Application of parallel artificial membrane permeability assay technique and chemometric modeling for blood-brain barrier permeability prediction of protein kinase inhibitors

Aim: This study aims to investigate the passive diffusion of protein kinase inhibitors through the blood-brain barrier (BBB) and to develop a model for their permeability prediction. Materials & methods: We used the parallel artificial membrane permeability assay to obtain logPe values of each of 34 compounds and calculated descriptors for these structures to perform quantitative structure-property relationship modeling, creating different regression models. Results: The logPe values have been calculated for all 34 compounds. Support vector machine regression was considered the most reliable, and CATS2D_09_DA, CATS2D_04_AA, B04[N-S] and F07[C-N] descriptors were identified as the most influential to passive BBB permeability. Conclusion: The quantitative structure-property relationship-support vector machine regression model that has been generated can serve as an efficient method for preliminary screening of BBB permeability of new analogs.

Neutrophil-to-Lymphocyte Ratio (NLR), Platelet-to-Lymphocyte Ratio (PLR) and Monocyte-to-Lymphocyte Ratio (MLR) as Biomarkers in Diagnosis Evaluation of Acute Exacerbation of Chronic Obstructive Pulmonary Disease: A Retrospective, Observational Study

Purpose

Hierarchical management is advocated in China to effectively manage chronic obstructive pulmonary disease (COPD) patients and reduce the incidence and mortality of acute exacerbation of COPD (AE-COPD). However, primary and community hospitals often have limited access to advanced equipment and technology. Complete blood count (CBC), which is commonly used in these hospitals, offers the advantages of being cost-effective and easily accessible. This study aims to evaluate the significance of routine blood indicators in aiding of diagnosing AE-COPD.

Patients and Methods

In this research, we enrolled a total of 112 patients diagnosed with AE-COPD, 92 patients with stable COPD, and a control group comprising 60 healthy individuals. Clinical characteristics, CBC parameters, and serum CRP levels were collected within two hours. To assess the associations between NLR/PLR/MLR and CRP by Spearman correlation test. The diagnostic accuracy of NLR, PLR and MLR in AE-COPD was assessed using Receiver Operating Characteristic Curve (ROC) and the area under the curve (AUC). Binary Logistic Regression analysis was conducted for the indicators of NLR, PLR and MLR.

Results

We found that patients with AE-COPD had significantly higher levels of NLR, PLR and MLR in contrast to patients with stable COPD. Additionally, the study revealed a noteworthy correlation between CRP and NLR (rs=0.5319, P<0.001), PLR (rs=0.4424, P<0.001), and MLR (rs=0.4628, P<0.001). By utilizing specific cut-off values, the amalgamation of NLR, PLR and MLR augmented diagnostic sensitivity. Binary logistic regression analysis demonstrated that heightened NLR and MLR act as risk factors for the progression of AE-COPD.

Conclusion

The increasing levels of NLR, PLR and MLR could function as biomarkers, akin to CRP, for diagnosis and assessment of acute exacerbations among COPD patients. Further research is required to validate this concept.

The predictive value of NLR, PLR and MLR in the differential diagnosis of benign uterine diseases and endometrial malignant tumors

OBJECTIVE

To explore the application of neutrophils to lymphocytes ratio (NLR), platelet to lymphocyte ratio (PLR) and monocyte to lymphocyte ratio (MLR) in the differential diagnosis of benign uterine diseases and endometrial malignant tumors.

METHODS

80 patients with endometrial malignant tumor diagnosed in our hospital from January 2019 to December 2022 were selected as the study group, and 74 patients with benign uterine diseases confirmed by pathology in our hospital during the same period were randomly selected as the control group. The differences of NLR, PLR and MLR in the peripheral blood of patients in each group were compared, and the value of individual indicators in the diagnosis of endometrial malignant tumor was evaluated using the Receiver Operating Characteristic (ROC) curve.

RESULTS

In peripheral blood, the NLR, PLR and MLR value in patients with endometrial cancer (EC) were significantly higher than those in patients with benign uterine diseases (P < 0.05). The area under the curve (AUC) of NLR, PLR, MLR in peripheral blood were 0.777, 0. 675 and 0.698. The best cutoff values were 2.02, 132.06 and 0.166. The sensitivity and specificity were 62.5% and 79.7%, 62.5% and 63.5%, 81 3% and 47.3%. The combination of these three indicators can significantly improved the diagnostic efficiency in endometrial cancer (AUC = 0.780), and the sensitivity and specificity were 60% and 83.8%.

CONCLUSIONS

In peripheral blood, NLR, PLR and MLR have certain diagnostic value in the differential diagnosis of endometrial cancer. When NLR, PLR and MLR are elevated, we should be alert to the occurrence of endometrial malignant tumors, and the combined diagnostic efficiency is high.

