A method for overcoming the ceiling effect of bounded pain scales

Objective.The Verbal Numerical Scale (VNS) for rating pain isbounded between 0 (= no pain) and 10 (= worst pain imaginable). Wehypothesized that the limitations inherent to this boundary when ratingextremely painful stimuli may be identified by integrating the VNS with anunbounded score such as magnitude estimation of relative change. Methods.Volunteers received stimuli of increasing current via cutaneous electrodesuntil they rated > 5 on the VNS scale. This stimulus, termed S, wasarbitrarily assigned a magnitude estimate of 100%. Then, stimuli of varyingcurrents were delivered; two were 10 mA and 20 mA higher than S(S₊₁₀ and S₊₂₀), two were 1/2 of the current for the Sstimulus (S_1/2), and one was at the original current(S_repeat). The pain elicited by each stimulus was scored inproportion to the S stimulus. The extrapolated VNS score (VNS_ext)was determined by multiplying this magnitude estimate (%) by the VNS score forS. Main Results.Seventy percent of the stimuli with higher intensitythan S generated a VNS_ext score above 10. The mean magnitudeestimations for S₊₁₀ and S₊₂₀ were 186% and 242%: theygenerated mean (median) VNS_ext values of 12.4 [12] and16.2 [14], respectively (p = 0.019 for the differencebetween them by Wilcoxon signed rank test). Conclusions.The combineduse of VNS and magnitude estimation confirmed that the ceiling of the boundedpain scale may significantly limit a patient's ability to describe a newpain stimulus. VNS_ext may provide a means of overcoming thislimitation.     

Benign multicystic peritoneal mesothelioma in a cesarean-section scar presenting as a fungating mass

We report a case of a benign multicystic mesothelioma, which presented as a fungating mass through the anterior abdominal wall and arose in a cesarean-section scar without direct peritoneal involvement. A wide local excision was done and the diagnosis was confirmed by histopathology and immunohistochemistry. The postoperative course was uneventful and the patient is asymptomatic at 3 years' follow-up. Although a history of previous abdominal surgery has been reported in a patient with benign multicystic mesothelioma, to the best of our knowledge, there is no report of a benign multicystic mesothelioma arising in a cesarean-section scar or presentation as a fungating skin mass. This unusual presentation may point to a traumatic or inflammatory etiology, although seeding of the wound during the previous surgeries is a more likely postulate. A pertinent review of the literature on benign multicystic mesothelioma is also presented.     

Antiurease screening of alkyl chain-linked thiourea derivatives: in vitro biological activities,molecular docking,and dynamic simulations studies

Urease has become an important therapeutic target because it stimulates the pathogenesis of many human health conditions, such as pyelonephritis, the development of urolithiasis, hepatic encephalopathy, peptic ulcers, gastritis and gastric cancer. A series of alkyl chain-linked thiourea derivatives were synthesized to screen for urease inhibition activity. Structure elucidation of these compounds was done by spectral studies, such as IR, ¹H NMR and ¹³C NMR, and MS analysis. In vitro urease enzyme inhibition assay revealed that compound 3c was the most potent thiourea derivative among the series with IC₅₀ values of 10.65 ± 0.45 μM, while compound 3g also exhibited good activity with an IC₅₀ value of 15.19 ± 0.58 μM compared to standard thiourea with an IC₅₀ value of 15.51 ± 0.11 μM. The other compounds in the series possessed moderate to weak urease inhibition activity with IC₅₀ values ranging from 20.16 ± 0.48 to 60.11 ± 0.78 μM. The most potent compounds 3c and 3g were docked to jack bean urease (PDB ID: 4H9M) to evaluate their binding affinities and to find the plausible binding poses. The docked complexes were refined through 100 ns-long MD simulations. The simulation results revealed that the average RMSD of 3c was less than that of the 3g compound. Furthermore, the radius of gyration plots for both complexes showed that 3c and 3g docking predicted binding modes did not induce any conformational change in the urease structure.

Urease has become an important therapeutic target because it stimulates the pathogenesis of many human health conditions, such as pyelonephritis, the development of urolithiasis, hepatic encephalopathy, peptic ulcers, gastritis and gastric cancer.     

