Data Bank Research Articles

Beneficial Effect of Pretreatment Hyperosmolality on Outcome in Severe Traumatic Brain Injury: Evidence from a South Korean Multicenter Registry and Propensity Score Matching Analysis.

Hyperosmolar therapy has long been a cornerstone in managing increased intracranial pressure and improving outcomes in severe traumatic brain injury (TBI). This therapy hinges on elevating serum osmolality, creating an osmotic gradient that draws excess water from the brain's cellular and interstitial compartments and effectively reducing cerebral edema. Given this information, we hypothesized that the serum hyperosmolality prior to any treatment could significantly impact the clinical outcomes of patients with severe TBI, potentially mitigating secondary cerebral edema after trauma. Data were extracted from the Korean Multi-center Traumatic Brain Injury data bank, encompassing 4628 patients with TBI admitted between January 2016 and December 2018. Of these, 507 patients diagnosed with severe TBI (Glasgow Coma Scale score < 9) were selected for comprehensive analysis across four data domains: clinical, laboratory, initial computed tomography scan, and treatment. Serum osmolality was assessed prior to treatment, and the hyperosmolar group was defined by a pretreatment serum osmolality exceeding 320mOsm/L, whereas favorable outcomes were characterized by a modified Rankin Scale score of ≤ 3 at 6months after trauma. Multivariate regression with receiver operating characteristic curve analysis and propensity score matching were used to dissect the data set. Multivariate analysis showed serum osmolality is significantly associated with clinical outcome in patients with severe TBI (p < 0.001). The optimal cutoff value for predicting favorable outcome was 331mOsm/L, with a sensitivity of 38.9% and a specificity of 87.7%. Notably, the propensity score matching analysis comparing patients with pretreatment serum hyperosmolality with those without indicated a markedly improved functional outcome in the former group (32.5% vs 18.8%, p = 0.025). The present study has uncovered a significant correlation between the pretreatment serum osmolality and the clinical outcomes of patients with severe TBI. These findings offer a novel perspective, indicating that a serum hyperosmolality prior to any treatment might potentially have a neuroprotective effect in patients with severe TBI.

In-hospital outcomes following surgery versus conservative therapy in elderly patients with C2 fractures: a propensity score-matched analysis.

Second cervical vertebrae (C2) fractures are a common traumatic spinal injury in the elderly population. Surgical fusion and nonoperative bracing are two primary treatments for cervical instability, but the former is often withheld in the elderly due to concerns for poor postoperative outcomes arising from patient frailty. This study sought to evaluate the in-hospital differences in mortality, outcomes, and discharge disposition in elderly patients with C2 fractures undergoing surgical intervention compared with conservative therapy. The National Trauma Data Bank was queried from 2017 to 2019 for all patients aged ≥ 65 years with C2 fractures undergoing either surgical stabilization or conservative therapy. Propensity score matching was performed using k-nearest neighbors with replacement based on patient demographics, comorbidities, insurance type, injury severity, and fracture type. Group differences were compared using Student t-tests and Pearson's chi-square tests with Benjamini-Hochberg multiple comparisons correction. Subgroup analyses were performed in the 65-74, 75-79, and 80+ year age subgroups. Six thousand forty-nine patients were identified, of whom 2156 underwent surgery and 3893 received conservative treatment. Following matching, the surgery group had significantly lower mortality rates (5.52% vs 9.6%, p < 0.001), a longer mean hospital length of stay (LOS; 12.64 vs 7.49 days p < 0.001), and slightly higher rates of several complications (< 3% difference), as well as lower rates of discharge home (14.56% vs 23.52%, p < 0.001) and to hospice (1.07% vs 2.09%, p = 0.02) and a higher rate of discharge to intermediate care (68.83% vs 48.28%, p < 0.001). Similar trends in mortality and LOS were noted in all 3 subgroups. In elderly patients with C2 fractures, surgical stabilization confers a small survival advantage with a slightly higher in-hospital complication rate compared to conservative therapy. The increased rate of discharge to rehabilitation may represent better long-term prognosis following surgery. The increased risk of short-term complications is present but relatively small, thus surgery should not be withheld in patients with good long-term prognosis.

