survival is impacted differently by RSI when comparing strokes and TBI. Methods This study was a retrospective analysis of 10 years of in-hospital and out-of-hospital data for all stroke and TBI patients attended by Ambulance Victoria, Australia. Logistic regression predicted the survival for ischemic and haemorrhagic strokes as well as TBI. The constituents of RSI, such a medications, intubation success and time intervals were analysed against survival using interactions to asses if RSI impacts survival differently for strokes compared to TBI. Results This analysis found significant interactions in the RSI-only group for age, number of intubation attempts, atropine, fentanyl, pulse rate and perhaps scene time and time- to-RSI. Such interactions imply that RSI impact survival differently for TBI versus strokes. Additionally, no significant difference in survival for TBI was found, with a − 0.7% lesser survival for RSI compared to no-RSI; OR 0.86 (95% CI 0.67 to 1.11; p = 0.25). Survival for haemorrhagic stroke was − 14.1% less for RSI versus no-RSI; OR 0.44 (95% CI 0.33 to 0.58; p = 0.01) and was − 4.3%; OR 0.67 (95% CI 0.49 to 0.91; p = 0.01) lesser for ischemic strokes. Conclusions Rapid sequence intubation and related factors interact with stroke and TBI, which suggests that RSI effects stroke survival in a different way from TBI. If RSI impact survival differently for strokes compared to TBI, then perhaps the TBI evidence cannot be used for stroke RSI.

Abstract A methanotrophic community was enriched in a semi-continuous reactor under non-aseptic conditions with methane and ammonia as carbon and nitrogen source. After a year of operation, Methylosinus sp., accounted for 80% relative abundance of the total sequences identified from potential polyhydroxyalkanoates (PHAs) producers, dominated the methane-fed enrichment. Prior to induction of PHA accumulation, cells harvested from the parent reactor contained low level of PHA at 4.0 ± 0.3 wt%. The cells were later incubated in the absence of ammonia with various combinations of methane, propionic acid, and valeric acid to induce biosynthesis of poly(3-hydroxybutyrate) (PHB) and poly(3-hydroxybutyrate-co-3-hydroxyvalerate) (PHBV). Previous studies reported that methanotrophic utilization of odd-chain fatty acids for the production of PHAs requires reducing power from methane oxidation. However, our findings demonstrated that the PHB-containing methanotrophic enrichment does not require methane availability to generate 3-hydroxybutyrate (3HB) and 3-hydroxyvalerate (3HV)—when odd-chain fatty acids are presented. The enrichment yielded up to 14 wt% PHA with various mole fractions of 3HV monomer depending on the availability of methane and odd-fatty acids. Overall, the addition of valeric acid resulted in a higher PHA content and a higher 3HV fraction. The highest 3HV fraction (up to 65 mol%) was obtained from the methane–valeric acid experiment, which is higher than those previously reported for PHA-producing methanotrophic mixed microbial cultures.

Abstract Until recently, the reptile fauna of Christmas Island in the Indian Ocean comprised five endemic species (two skinks, two geckos, and one snake) and one native, non‐endemic skink. Four of these species were common and widespread until at least 1979, but by 2012 had disappeared from the wild. During the years of decline, little research was undertaken to examine why the species were disappearing. Here, we use a retrospective expert elicitation to rank potential factors that contributed to the loss of Christmas Island's reptiles and to assess the likelihood of re‐establishing populations of two species now listed as Extinct in the Wild. We additionally considered why one endemic lizard, the Christmas Island giant gecko (Cyrtodactylus sadleiri), and three introduced lizards remain common. Experts considered that the introduced common wolf snake (Lycodon capucinus) was the most likely cause of decline, as its temporal and spatial spread across the island closely matched patterns of lizard disappearances. An Asian co‐occurrence in recent evolutionary timeframes of the common wolf snake with the Christmas Island giant gecko and three introduced reptiles was the most marked point of difference between the extant and lost lizard species. The demise in less than 20 years of 80% of Christmas Island's native lizard assemblage highlights the vulnerability of island fauna to invading species.

The leishmaniases are multifactorial zoonotic diseases requiring a multidisciplinary One Health approach for diagnosis and control. For leishmaniasis diagnosis, here we describe production of a new recombinant protein based on a kinesin-related gene of Leishmania braziliensis (Lbk39), which shows 59% amino acid identity to the L. infantum homologue. The Lbk39 gene was synthesized, inserted into the pLEXSY-sat2 vector and transfected into L. tarentolae cells by electroporation. Culturing was carried out, and the secreted recombinant protein with a C-terminal histidine tag purified using nickel affinity chromatography on the culture supernatant, yielding a final product at 0.4 mg/mL. An indirect enzyme linked immunosorbent assay (ELISA) was standardised using sera from 74 Brazilian patients with cutaneous leishmaniasis and 11 with visceral leishmaniasis. Optimal ELISA conditions were established for the Lbk39 antigen in comparison with a crude extract from L. braziliensis. The sensitivity, specificity analysis and receiver operating characteristic (ROC) curve were determined with a significance level of 5%. The ROC curve showed a good accuracy with an area under curve (AUC) = 0.967, p

Abstract Adoptive cellular therapy involving genetic modification of T cells with chimeric antigen receptor (CAR) transgene offers a promising strategy to broaden the efficacy of this approach for the effective treatment of cancer. Although remarkable antitumor responses have been observed following CAR T‐cell therapy in a subset of B‐cell malignancies, this has yet to be extended in the context of solid cancers. A number of promising strategies involving reprogramming the tumor microenvironment, increasing the specificity and safety of gene‐modified T cells and harnessing the endogenous immune response have been tested in preclinical models that may have a significant impact in patients with solid cancers. This review will discuss these exciting new developments and the challenges that must be overcome to deliver a more sustained and potent therapeutic response.

Protein phase separation is implicated in formation of membraneless organelles, signaling puncta and the nuclear pore. Multivalent interactions of modular binding domains and their target motifs can drive phase separation. However, forces promoting the more common phase separation of intrinsically disordered regions are less understood, with suggested roles for multivalent cation-pi, pi-pi, and charge interactions and the hydrophobic effect. Known phase-separating proteins are enriched in pi-orbital containing residues and thus we analyzed pi-interactions in folded proteins. We found that pi-pi interactions involving non-aromatic groups are widespread, underestimated by force-fields used in structure calculations and correlated with solvation and lack of regular secondary structure, properties associated with disordered regions. We present a phase separation predictive algorithm based on pi interaction frequency, highlighting proteins involved in biomaterials and RNA processing.