CLR (C-Reactive Protein to Lymphocyte Ratio) Served as a Promising Predictive Biomarker for Cerebral Vasospasm in Aneurysmal Subarachnoid Hemorrhage (aSAH): A Retrospective Cohort Study

Background: Subarachnoid hemorrhage is a devastating disease. Even after state-of-the-art treatment patients suffer from complications, including cerebral vasospasm (CVS), delayed cerebral ischemia (DCI), and chronic hydrocephalus (CH) following aneurysmal subarachnoid hemorrhage (aSAH). The aim of our study is to identify the predictive value of the C-reactive protein to lymphocyte ratio (CLR) for neurological functional outcome and complications after aSAH. Methods: We retrospectively analyzed a total of 166 aSAH patients who met the inclusion criteria enrolled in our study. Multivariate logistic regression analyses were performed to evaluate the independent risk factors. The predictive value of different models was compared by calculating the areas under the receiver operating characteristic (ROC) curve. Results: On-admission levels of CLR in patients with poor outcomes (6 months mRS 3-6), CVS, DCI, and CH were significantly higher than those in patients with good outcomes (6 months mRS 0-2), non-CVS, non-DCI, and non-CH. Multivariate logistic regression analysis revealed that admission CLR was independently associated with CVS (OR [95% CI] 2.116 [1.507-2.971]; p < 0.001), and DCI (OR [95% CI] 1.594 [1.220-2.084]; p = 0.001). In ROC analysis, the area under the curve (AUC) of CLR for poor outcomes (6 months mRS 3-6), CVS, DCI, and CH prediction were (AUC [95% CI] 0.639 [0.555-0.724]; p = 0.002), (AUC [95% CI] 0.834 [0.767-0.901]; p < 0.001), (AUC [95% CI] 0.679 [0.581-0.777]; p < 0.001), and (AUC [95% CI] 0.628 [0.543-0.713]; p = 0.005) revealing that admission CLR had a favorable predictive value for CVS after aSAH. The sensitivity and specificity of admission CLR for CVS prediction were 77.1% and 75.4%. On-admission CLR of 0.757 mg × 10-6 was identified as the best cutoff threshold to discriminate between CVS and non-CVS (CVS: CLR < 0.757 mg × 10-6 11/100 [11.0%] vs. CLR ≥ 0.757 mg × 10-6 37/66 [56.1%]; p < 0.001). Conclusions: High levels of on-admission CLR serve as an independent risk factor for CVS and DCI after aSAH. Admission CLR is an easy-to-quantify laboratory parameter that efficiently predicts the CVS after aSAH, which can provide some guidance for clinicians to evaluate for possible progression and treatment strategies in patients with aSAH.

In silico soil degradation and ecotoxicity analysis of veterinary pharmaceuticals on terrestrial species: first report

With the aim of persistence property analysis and ecotoxicological impact of veterinary pharmaceuticals on different terrestrial species, different classes of veterinary pharmaceuticals (n = 37) with soil degradation property (DT50) were gathered and subjected to QSAR and q-RASAR model development. The models were developed from 2D descriptors under organization for economic cooperation and development guidelines with the application of multiple linear regressions along with genetic algorithm. All developed QSAR and q-RASAR were statistically significant (Internal = R2adj: 0.721-0.861, Q2LOO: 0.609-0.757, and external = Q2Fn = 0.597-0.933, MAEext = 0.174-0.260). Further, the leverage approach of applicability domain assured the model's reliability. The veterinary pharmaceuticals with no experimental values were classified based on their persistence level. Further, the terrestrial toxicity analysis of persistent veterinary pharmaceuticals was done using toxicity prediction by computer assisted technology and in-house built quantitative structure toxicity relationship models to prioritize the toxic and persistent veterinary pharmaceuticals. This study will be helpful in estimation of persistence and toxicity of existing and upcoming veterinary pharmaceuticals.

Assessing livelihood resilience in drought-affected areas: Lessons from Raya Kobo district, northeast Ethiopia

In comparison to other types of resilience, livelihood resilience in the context of climate-related extremes like droughts is grounded in actual-life scenarios with the purpose of carefully assessing and improving the resiliency of individuals, households, communities, and nations. This study assesses households' livelihood resilience to droughts in Raya Kobo District. A mixed approach with a concurrent research design was used to achieve this goal. The quantitative data were collected from 354 randomly selected survey respondents, while the qualitative data were collected from purposefully chosen FGD and KI participants. Principal Component Analysis (PCA) and Multiple Linear Regression (MLR) models were employed to analyse the quantitative data, whereas thematic data analysis was used to analyse the qualitative data through the creation of major and sub-themes. To determine households' livelihood resilience, the livelihood resilience index (LRI) was measured using thirty-eight indicators of resilience based on the five livelihood assets. The study identified fifteen latent dimensions, such as infrastructure, technology, water harvesting scheme, land quality, cropping season, household working capacity, farm experience, educational status, social trust, risk response, social security, support service, income, crop diversity, and assets. The average score of these latent dimensions is 0.3999, suggesting that households in the study area are less resilient. The MLR results show a positive association between the latent dimensions and LRI and the relative importance of the latent dimensions for LRI. These findings provide significant policy implications regarding mitigating vulnerability, strengthening resilience, and establishing pathways out of livelihood insecurity. Education, healthcare, road construction, agricultural inputs (pesticides, herbicides, chemical fertilizers, and improved seeds), irrigation technologies (small-scale drip irrigation systems and human-powered pedals), income diversification, social trust, risk response, social security, support services, and asset building should be the focus of policymakers.