Explaining Conformational Diversity in Protein Families through Molecular Motions

Proteins play a central role in biological processes, and understanding their conformational variability is crucial for unraveling their functional mechanisms. Recent advancements in high-throughput technologies have enhanced our knowledge of protein structures, yet predicting their multiple conformational states and motions remains challenging. This study introduces Dimensionality Analysis for protein Conformational Exploration (DANCE) for a systematic and comprehensive description of protein families conformational variability. DANCE accommodates both experimental and predicted structures. It is suitable for analysing anything from single proteins to superfamilies. Employing it, we clustered all experimentally resolved protein structures available in the Protein Data Bank into conformational collections and characterized them as sets of linear motions. The resource facilitates access and exploitation of the multiple states adopted by a protein and its homologs. Beyond descriptive analysis, we assessed classical dimensionality reduction techniques for sampling unseen states on a representative benchmark. This work improves our understanding of how proteins deform to perform their functions and opens ways to a standardised evaluation of methods designed to sample and generate protein conformations.

Managing macromolecular crystallographic data with a laboratory information management system.

Protein crystallography is an established method to study the atomic structures of macromolecules and their complexes. A prerequisite for successful structure determination is diffraction-quality crystals, which may require extensive optimization of both the protein and the conditions, and hence projects can stretch over an extended period, with multiple users being involved. The workflow from crystallization and crystal treatment to deposition and publication is well defined, and therefore an electronic laboratory information management system (LIMS) is well suited to management of the data. Completion of the project requires key information on all the steps being available and this information should also be made available according to the FAIR principles. As crystallized samples are typically shipped between facilities, a key feature to be captured in the LIMS is the exchange of metadata between the crystallization facility of thehome laboratory and, for example, synchrotron facilities. On completion, structures are deposited in the Protein Data Bank (PDB) and the LIMS can include the PDB code in its database, completing the chain of custody from crystallization to structure deposition and publication. A LIMS designed for macromolecular crystallography, IceBear, is available as a standalone installation and as a hosted service, and the implementation of key features for the capture of metadata in IceBear is discussed as an example.

Virtual Screening of Indonesian Herbal Compounds with Neuraminidase Inhibitor Activity against N2 Influenza Virus Protein: An in silico Study

Background: Neuraminidase inhibitor (NAI) is one of anti-influenza drugs recommended for use by the World Health Organization (WHO). However, after treatment with NAI drugs in human, resistance to influenza antiviral drugs is begun to rise. Therefore, identification of compounds from Indonesian herbal plants as natural inhibitors of the influenza virus neuraminidase protein needs to be conducted for the development of new anti-influenza drugs.Materials and methods: The crystal structure of the neuraminidase protein complex used in this study was obtained from the Protein Data Bank (PDB). Structure-based pharmacophore modeling was performed using LigandScout version 4.4.5 software. Indonesian herbal plant compounds were collected from the HerbalDB database. Protein and ligand processing was carried out using Autodock 4.2 software. The 3D interaction visualization was carried out with Autodock software, while 2D interaction visualization was carried out with LigPlot software. To determine the toxicity and drug-likeliness of the ligand, the test ligands that had the best docking results were predicted using SwissADME and AdmetSAR.Results: From the virtual screening results, 24 hits were found, and five compounds had the best binding energy among the 24 tested compounds, these were pollenitin (ΔG=-7.22 kcal/mol), OPC-4:0 (ΔG=-7.11 kcal/mol), 6-hydroxykaempferol (ΔG=-7.08 kcal/mol), 5,8-dihydroxy-7,4'-dimethoxyflavone (ΔG=-7.07 kcal/mol), and 3,5,6,7-tetrahydroxy-4'-methoxyflavone (ΔG=-6.95 kcal/mol). The best five compounds were then chosen for further analysis.Conclusion: OPC-4:0 is found to be the best compound for the NAI based on its binding energy, pharmacokinetics, toxicity, and drug-likeliness. Thus, OPC-4:0 might be a potential candidate as a NAI of HxN2 virus. Keywords: influenza, molecular docking, neuraminidase, resistance, virtual screening

A Closer Look at Type I Left-Handed β-Helices Provides a Better Understanding in Their Sequence-Structure Relationship: Toward Their Rational Design.