Structural Bases of Zoonotic and Zooanthroponotic Transmission of SARS-CoV-2

The emergence of multiple variants of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) highlights the importance of possible animal-to-human (zoonotic) and human-to-animal (zooanthroponotic) transmission and potential spread within animal species. A range of animal species have been verified for SARS-CoV-2 susceptibility, either in vitro or in vivo. However, the molecular bases of such a broad host spectrum for the SARS-CoV-2 remains elusive. Here, we structurally and genetically analysed the interaction between the spike protein, with a particular focus on receptor binding domains (RBDs), of SARS-CoV-2 and its receptor angiotensin-converting enzyme 2 (ACE2) for all conceivably susceptible groups of animals to gauge the structural bases of the SARS-CoV-2 host spectrum. We describe our findings in the context of existing animal infection-based models to provide a foundation on the possible virus persistence in animals and their implications in the future eradication of COVID-19.     

Prediction of Mechanical Properties of Fly-Ash/Slag-Based Geopolymer Concrete Using Ensemble and Non-Ensemble Machine-Learning Techniques

The emission of greenhouse gases and natural-resource depletion caused by the production of ordinary Portland cement (OPC) have a detrimental effect on the environment. Thus, an alternative means is required to produce eco-friendly concrete such as geopolymer concrete (GPC). However, GPC has a complex cementitious matrix and an ambiguous mix design. Aside from that, the composition and proportions of materials utilized may have an impact on the compressive strength. Similarly, the use of robust and efficient machine-learning (ML) approaches is now required to forecast the strength of such a composite cementitious matrix. As a result, this study anticipated the compressive strength of GPC with waste resources using ensemble and non-ensemble ML algorithms. This was accomplished through the use of Anaconda (Python). To build a strong ensemble learner by integrating weak learners, adaptive boosting, random forest (RF), and ensemble learner bagging were employed. Furthermore, ensemble learners were utilized on non-ensemble or weak learners, such as decision trees (DT) and support vector machines (SVM) via regression. The data encompassed 156 statistical samples in which nine variables, namely superplasticizer (kg/m³), fly ash (kg/m³), ground granulated blast-furnace slag (GGBS), temperature (°C), coarse and fine aggregate (kg/m³), sodium silicate (Na₂SiO₃), and sodium hydroxide (NaOH), were chosen to anticipate the results. Exploring it in depth, twenty sub-models with ensemble boosting and bagging approaches were trained, and tuning was performed to achieve the highest possible coefficient of determination (R²). Moreover, cross K-Fold validation analysis and statistical checks were performed via indicators for the evaluation of the models. The result revealed that ensemble approaches yielded robust performance compared to non-ensemble algorithms. Generally, an ensemble learner with the RF and bagging approach on a DT yielded robust performance by achieving a better R² as 0.93, and with the lowest statistical errors. The communal model in artificial-intelligence analysis, on average, improved the accuracy of the model.     

Immune Checkpoint Inhibitors: The Unexplored Landscape of Geriatric Oncology

Cancer is classically considered a disease of aging, with over half of all new cancer diagnoses occurring in patients over the age of 65 years. Immune checkpoint inhibitors (ICIs) have revolutionized cancer treatment, yet the participation of older adults with cancer in ICI trials has been suboptimal, particularly at the extremes of age. Despite significant improvement in treatment response and an improved toxicity profile when compared with conventional cytotoxic chemotherapies, many cancers develop resistance to ICIs, and these drugs are not free of toxicities. This becomes particularly important in the setting of older adults with cancer, who are generally frailer and harbor more comorbidities than do their younger counterparts. Immunosenescence, a concept involving age-related changes in immune function, may also play a role in differential responses to ICI treatment in older patients. Data on ICI treatment response in older adult with cancers remains inconclusive, with multiple studies revealing conflicting results. The molecular mechanisms underlying response to ICIs in older cancer patients are poorly understood, and predictors of response that can delineate responders from non-responders remain to be elucidated. In this review, we explore the unique geriatric oncology population by analyzing existing retrospective datasets, and we also sought to highlight potential cellular, inflammatory, and molecular changes associated with aging as potential biomarkers for response to ICIs.     