Generation of Non-Alloreactive Antiviral Central Memory CD8 Human Veto T Cells for Cell Therapy

Previous studies in mice demonstrated that CD8 T cells exhibit marked veto activity enhancing engraftment in several models for T cell-depleted bone marrow (TDBM) allografting. To reduce the risk of graft-versus-host disease (GVHD) associated with allogeneic CD8 veto T cells, these studies made use of naive CD8 T cells stimulated against third-party stimulators under cytokine deprivation and subsequent expansion in the presence of IL-15. More recently, it was shown that mouse CD8 veto T cells can be generated by stimulating CD8 memory T cells from ovalbumin immunized mice under cytokine deprivation, using ovalbumin as a third-party antigen. These cells also exhibited substantial enhancement of BM allografting without GVHD. In this study, we tested the hypothesis that stimulation and expansion of human CD8 memory T cells under IL-15 and IL-7 deprivation during the early phase of activation against recall viral antigens can lead to substantial loss of alloreactive T clones while retaining marked veto activity. Memory CD8 T cells were enriched by removal of CD45RA+, CD4+, and CD56+ cells from peripheral blood of cytomegalovirus (CMV)- and Epstein-Barr virus (EBV)-positive donors. In parallel, CD14+ monocytes were isolated; differentiated into mature dendritic cells (mDCs); pulsed with a library of CMV, EBV, adenovirus, and BK virus peptides; and irradiated. The CD8 T cell-enriched fraction was then cultured with the pulsed mDCs in the presence of IL-21 for 3 days, after which IL-15 and IL-7 were added. After 12 days of culture, the cells were tested by limiting dilution analysis for the frequency of alloreactive T cell clones and their veto activity. In preclinical runs using GMP reagents, we established that within 12 days of culture, a large number of highly homogenous CD8 T cells, predominantly expressing a central memory phenotype, could be harvested. These cells exhibited marked veto activity in vitro and >3-log depletion of alloreactivity. Based on these preclinical data, a phase 1-2 clinical trial was initiated to test the safety and efficacy of these antiviral CD8 central memory veto cells in the context of nonmyeloablative (NMA) T cell-depleted haploidentical hematopoietic stem cell transplantation (HSCT). In 2 validation runs and 11 clinical runs using GMP reagents, >1 × 1010 cells were generated from a single leukapheresis in 12 out of 13 experiments. At the end of 12 days of culture, there were 97 ± 2.5% CD3+CD8+ T cells, of which 84 ± 9.0% (range, 71.5% to 95.1%) exhibited the CD45RO+CD62L+ CM phenotype. Antiviral activity tested by intracellular expression of INF-γ and TNF-α and showed an average of 38.8 ± 19.6% positive cells on 6 hours of stimulation against the viral peptide mixture. Our results demonstrate a novel approach for depleting alloreactive T cell clones from preparations of antiviral CD8 veto cells. Based on these results, a phase 1-2 clinical trial is currently in progress to test the safety and efficacy of these veto cells in the context of NMA haploidentical T cell-depleted HSCT. Studies testing the hypothesis that these non-alloreactive CD8 T cells could potentially offer a platform for off-the-shelf veto chimeric antigen receptor T cell therapy in allogenic recipients, are warranted.

The Monocyte-to-Lymphocyte Ratio on the 6th Day Postburn Is Associated with Clinical Outcome of Severe Burns

OBJECTIVE

As an immune/inflammatory indicator, the application of monocyte-lymphocyte ratio (MLR) in the treatment of severe burns is lacking. The aim of this study was to investigate the dynamic changes of the MLR value in the early stage of severe burns and its clinical value.

METHODS

This is a 5-year retrospective cohort study involving 100 patients with severe burns (II-III degree and total body surface area (TBSA) >50%), in which the lymphocyte count, monocyte count, MLR value, C-reactive protein (CRP), creatinine (Scr), and capillary leakage index (CLI) were evaluated soon after injury, and 30-day mortality rates were investigated.

RESULTS

The MLR values in non-survivors with severe burns were higher than those in survivors in the first two days after injury, while the values on the 3rd, 5th, 6th and 7th day after injury were lower than those in survivors. The differences between the 6th and 7th days after injury were statistically significant. According to the results of logistic and Cox regression analysis, the MLR values on the 6th day after injury were independent predictors of mortality, and the area under the ROC curve of the 6th day MLR for severe burn-delayed death prediction was 0.658 (95% confidence interval, 0.541-0.774), and the optimal cut-off value was 0.991. The 30-day mortality rates differed significantly between the MLR6 ≥0.991 group and the MLR6≤0.991 group (P<0.05). Within one week after injury, the MLR values were negatively correlated with Scr, CRP and CLI levels for severe burns.