Understanding the sequence-structure relationship in protein is of fundamental interest, but has practical applications such as the rational design of peptides and proteins. This relationship in the Type I left-handed β-helix containing proteins is updated and revisited in this study. Analyzing the available experimental structures in the Protein Data Bank, we could describe, further in detail, the structural features that are important for the stability of this fold, as well as its nucleation and termination. This study is meant to complete previous work, as it provides a separate analysis of the N-terminal and C-terminal rungs of the helix. Particular sequence motifs of these rungs are described along with the structural element they form.

Ultra-high-performance liquid chromatography-quadrupole time-of-flight-mass spectrometry-characterized extract of Aerides odorata Lour alleviates paracetamol-induced hepatotoxicity in animal model evidenced by biochemical, molecular, and computational studies.

Many kinds of orchids have significant health benefits although adequate research on their biological functions is yet to be carried out. This study investigated the paracetamol-induced liver damage-protecting effect of epiphytic Aerides odorata methanol extract (AODE). The protective effects of AODE were studied by analyzing its effect on liver function parameters, messenger RNA (mRNA) expression, and tissue histopathological architecture. The results were confirmed by ligand-receptor interaction of molecular docking and multitarget interaction of network pharmacological analyses. AODE significantly (p < 0.05) minimized the dose-dependent increase in acid phosphatase, aspartate aminotransferase, alanine aminotransferase, alkaline phosphatase, γ-glutamyl transferase, lactate dehydrogenase, and total bilirubin compared to the reference drug silymarin. Malondialdehyde level decreased, and the antioxidant genes catalase (CAT), superoxide dismutase (SOD), β-actin, paraoxonase-1 (PON1), and phosphofructokinase-1 (PFK-1) were upregulated in AODE-treated paracetamol-intoxicated rats. A total of 376 compounds comprising phenols and flavonoids were identified using ultra-high-performance liquid chromatography-quadrupole time-of-flight-mass spectrometry (UPLC-qTOF-MS). The online toxicity assessment using SwissADME and admetSAR exhibited drug-like, nontoxic, and potential pharmacological properties. Additionally, in silico analysis showed that isoacteoside, one of the identified compounds, exhibited the best docking score (-11.42) with the liver protein human pituitary adenylate cyclase-1 (Protein Data Bank ID: 3N94). Furthermore, network pharmacology analysis identified the top 10 hub genes, namely AKT1 (protein kinase B), CTNNB1 (catenin beta-1), SRC (proto-oncogene c-Src), TNF (tumor necrosis factor), EGFR (epidermal growth factor receptor), HSP90AA1 (heat shock protein 90α), MAPK3 (mitogen-activated protein kinase 3), STAT3 (signal transducer and activator of transcription 3), CASP3 (caspase protein), and ESR1 (estrogen receptor 1), which are responsible for hepatoprotective activity. The findings demonstrate that AODE could be a novel hepatoprotective target in drug-induced liver damage with a further single compound-based animal study.

Virtual Screening of Indonesian Herbal Compounds with Neuraminidase Inhibitor Activity against N2 Influenza Virus Protein: An in silico Study

Background: Neuraminidase inhibitor (NAI) is one of anti-influenza drugs recommended for use by the World Health Organization (WHO). However, after treatment with NAI drugs in human, resistance to influenza antiviral drugs is begun to rise. Therefore, identification of compounds from Indonesian herbal plants as natural inhibitors of the influenza virus neuraminidase protein needs to be conducted for the development of new anti-influenza drugs.Materials and methods: The crystal structure of the neuraminidase protein complex used in this study was obtained from the Protein Data Bank (PDB). Structure-based pharmacophore modeling was performed using LigandScout version 4.4.5 software. Indonesian herbal plant compounds were collected from the HerbalDB database. Protein and ligand processing was carried out using Autodock 4.2 software. The 3D interaction visualization was carried out with Autodock software, while 2D interaction visualization was carried out with LigPlot software. To determine the toxicity and drug-likeliness of the ligand, the test ligands that had the best docking results were predicted using SwissADME and AdmetSAR.Results: From the virtual screening results, 24 hits were found, and five compounds had the best binding energy among the 24 tested compounds, these were pollenitin (ΔG=-7.22 kcal/mol), OPC-4:0 (ΔG=-7.11 kcal/mol), 6-hydroxykaempferol (ΔG=-7.08 kcal/mol), 5,8-dihydroxy-7,4'-dimethoxyflavone (ΔG=-7.07 kcal/mol), and 3,5,6,7-tetrahydroxy-4'-methoxyflavone (ΔG=-6.95 kcal/mol). The best five compounds were then chosen for further analysis.Conclusion: OPC-4:0 is found to be the best compound for the NAI based on its binding energy, pharmacokinetics, toxicity, and drug-likeliness. Thus, OPC-4:0 might be a potential candidate as a NAI of HxN2 virus. Keywords: influenza, molecular docking, neuraminidase, resistance, virtual screening