Estimating Radiation Shielding of Fired Clay Bricks Using ANN and GEP Approaches

This study aimed to determine how radiation attenuation would change when the thickness, density, and compressive strength of clay bricks, modified with partial replacement of clay by fly ash, iron slag, and wood ash. To conduct this investigation, four distinct types of bricks—normal, fly ash-, iron slag-, and wood ash-incorporated bricks were prepared by replacing clay content with their variable percentages. Additionally, models for predicting the radiation-shielding ability of bricks were created using gene expression programming (GEP) and artificial neural networks (ANN). The addition of iron slag improved the density and compressive strength of bricks, thus increasing shielding capability against gamma radiation. In contrast, fly ash and wood ash decreased the density and compressive strength of burnt clay bricks, leading to low radiation shielding capability. Concerning the performance of the Artificial Intelligence models, the root mean square error (RMSE) was determined as 0.1166 and 0.1876 nC for the training and validation data of ANN, respectively. The training set values for the GEP model manifested an RMSE equal to 0.2949 nC, whereas the validation data produced RMSE = 0.3507 nC. According to the statistical analysis, the generated models showed strong concordance between experimental and projected findings. The ANN model, in contrast, outperformed the GEP model in terms of accuracy, producing the lowest values of RMSE. Moreover, the variables contributing towards shielding characteristics of bricks were studied using parametric and sensitivity analyses, which showed that the thickness and density of bricks are the most influential parameters. In addition, the mathematical equation generated from the GEP model denotes its significance such that it can be used to estimate the radiation shielding of burnt clay bricks in the future with ease.     

Application of Soft-Computing Methods to Evaluate the Compressive Strength of Self-Compacting Concrete

This research examined machine learning (ML) techniques for predicting the compressive strength (CS) of self-compacting concrete (SCC). Multilayer perceptron (MLP), bagging regressor (BR), and support vector machine (SVM) were utilized for analysis. A total of 169 data points were retrieved from the various published articles. The data set was based on 11 input parameters, such as cement, limestone, fly ash, ground granulated blast-furnace slag, silica fume, rice husk ash, coarse aggregate, fine aggregate, superplasticizers, water, viscosity modifying admixtures, and one output with compressive strength of SCC. In terms of properly predicting the CS of SCC, the BR technique outperformed both the SVM and MLP models, as determined by the research results. In contrast to SVM and MLP, the coefficient of determination (R²) for the BR model was 0.95, whereas for SVM and MLP, the R² was 0.90 and 0.86, respectively. In addition, a k-fold cross-validation approach was adopted to check the accuracy of the employed models. The statistical measures mean absolute percent error, mean absolute error, and root mean square error ensure the validity of the model. Using sensitivity analysis, the influence of input factors on the intended CS of SCC was also explored. This analysis reveals that the highest contributing parameter towards the CS of SCC was cement with 16.2%, while rice husk ash contributed the least with 4.25% among all the input variables.     

Evaluation of therapeutic control in a Pakistani population with hypertension

Rationale, aims and objectives Cardiovascular diseases (CVD) are increasing at an alarming rate in South Asia. High blood pressure is a modifiable risk factor for CVD. In this study, we evaluated the control of blood pressure and the prevalence of cardiovascular risk factors in patients with hypertension. Method A cross‐sectional study was conducted in 50 primary health care centres throughout Pakistan. Individuals with a documented history of hypertension, receiving pharmacological therapy, were enrolled and evaluated for the control of their blood pressure. Results The recommended therapeutic control of hypertension (systolic blood pressure <140 mmHg, diastolic blood pressure <90 mmHg) was seen in only 6.4% of the study participants. Values of both the mean systolic and diastolic blood pressures in all subjects were higher than the desired therapeutic levels (P < 0.001). There was a high prevalence in the study population of established but modifiable risk factors of CVD, such as smoking (30.5%), hypercholesterolemia (59.5%) and sedentary lifestyle (43.5%). Lack of therapeutic control of systolic blood pressure was found significantly associated with age, hypercholesterolemia and sedentary lifestyle (P < 0.05). Conclusions Patients being treated at primary health care centres in Pakistan have inadequate control of high blood pressure. Evidence‐based continuous education of primary health care physicians is a necessary intervention for optimizing treatment strategies and achieving better therapeutic control of hypertension in our population.     

In-silico studies of inhibitory compounds against protease enzymes of SARS-CoV-2

In December 2019, a COVID-19 outbreak caused by SARS-CoV-2 raised worldwide health concerns. In this case, molecular docking and drug repurposing computational approaches were engaged to check the efficiency of plant-based inhibitory compounds against SARS-CoV-2 main protease enzyme and papain-like protease enzyme. Twenty phytochemical inhibitory compounds were collected. Then these compounds were screened based on Lipinski’s rule. As a result of this screening eleven compounds were further selected. Quantitative structure–activity relationships analysis was done before molecular docking to check especially the antiviral activity of inhibitory compounds. Docking validation of these compounds was checked by using online server Database of Useful Decoys: Enhanced. Binding affinity value, and pharmacokinetic properties of Aloin compound indicated that it can be used against main protease enzyme of SARS-CoV-2. So, it makes it a promising compound to follow further in cell and biochemical-based assays to explore its potential use against COVID-19.