CONCLUSIONS

Our results revealed the dynamic characteristics of the MLR value in the early stage of severe burns, reflecting important changes in the immune/inflammatory related stress response soon after injury, low MLR level was associated with the worsening of disease condition.

Predictive value of CRP combined with peripheral blood cell ratio for the prognosis of advanced NSCLC

Lung cancer remains the leading cause of cancer-related deaths worldwide, with non-small cell lung cancer (NSCLC) accounting for the vast majority. In recent years, the interaction between inflammation and tumorigenesis has become the focus of attention, which has also confirmed the importance of inflammatory markers such as C-reactive protein (CRP) in prognosis. In this study, we explored the effects of CRP, systemic inflammatory immune index (SII), and monocyte/lymphocyte ratio (MLR) on the prognosis of patients with advanced NSCLC. We conducted a retrospective study of 274 patients suffering from stage III/IV NSCLC. Among them, 224 patients served as the training set and 50 patients served as the validation set. The independent factors affecting PFS (Progression-Free Survival) and OS (Overall Survival) in the patients were analyzed by Cox regression. Our results showed that CPR (HR=1.691, P=0.004), SlI (HR=1.960, P<0.001), MLR (HR=1.578, P=0.003), CEA (HR=1.845, P=0.006), NSE (HR=2.138, P=0.003) and adrenal metastasis (HR=2.896, P<0.003) were independent factors affecting the PFS of NSCLC patients. SII (HR=1.645, P=0.004), CEA (HR=2.021, P=0.002) and brain metastasis (HR=2.899, P<0.001) were independent factors affecting the OS of NLSCL patients. The DCA curve demonstrated that the prediction model provided better clinical net benefit in predicting patients' 6-month PFS and 12-month OS under different threshold probabilities. DeLong test showed no significant difference between AUCs of SII and risk score (P>0.05). Compared with CEA, SII and risk score had higher predictive value for patients' 6-month PFS and 12-month OS (P<0.05). In conclusion, the results of this study indicate that serum inflammatory factor SII can be used as an independent indicator to evaluate 6-month PFS and 12-month OS in patients with advanced NSCLC, and its predictive value is similar to that of the nomogram model.

The Prognostic Value of Platelet-Lymphocyte Ratio, Neutrophil-Lymphocyte Ratio, and Monocyte-Lymphocyte Ratio in Head and Neck Squamous Cell Carcinoma (HNSCC)-A Retrospective Single Center Study and a Literature Review

INTRODUCTION

Neutrophil-to-lymphocyte ratio (NLR), lymphocyte-to-monocyte ratio (LMR), and pallets-to-lymphocyte ratio (PLR) are currently validated as cheap and accessible biomarkers in different types of solid tumors, including head and neck cancers (HNC).

THE PURPOSE OF THE STUDY

To evaluate the possible purposes and biomarker value of NLR, PLR, and MLR recorded pre-treatment (radiotherapy/chemotherapy) in HNC.

MATERIALS AND METHODS

From 190 patients with HNC included in the oncology records in the oncology outpatient clinic of the Craiova County Emergency Hospital (from January 2002 to December 2022), 39 cases met the inclusion criteria (squamous cell carcinoma and the possibility to calculate the pre-treatment (chemotherapy/radiotherapy) value of NLR, PLR, and MLR. Overall survival (OS) values were correlated with NLR, PLR, and MLR.

RESULTS

The median values for NLR, PLR, and MLR were 6.15 (1.24-69), 200.79 (61.3-1775.0), and 0.53 (0.12-5.5), respectively. In the study, the mean values for NLR, PLR, and MLR of 2.88, 142.97, and 0.36, respectively, were obtained. The median OS in the study group was 11 months (1-120). Although a negative Pearson's correlation was present, the relationship between the variables was only weak, with values of R = 0.07, p = 0.67, R = 0.02, p = 0.31, and R = 0.07, p = 0.62 being related to NLR, PLR, and MLR, respectively, in correlation with OS. The median values of NLR, PLR, and MLR were calculated (1.53, 90.32, and 0.18, respectively) for the HNC cases with pre-treatment values of NLR < 2 and for the HNC cases with NLR values ≥ 6 (23.5, 232.78, and 0.79, respectively). The median OS for cases with NLR < 2 and NLR ≥ 6 were 17.4 and 13 months, respectively.

CONCLUSIONS

The comparative analysis of the data highlights a benefit to OS for cases low values of NLR. The role of not only borderline NLR values (between 2 and 6) as a prognostic marker in HNSCC but also the inclusion of PLR and MLR in a prognostic score must also be defined in the future. Prospective studies with more uniformly selected inclusion criteria could demonstrate the value of pre-treatment NLR, PLR, and MLR for treatment stratification through the intensification or de-escalation of non-surgical curative treatment in HNSCC.

R software for QSAR analysis in phytopharmacological studies

INTRODUCTION

In recent decades, quantitative structure-activity relationship (QSAR) analysis has become an important method for drug design and natural product research. With the availability of bioinformatic and cheminformatic tools, a vast number of descriptors have been generated, making it challenging to select potential independent variables that are accurately related to the dependent response variable.