Comparing Life Expectancy Determinants Between Indonesia and Oman from 1980 to 2020.

Life expectancy (LE) is a health indicator of a population's health and well-being. Modeling the trajectory of LE aligns with the objectives of Indonesia's Vision 2045 and Oman's Vision 2040. This study examines the influence of health status-resources (HSR), macroeconomic (ME), and sociodemographic (SD) factors on LE in Indonesia and Oman. These two nations navigate the challenges of the middle-income trap in the Organization of Islamic Cooperation. This study adopted a national-scale population-based approach that focuses on retrospective observations. We used partial least square structural equation models with World Bank data from 1980 to 2020 to analyze the relationship between the mentioned factors and the LE of Oman and Indonesia. For Indonesia's model, the results showed that ME has a total effect of β = 0.737 (p < 0.05) on LE, SD has a total effect of β = 0.675 (p < 0.05) on LE, and HSR has a total effect of β = 0.823 (p < 0.05) on LE. In Oman's model, ME has a total effect of β = 0.848 (p < 0.05) on LE, SD has a total effect of β = 0.755 (p < 0.05) on LE, and HSR has a total effect of β = 0.335 (p < 0.05) on LE. The findings underscore the need for policies that meld health and societal perspectives to improve public health in both nations. A shift in public health interventions and perceptions towards socioeconomic well-being and societal issues is pivotal for advancing LE growth, potentially steering these countries from the middle-income trap.

Understanding and predicting lapses in mortgage life insurance using a machine learning approach

Mortgage Life Insurance (MLI) offers lucrative opportunities for insurers. However, customer retention has proven to be a daunting challenge, particularly following the regulatory changes of 2009 in Europe. New market entrants strategically employing low-premium tactics have reshaped the competitive landscape, leading established insurers and banks to grapple with retaining their MLI clientele. Consequently, increasing policy lapses hold critical implications for these financial entities. Responding to this intricate landscape, our research presents a predictive model that pinpoints the MLI policies at risk of lapse and disentangles the underlying factors propelling this risk. The objective is to provide insurers with a practical and strategic tool to improve customer retention, enabling them to identify specific actions to reduce customer attrition, improve financial stability, and strengthen customer loyalty. We used a dataset obtained from an insurance company and its partner bank to build the model. The effectiveness of four machine learning models, namely Logistic Regression, Random Forest, Neural Networks, and XGBoost, is investigated, with XGBoost outperforming the others. SHapley Additive exPlanations (SHAP) were utilized to bolster interpretability, thereby facilitating the conception and explication of the predictive model’s most influential features. Underpinning the benefits of a nuanced exploration, the study’s focus on a solitary insurance protection product and integrating bank data enabled us to apprehend the multifaceted drivers of lapse behavior. The study accentuates the merit of comprehensive data encapsulating a holistic perspective, with the four most influential features originating from bank data. From an insurer’s standpoint, this research provides a strategic vantage point to proactively identify and engage with customers at risk of policy lapse and reformulate their policies to mitigate customer attrition.

A Reconstruction of May–June Mean Temperature since 1775 for Conchos River Basin, Chihuahua, Mexico, Using Tree-Ring Width

Currently there are several precipitation reconstructions for northern Mexico; however, there is a lack of temperature reconstructions to understand past climate change, the impact on ecosystems and societies, etc. The central region of Chihuahua is located in a transition zone between the Sierra Madre Occidental and the Great Northern Plain, characterized by extreme temperatures and marked seasonal variability. The objectives of this study were (1) to generate a climatic association between variables from reanalysis models and the earlywood series for the center of Chihuahua, (2) to generate a reconstruction of mean temperature, (3) to determine extreme events, and (4) to identify the influence of ocean–atmosphere phenomena. Chronologies were downloaded from the International Tree-Ring Data Bank and climate information from the NLDAS-2 and ClimateNA reanalysis models. The response function was performed using climate models and regional dendrochronological series. A reconstruction of mean temperature was generated, and extreme periods were identified. The representativeness of the reconstruction was evaluated through spatial correlation, and low-frequency events were determined through multitaper spectral analysis and wavelet analysis. The influence of ocean–atmosphere phenomena on temperature reconstruction was analyzed using Pearson correlation, and the influence of ENSO was examined through wavelet coherence analysis. Highly significant correlations were found for maximum, minimum, and mean temperature, as well as for precipitation and relative humidity, before and after the growth year. However, the seasonal period with the highest correlation was found from May to June for mean temperature, which was used to generate the reconstruction from 1775 to 2022. The most extreme periods were 1775, 1801, 1805, 1860, 1892–1894, 1951, 1953–1954, and 2011–2012. Spectral analysis showed significant frequencies of 56.53 and 2.09 years, and wavelet analysis from 0 to 2 years from 1970 to 1980, from 8 to 11 years from 1890 to 1910, and from 30 to 70 years from 1860 to 2022. A significant association was found with the Multivariate ENSO Index phenomenon (r = 0.40; p = 0.009) and Pacific Decadal Oscillation (r = −0.38; p = 0.000). Regarding the ENSO phenomenon, an antiphase association of r = −0.34; p = 0.000 was found, with significant periods of 1 to 4 years from 1770 to 1800, 1845 to 1850, and 1860 to 1900, with periods of 6 to 10 years from 1875 to 1920, and from 6 to 8 years from 1990 to 2000. This study allowed a reconstruction of mean temperature through reanalysis data, as well as a historical characterization of temperature for central Chihuahua beyond the observed records.

Associations between hollow viscus injury and acute kidney injury in blunt abdominal trauma: A national trauma data bank analysis.

It is well established that hollow viscus perforation leads to sepsis and acute kidney injury (AKI) in non-trauma patients. However, the relationship between traumatic hollow viscus injury (HVI) and AKI is not well understood. Utilizing data from the National Trauma Data Bank, we investigated whether HVI serves as a risk factor for AKI. Additionally, we examined the characteristics of AKI in stable patients who underwent conservative treatment. We reviewed blunt abdominal trauma (BAT) cases from 2012 to 2015, comparing patients with and without AKI. Significant factors from univariate analysis were tested in a multivariate logistic regression (MLR) to identify independent AKI determinants. We also analyzed subsets: patients without HVI and stable patients given conservative management. Out of the 563,040 BAT patients analyzed, 9073 (1.6%) developed AKI. While a greater proportion of AKI patients had HVI than those without AKI (13.3% vs. 5.2%, p<0.001), this difference wasn't statistically significant in the MLR (p=0.125). Notably, the need for laparotomy (odds=3.108, p<0.001) and sepsis (odds=13.220, p<0.001) were identified as independent risk factors for AKI. For BAT patients managed conservatively (systolic blood pressure >90mmHg, without HVI or laparotomy; N=497,066), the presence of sepsis was a significant predictor for the development of AKI (odds=16.914, p<0.001). While HVI wasn't a significant risk factor for AKI in BAT patients, the need for laparotomy was. Stable BAT patients managed conservatively are still at risk for AKI due to non-peritonitis related sepsis.

Potencial petrolífero remanescente do Brasil - Estado da arte 2023

Petroleum and its byproducts will continue to be humankind’s primary energy source for at least 50 years. Brazil is already one of the main suppliers of this raw material and will continue as such for the next decades. Several large sedimentary basins in Brazil are practically untouched by exploration, especially in the deep and ultra-deep water realms. There are also large tracts of unexplored areas in basins that already have discoveries, reserves and hydrocarbon production. The objective of this work is to make predictions about the remaining petroleum potential of the Brazilian basins. The predictions are made based on the big data bank acquired by the petroleum industry in Brazil in the last 83 years, based on the most updated concepts in petroleum geology, and mainly on my 45 years of experience as an explorationist. Our final forecast for the remaining petroleum potential of Brazil lies in the range of 50-100 billion barrels of oil equivalent recoverable (boer) (prospective resources, already identified or not, but still needed to be discovered). The basins that will contribute the most to realizing this potential are Santos/Campos, Pará-Maranhão/Foz do Amazonas/Barreirinhas, Pelotas and Paraná; besides, in a lesser scale, but still significant, Sergipe-Alagoas, Espírito Santo, Camamu-Almada, Solimões and Parnaiba. We are perfectly aware that Geology is a complex and astounding science. New data, innovative new technologies and creative concepts never formulated can quickly destroy these predictions, for better or worse.