OBJECTIVE

The objective of this study is to demonstrate various descriptor selection procedures, such as the Boruta approach, all subsets regression, the ANOVA approach, the AIC method, stepwise regression, and genetic algorithm, that can be used in QSAR studies. Additionally, we performed regression diagnostics using R software to test parameters such as normality, linearity, residual histograms, PP plots, multicollinearity, and homoscedasticity.

RESULTS

The workflow designed in this study highlights the different descriptor selection procedures and regression diagnostics that can be used in QSAR studies. The results showed that the Boruta approach and genetic algorithm performed better than other methods in selecting potential independent variables. The regression diagnostics parameters tested using R software, such as normality, linearity, residual histograms, PP plots, multicollinearity, and homoscedasticity, helped in identifying and diagnosing model errors, ensuring the reliability of the QSAR model.

CONCLUSION

QSAR analysis is vital in drug design and natural product research. To develop a reliable QSAR model, it is essential to choose suitable descriptors and perform regression diagnostics. This study offers an accessible, customizable approach for researchers to select appropriate descriptors and diagnose errors in QSAR studies.

Inflammatory Markers Used as Predictors of Subclinical Atherosclerosis in Patients with Diabetic Polyneuropathy

BACKGROUND

peripheral arterial disease (PAD) is identified late in diabetic patients because, in the majority of cases, it is associated with diabetic peripheral neuropathy, resulting in little or no symptoms, or symptoms that are completely neglected.

METHODS

In this study were enrolled all patients over 18 years of age, with diabetes mellitus type II for more than a year with poor glycemic control, diagnosed with diabetic polyneuropathy admitted to the Diabetology Department, Emergency County Hospital of Targu Mures, Romania between January 2020 and March 2023. We divided the patients into two groups, based on the presence or absence of subclinical atherosclerosis in the lower limb, named "SA" and "non-SA".

RESULTS

Patients in the SA group were older (p = 0.01) and had a higher incidence of IHD (p = 0.03), history of MI (p = 0.02), and diabetic nephropathy (p = 0.01). Moreover, patients with subclinical atherosclerosis had a higher BMI (p < 0.0001) and a longer duration of diabetes (p < 0.0001). Among all patients, the systemic inflammatory markers, MLR (r = 0.331, p < 0.001), NLR (r = 0.517, p < 0.001), PLR (r = 0.296, p < 0.001), SII (r = 0.413, p < 0.001), as well as BMI (r = 0.241, p < 0.001) and HbA1C (r = 0.489, p < 0.001), demonstrated a strong positive correlation with the diabetes duration. The multivariate logistic regression analysis showed that older patients (OR: 2.58, p < 0.001), the male gender (OR: 2.30, p = 0.006), a higher baseline levels of BMI (OR: 7.71, p < 0.001), and the duration of diabetes (OR: 8.65, p < 0.001) are predictors of subclinical atherosclerosis in DN patients. Additionally, the high baseline levels of all systemic inflammatory markers (for all: p < 0.001) and poor diabetes management (OR: 10.4, p < 0.001 for HbA1C; OR: 10.78, p < 0.001 for admission glucose) are independent predictors of SA.

CONCLUSIONS

the inflammatory markers, NLR, MLR, PLR, and SII, being cheap and easy to collect in routine medical practice from the standard blood tests, could be an important step in predicting vascular outcomes in diabetic patients and the disease's progression, playing a key role in follow-up visits in type-2 diabetic patients and PAD patients.

QSAR modeling of pyrazoline derivative as carbonic anhydrase inhibitors

The efficacy of 34 pyrazoline derivatives as carbonic anhydrase inhibitors was studied in silico. The quantum descriptors were calculated by the DFT/B3LYP method using the 6-31G(d) basis; the dataset was randomly divided into training and testing. By altering the compounds in the sets, four models were created, and they were then used to determine the predicted pIC50 values for the six chemicals in the test set. According to the OECD guidelines for QSAR model validation and the Golbraikh and Tropsha's criteria for model approval, each created model was independently validated both internally and externally, along with YRandomization. Model 3 is chosen because it has higher R₂, R_2test, and Q_2cv values (R₂ = 0.79, R²_test = 0.95, Q²_cv = 0.64). Only one descriptor has a proportional influence on pIC50 activity, but the other four descriptors have an inverse influence on pIC50 because of the negative contribution coefficient. Given the descriptors of the model, we could propose new molecules with remarkable inhibitory activity.

In Silico Studies of Indole Derivatives as Antibacterial Agents

Objectives

Molecular docking and QSAR studies of indole derivatives as antibacterial agents.

Methods

In this study, we used a multiple linear regressions (MLR) approach to construct a 2D quantitative structure activity relationship of 14 reported indole derivatives. It was performed on the reported antibacterial activity data of 14 compounds based on theoretical chemical descriptors to construct statistical models that link structural properties of indole derivatives to antibacterial activity. We have also performed molecular docking studies of same compounds by using Maestro module of Schrodinger. A set the molecular descriptors like hydrophobic, geometric, electronic and topological characters were calculated to represent the structural features of compounds. The conventional antibiotics sultamicillin and ampicillin were not used in the model development since their structures are different from those of the created compounds. Biological activity data was first translated into pMIC values (i.e. -log MIC) and used as a dependent variable in QSAR investigation.