Effects of Concomitant Traumatic Spinal Cord and Brain Injury on In-Hospital Mortality: A Retrospective Analysis of a Nationwide Trauma Registry in Japan.

Isolated traumatic spinal cord injury (t-SCI) and traumatic brain injury (TBI) represent significant public health concerns, resulting in long-term disabilities and necessitating sophisticated care, particularly when occurring concurrently. The impact of these combined injuries, while crucial in trauma management, on clinical, socioeconomic, and health care outcomes is largely unknown. To address this gap, our secondary retrospective cohort study used data from the Japan Trauma Data Bank, covering patients enrolled over a 13-year period (2006-2018), to elucidate the effects of concurrent t-SCI and TBI on in-hospital mortality. Data on patient demographics, injury characteristics, treatment modalities, and outcomes were analyzed. Multivariate logistic regression analysis was performed to examine prognostic variables associated with in-hospital mortality, including interaction terms between t-SCI severity and TBI presence. This study included 91,983 patients with neurotrauma, with a median age of 62 years (69.7% men). Among the patients, 9,018 (9.8%) died in the hospital. Concomitant t-SCI and TBI occurred in 2,954 (3.2%) patients. t-SCI only occurred in 9,590 (10.4%) patients, whereas TBI only occurred in the majority of these cases (79,439, 86.4%). Multivariate logistic regression analysis revealed age; sex; total number of comorbidities; systolic blood pressure at presentation; Glasgow coma scale score at presentation; and Abbreviated Injury Scale (AIS) scores for head, face, chest, abdomen, cervical-SCI, thoracic-SCI, and lumbar-SCI as significant independent factors for in-hospital mortality. The odds ratio of cervical-SCI × head AIS as an interaction term was 0.85 (95% confidence interval: 0.77-0.95), indicating a negative interaction. In conclusion, we identified 12 factors associated with in-hospital mortality in patients with t-SCI. In addition, the negative interaction between cervical t-SCI and TBI suggests that the presence of t-SCI in patients with TBI may be underestimated. This study highlights the importance of early recognition and comprehensive management of these complex trauma conditions while considering the possibility of concomitant t-SCI in patients with TBI.

Assessing pH-Dependent Conformational Changes in the Fusion Peptide Proximal Region of the SARS-CoV-2 Spike Glycoprotein

One of the entry mechanisms of the SARS-CoV-2 coronavirus into host cells involves endosomal acidification. It has been proposed that under acidic conditions, the fusion peptide proximal region (FPPR) of the SARS-CoV-2 spike glycoprotein acts as a pH-dependent switch, modulating immune response accessibility by influencing the positioning of the receptor binding domain (RBD). This would provide indirect coupling of RBD opening to the environmental pH. Here, we explored this possible pH-dependent conformational equilibrium of the FPPR within the SARS-CoV-2 spike glycoprotein. We analyzed hundreds of experimentally determined spike structures from the Protein Data Bank and carried out pH-replica exchange molecular dynamics to explore the extent to which the FPPR conformation depends on pH and the positioning of the RBD. A meta-analysis of experimental structures identified alternate conformations of the FPPR among structures in which this flexible regions was resolved. However, the results did not support a correlation between the FPPR conformation and either RBD position or the reported pH of the cryo-EM experiment. We calculated pKa values for titratable side chains in the FPPR region using PDB structures, but these pKa values showed large differences between alternate PDB structures that otherwise adopt the same FPPR conformation type. This hampers the comparison of pKa values in different FPPR conformations to rationalize a pH-dependent conformational change. We supplemented these PDB-based analyses with all-atom simulations and used constant-pH replica exchange molecular dynamics to estimate pKa values in the context of flexibility and explicit water. The resulting titration curves show good reproducibility between simulations, but they also suggest that the titration curves of the different FPPR conformations are the same within the error bars. In summary, we were unable to find evidence supporting the previously published hypothesis of an FPPR pH-dependent equilibrium: neither from existing experimental data nor from constant-pH MD simulations. The study underscores the complexity of the spike system and opens avenues for further exploration into the interplay between pH and SARS-CoV-2 viral entry mechanisms.

Comparison of Changes in the Neutrophil-lymphocyte Ratio (NLR) and Post-surgery Outcomes in Child Patients with COVID-19 and Non-COVID-19

Background Pediatric patients with COVID-19 who undergo surgery have two actual problems, both the presence of a viral inflammatory response and an inflammatory response to surgery, which can affect the outcome of surgery and therapy. NLR is considered a fairly accurate predictor of surgery and COVID-19 infection. Objective This study aims to investigate the effect of changes in neutrophil-lymphocyte (NLR) on post-surgery outcomes in pediatric patients with COVID-19 infection. Methods It is a retrospective cohort study with an observational analysis. This research is a multi-center study on COVID-19 patients using a data bank of pediatric patients with COVID-19 and control patients non COVID-19 children undergoing surgery in three hospitals. Data recorded were age, gender, nutritional status, anesthetic status before surgery, length of stay, repeat surgery, sepsis, septic shock, and mortality. Statistical analysis was performed by the U-mann Whitney test. Results The study sample consisted of 87 total samples consisting of 2 groups, namely the group of pediatric patients with 29 samples with COVID-19 and 58 non-COVID-19 samples. Gender, nutritional status, length of stay, the incidence of reoperation, sepsis, septic shock, and mortality were not significantly different between the two groups with a p&lt;0.05. Risk analysis by looking at the incidence of sepsis and septic shock obtained an intersection point of 2.175 and 2.225, respectively. Analysis of changes in the NLR (delta-NLR) in the mortality events of the COVID-19 group and non-COVID-19 found that the NLR of the COVID-19 group was two times greater than the cut-off NLR of 2,175. Conclusion There is no difference in the outcome of surgery for children with COVID-19 and non-COVID-19 based on the NLR change analysis.

The Nexus Between Higher Education Expansion and Economic Growth in Ethiopia: An Empirical Examination

Over the past three decades, Ethiopia’s higher education system has undergone substantial expansion, marked by an increase in the number of universities from two to more than 100 and a surge in student enrollment from 48 000 to more than 400 000. Despite this growth, there is a paucity of research on the relationship between higher education expansion and economic growth, with the few quantitative studies that have been undertaken yielding inconsistent outcomes. This research study embraced endogenous economic growth theory, employed the Autoregressive Distributive Lag (ARDL) bound testing model, and used World Bank data from 1991 to 2021 to explore the relationship between economic growth (measured by GDP per capita) and the expansion of higher education (proxied by gross tertiary enrollment). Contrary to prevailing assumptions, the study uncovered an insignificant association between higher education expansion and economic growth. Unlike other studies, it used qualitative analysis to unearth the potential contributing factors and identified subpar educational quality, limited university autonomy, and constrained academic freedom as critical issues. It is recommended that policymakers in countries undergoing similar higher education expansion should not only focus on increasing the number of students, but also prioritise improving the quality of education, granting greater autonomy to universities, and ensuring academic freedom. These factors are crucial for higher education to effectively contribute to economic growth.

Understanding and predicting lapses in mortgage life insurance using a machine learning approach

Mortgage Life Insurance (MLI) offers lucrative opportunities for insurers. However, customer retention has proven to be a daunting challenge, particularly following the regulatory changes of 2009 in Europe. New market entrants strategically employing low-premium tactics have reshaped the competitive landscape, leading established insurers and banks to grapple with retaining their MLI clientele. Consequently, increasing policy lapses hold critical implications for these financial entities. Responding to this intricate landscape, our research presents a predictive model that pinpoints the MLI policies at risk of lapse and disentangles the underlying factors propelling this risk. The objective is to provide insurers with a practical and strategic tool to improve customer retention, enabling them to identify specific actions to reduce customer attrition, improve financial stability, and strengthen customer loyalty. We used a dataset obtained from an insurance company and its partner bank to build the model. The effectiveness of four machine learning models, namely Logistic Regression, Random Forest, Neural Networks, and XGBoost, is investigated, with XGBoost outperforming the others. SHapley Additive exPlanations (SHAP) were utilized to bolster interpretability, thereby facilitating the conception and explication of the predictive model’s most influential features. Underpinning the benefits of a nuanced exploration, the study’s focus on a solitary insurance protection product and integrating bank data enabled us to apprehend the multifaceted drivers of lapse behavior. The study accentuates the merit of comprehensive data encapsulating a holistic perspective, with the four most influential features originating from bank data. From an insurer’s standpoint, this research provides a strategic vantage point to proactively identify and engage with customers at risk of policy lapse and reformulate their policies to mitigate customer attrition.