Results

Compounds with high electronic energy and dipole moment were effective antibacterial agents against S. aureus, indole derivatives with lower κ₂ values were excellent antibacterial agents against MRSA standard strain, and compounds with lower R value and a high ²χ^v value were effective antibacterial agents against MRSA isolate.

Conclusion

Compounds 12 and 2 showed better binding score against penicillin binding protein 2 and penicillin binding protein 2a respectively.

Corneal Permeability and Uptake of Twenty-Five Drugs: Species Comparison and Quantitative Structure-Permeability Relationships

The purpose of this study was to determine corneal permeability and uptake in rabbit, porcine, and bovine corneas for twenty-five drugs using an N-in-1 (cassette) approach and relate these parameters to drug physicochemical properties and tissue thickness through quantitative structure permeability relationships (QSPRs). A twenty-five-drug cassette containing β-blockers, NSAIDs, and corticosteroids in solution at a micro-dose was exposed to the epithelial side of rabbit, porcine, or bovine corneas mounted in a diffusion chamber, and the corneal drug permeability and tissue uptake were monitored using an LC-MS/MS method. Data obtained were used to construct and evaluate over 46,000 quantitative structure-permeability (QSPR) models using multiple linear regression, and the best-fit models were cross-validated by Y-randomization. Drug permeability was generally higher in rabbit cornea and comparable between bovine and porcine corneas. Permeability differences between species could be explained in part by differences in corneal thickness. Corneal uptake between species correlated with a slope close to 1, indicating generally similar drug uptake per unit weight of tissue. A high correlation was observed between bovine, porcine, and rabbit corneas for permeability and between bovine and porcine corneas for uptake (R² ≥ 0.94). MLR models indicated that drug characteristics such as lipophilicity (LogD), heteroatom ratio (HR), nitrogen ratio (NR), hydrogen bond acceptors (HBA), rotatable bonds (RB), index of refraction (IR), and tissue thickness (TT) are of great influence on drug permeability and uptake. When data for all species along with thickness as a parameter was used in MLR, the best fit equation for permeability was Log (% transport/cm²·s) = 0.441 LogD - 8.29 IR + 8.357 NR - 0.279 HBA - 3.833 TT + 10.432 (R² = 0.826), and the best-fit equation for uptake was Log (%/g) = 0.387 LogD + 4.442 HR + 0.105 RB - 0.303 HBA - 2.235 TT + 1.422 (R² = 0.750). Thus, it is feasible to explain corneal drug delivery in three species using a single equation.

Local and regional factors of spatial differentiation of the excess mortality related to the COVID-19 pandemic in Romania

COVID-19 revealed some major weaknesses and threats that are related to the level of territorial development. In Romania, the manifestation and the impact of the pandemic were not homogenous, which was influenced, to a large extent, by a diversity of sociodemographic, economic, and environmental/geographic factors. The paper is an exploratory analysis focused on selecting and integrating multiple indicators that could explain the spatial differentiation of COVID-19-related excess mortality (EXCMORT) in 2020 and 2021. These indicators include, among others, health infrastructure, population density and mobility, health services, education, the ageing population and distance to the closest urban center. We analyzed the data from local (LAU2) and county level (NUTS3) by applying multiple linear regression and geographically weighted regression models. The results show that mobility and lower social distancing were far more critical factors for higher mortality than the intrinsic vulnerability of the population, at least in the first two years of COVID-19. However, the highly differentiated patterns and specificities of different areas of Romania resulting from the modelling of EXCMORT factors drive to the conclusion that the decision-making approaches should be place-specific in order to have more efficiency in case of pandemics.

CuO-ZnO p-n junctions for accurate prediction of multiple volatile organic compounds aided by machine learning algorithms

Detection and quantification of multiple volatile organic compounds (VOCs) are emerging as critical requirements for several niche applications including healthcare. It is desirable to get multiple gases identified rapidly and using minimum number of sensors. Heterojunctions of metal oxides are still among the top-picks for efficient VOC sensing because they unfold exciting sensing characteristics in addition to enhanced response. This work reports the synthesis of nanostructures of CuO, ZnO, and three CuO-ZnO p-n junctions having different weight percentages (1-0.5, 1-1, and 0.5-1) of CuO and ZnO, using a facile one-pot hydrothermal method. The nanomaterials were characterized using X-ray diffraction, field emission scanning electron microscopy, and UV-Visible spectroscopy. Resistive sensors were fabricated of all five nanomaterials and were tested for 25-200 ppm of four VOCs - isopropanol, methanol, acetonitrile, and toluene. The CuO and CuO-ZnO (1-0.5) sensors showed the highest response for isopropanol (7.5-65.3% and 19-122%, respectively) at 250 °C, CuO-ZnO (1-1) and CuO-ZnO (0.5-1) exhibited the highest responses for methanol (9-60%) and isopropanol (15-120%), respectively at 350 °C, and the intrinsic ZnO showed maximum response to toluene (29-76%) at 400 °C. All the sensing layers were observed to exhibit finite responses to the other three VOCs so, an attempt to classify and quantify the four VOCs accurately was made using support vector machine (SVM) and multiple linear regression (MLR) algorithms. The response and response times of two sensors were observed to be sufficient as inputs to the machine learning algorithms for classifying and quantifying all the four VOCs. The combinations of (CuO-ZnO (1-0.5) & (1-1) and CuO-ZnO (1-1) & (0.5-1) demonstrated the highest classification accuracy of 98.13% with SVM. The combination of CuO-ZnO (1-0.5) & (1-1) demonstrated the best quantification of the four VOCs using MLR.