Countries’ green brands within the context of sustainable development goals

This study aims to fill the gap in estimating the Green Country Brand Strength Index and its convergence among EU countries and Ukraine (as a potential EU candidate) from 2006 to 2020. Drawing upon data from the World Data Bank, Eurostat, and The Food and Agriculture Organization, the study employs various analytical tools including principal component analysis, the global Malmquist‒Luenberger productivity index, the entropy method, and σ- and β-convergence theories. The findings suggest a notable increase in the green brand values of the analyzed countries from 2006 to 2020, with a significant expansion of the measurement scale. Notably, France, Germany, Spain, the Netherlands, and Sweden emerged as leaders in the Green Country Brand Strength Index by 2020. Furthermore, the study uncovers convergent policies among EU countries aimed at fostering green brands aligned with the Sustainable Development Goals, as evidenced by a decline in σ- and β-convergences. This research provides valuable insights into the dynamics of green branding and policy convergence within the EU and prospective candidate countries. It introduces a distinctive approach to assessing and analyzing green brand strength, emphasizing its critical importance to the formulation of sustainable development policies and strategies.

Multireceptor Analysis for Evaluating the Antidiabetic Efficacy of Karanjin: A Computational Approach.

Diabetes mellitus, notably type 2, is a rising global health challenge, prompting the need for effective management strategies. Common medications such as metformin, insulin, repaglinide and sitagliptin can induce side effects like gastrointestinal disturbances, hypoglycemia, weight gain and specific organ risks. Plant-derived therapies like Karanjin from Pongamia pinnata present promising alternatives due to their historical use, holistic health benefits and potentially fewer adverse effects. This study employs in silico analysis to explore Karanjin's interactions with diabetes-associated receptors, aiming to unveil its therapeutic potential while addressing the limitations and side effects associated with conventional medications. The research encompassed the selection of proteins from the Protein Data Bank (PDB), followed by structural refinement processes and optimization. Ligands such as Karanjin and standard drugs were retrieved from PubChem, followed by a comprehensive analysis of their ADMET profiling and pharmacokinetic properties. Protein-ligand interactions were evaluated through molecular docking using AutoDockTools 1.5.7, followed by the analysis of structural stability using coarse-grained simulations with CABS Flex 2.0. Molecular dynamics simulations were performed using Desmond 7.2 and the OPLS4 force field to explore how Karanjin interacts with proteins over 100 nanoseconds, focusing on the dynamics and structural stability. Karanjin, a phytochemical from Pongamia pinnata, shows superior drug candidate potential compared to common medications, offering advantages in efficacy and reduced side effects. It adheres to drug-likeness criteria and exhibits optimal ADMET properties, including moderate solubility, high gastrointestinal absorption and blood-brain barrier penetration. Molecular docking revealed Karanjin's highest binding energy against receptor 3L2M (Pig pancreatic alpha-amylase) at -9.1 kcal/mol, indicating strong efficacy potential. Molecular dynamics simulations confirmed stable ligand-protein complexes with minor fluctuations in RMSD and RMSF, suggesting robust interactions with receptors 3L2M. Karanjin demonstrates potential in pharmaceutical expansion for treating metabolic disorders such as diabetes, as supported by computational analysis. Prospects for Karanjin in pharmaceutical development include structural modifications for enhanced efficacy and safety. Nanoencapsulation may improve bioavailability and targeted delivery to pancreatic cells, while combination therapies could optimize treatment outcomes in diabetes management. Clinical trials and experimental studies are crucial to validate its potential as a novel therapeutic agent.