Changing Face of Inflammatory Activation in Complex Coronary Artery Disease during the COVID-19 Pandemic

INTRODUCTION

The COVID-19 pandemic has changed the immunological status of the population, indicating increased activation. The aim of the study was to compare the degree of inflammatory activation in patients admitted for surgical revascularization in the period before and during the COVID-19 pandemic.

MATERIALS AND METHODS

This retrospective analysis included an analysis of inflammatory activation assessed on the basis of whole blood counts in 533 patients (435 (82%) male and 98 (18%) female) with a median age of 66 (61-71) years who underwent surgical revascularization, including 343 and 190 patients operated on in 2018 and 2022, respectively.

RESULTS

The compared groups were matched by propensity score matching analysis, obtaining 190 patients in each group. Significantly higher values of preoperative monocyte count (p = 0.015), monocyte-to-lymphocyte ratio (p = 0.004) and systemic inflammatory response index (p = 0.022) were found in the during-COVID subgroup. The perioperative and 12-month mortality rates were comparable, with 1% (n = 4) in 2018 vs. 1% (n = 2) in 2022 (p = 0.911), and 5.6 % (n = 11 patients) vs. 7% (n = 13 patients) (p = 0.413), in the pre-COVID and during-COVID subgroups, respectively.

CONCLUSIONS

Simple whole blood analysis in patients with complex coronary artery disease performed before and during the COVID-19 pandemic indicates excessive inflammatory activation. However, the immune variation did not interfere with one-year mortality rate after surgical revascularization.

Efficient prediction of early-stage diabetes using XGBoost classifier with random forest feature selection technique

Diabetes is one of the most common and serious diseases affecting human health. Early diagnosis and treatment are vital to prevent or delay complications related to diabetes. An automated diabetes detection system assists physicians in the early diagnosis of the disease and reduces complications by providing fast and precise results. This study aims to introduce a technique based on a combination of multiple linear regression (MLR), random forest (RF), and XGBoost (XG) to diagnose diabetes from questionnaire data. MLR-RF algorithm is used for feature selection, and XG is used for classification in the proposed system. The dataset is the diabetic hospital data in Sylhet, Bangladesh. It contains 520 instances, including 320 diabetics and 200 control instances. The performance of the classifiers is measured concerning accuracy (ACC), precision (PPV), recall (SEN, sensitivity), F1 score (F1), and the area under the receiver-operating-characteristic curve (AUC). The results show that the proposed system achieves an accuracy of 99.2%, an AUC of 99.3%, and a prediction time of 0.04825 seconds. The feature selection method improves the prediction time, although it does not affect the accuracy of the four compared classifiers. The results of this study are quite reasonable and successful when compared with other studies. The proposed method can be used as an auxiliary tool in diagnosing diabetes.

Application of logistic regression, support vector machine and random forest on the effects of titanium dioxide nanoparticles using macroalgae in treatment of certain risk factors associated with kidney injuries

The use of titanium dioxide (TiO₂) nanoparticles in many biological and technical domains is on the rise. There hasn't been much research on the toxicity of titanium dioxide nanoparticles in biological systems, despite their ubiquitous usage. In the current investigation, samples were exposed to various dosages of TiO₂ nanoparticles for 4 days, 1 month, and 2 months following treatment. ICP-AES was used to dose TiO₂ into the tissues, and the results showed that the kidney had a significant TiO₂ buildup. On the other hand, apoptosis of renal tubular cells is one of the most frequent cellular processes contributing to kidney disease (KD). Nevertheless, the impact of macroalgal seaweed extract on KD remains undetermined. In this work, machine learning (ML) approaches have been applied to develop prediction algorithms for acute kidney injury (AKI) by use of titanium dioxide and macroalgae in hospitalized patients. Fifty patients with (AKI) and 50 patients (non-AKI group) have been admitted and considered. Regarding demographic data, and laboratory test data as input parameters, support vector machine (SVM), and random forest (RF) are utilized to build models of AKI prediction and compared to the predictive performance of logistic regression (LR). Due to its strong antioxidant and anti-inflammatory powers, the current research ruled out the potential of using G. oblongata red macro algae as a source for a variety of products for medicinal uses. Despite a high and fast processing of algorithms, logistic regression showed lower overfitting in comparison to SVM, and Random Forest. The dataset is subjected to algorithms, and the categorization of potential risk variables yields the best results. AKI samples showed significant organ defects than non-AKI ones. Multivariate LR indicated that lymphocyte, and myoglobin (MB) ≥ 1000 ng/ml were independent risk parameters for AKI samples. Also, GCS score (95% CI 1.4-8.3 P = 0.014) were the risk parameters for 60-day mortality in samples with AKI. Also, 90-day mortality in AKI patients was significantly high (P < 0.0001). In compared to the control group, there were no appreciable changes in the kidney/body weight ratio or body weight increases. Total thiol levels in kidney homogenate significantly decreased, and histopathological analysis confirmed these biochemical alterations. According to the results, oral TiO₂ NP treatment may cause kidney damage in experimental samples.

A QSAR study to predict the survival motor neuron promoter activity of candidate diaminoquinazoline derivatives for the potential treatment of spinal muscular atrophy

Spinal Muscular Atrophy is a genetic neuromuscular disease that leads to muscle weakness and atrophy and it is characterized by the loss of α-motor neurons in the spinal cord's anterior horn cells. The disease appears due to low levels of the survival motor neuron protein. There are continuing clinical trials for the treatment of Spinal Muscular Atrophy. Quinazoline-based compounds are promising since they were tested on fibroblasts derived from the patients and found to increase the survival motor neuron protein levels. In this study, using multiple linear regression, we generated robust and valid quantitative structure- activity relationship models to predict the survival motor neuron-2 promoter activity of the new candidate compounds using the experimental survival motor neuron-2 promoter activity values of 2,4-diaminoquinazoline derivatives taken from the literature. The novel compounds designed by combining the pyrido[1,2-α]pyrimidin-4-one moeity of the known drug Risdiplam with that of 2,4 - diaminoquinazoline scaffold were predicted to exhibit strong promoter activities.

Clinical study on the correlation between monocyte-related ratios and calcification of the abdominal aorta in peritoneal dialysis patients

INTRODUCTION

We aimed to investigate the relationship between monocyte/lymphocyte ratio (MLR) and monocyte/high-density lipoprotein ratio (MHR) with abdominal aortic calcification (AAC) in patients on peritoneal dialysis (PD).

METHODS

The time-averaged (TA) of relevant indexes and AAC scores (AACs) of 160 eligible patients were measured.

RESULTS

Patients divided into the new AAC (n = 57) and the other without (n = 82). High TA-MLR (OR = 110.537, p = 0.018) and long duration of dialysis (OR = 1.045, p < 0.001) were independent risk factors of the new AAC. Patients divided into the no AAC (n = 82), the moderate-to-severe AAC (n = 26), and the mild AAC (n = 52). High TA-MLR (OR = 42.649, p = 0.032), high age at starting PD (OR = 1.055, p < 0.001), and long duration of PD (OR = 1.036, p < 0.001) were independent risk factors of AAC severity.

CONCLUSIONS

MLR is an independent risk factor for the occurrence and severity of AAC and its value for the assessment of AAC is better than MHR.

[Study on the prognostic influencing factors of esophageal squamous cell carcinoma and the predictive value of inflammatory reaction indexes on its postoperative recurrence]

Objective: To explore the influence factors of poor prognosis of esophageal squamous cell carcinoma (ESCC) and the predictive value of inflammatory reaction indexes including neutrophils and lymphocytes ratio (NLR), platelet and lymphocyte ratio (PLR), monocyte and lymphocyte ratio (MLR) provision and differentiation degree, infiltration depth, lymph node metastasis number on the postoperative recurrence of ESCC. Methods: A total of 130 patients with ESCC who underwent radical resection from February 2017 to February 2019 in Nanyang Central Hospital were selected and divided into good prognosis group (66 cases) and poor prognosis group (64 cases) according to the prognostic effect. The clinical data and follow-up data were collected. Multivariate logistic regression analysis was used to determine the independent influencing factors of poor prognosis. Spearman correlation analysis was used to determine the correlation between preoperative NLR, PLR and MLR with the degree of differentiation, depth of invasion and number of lymph node metastases. Receiver operating characteristic (ROC) curve analysis was used to evaluate the efficacy of NLR, PLR and MLR in predicting poor prognosis of ESCC. Results: Univariate analysis showed that the degree of differentiation, the degree of invasion and the number of lymph node metastasis were related to the prognoses of patients with ESCC (P<0.05). Multivariate logistic regression analysis showed that the degree of differentiation, depth of invasion and number of lymph node metastases were independent influencing factors for poor prognosis of patients with ESCC, moderate differentiation (OR=2.603, 95% CI: 1.009-6.715) or low differentiation (OR=9.909, 95% CI: 3.097-31.706), infiltrating into fibrous membrane (OR=14.331, 95% CI: 1.333-154.104) or surrounding tissue (OR=23.368, 95% CI: 1.466-372.578), the number of lymph node metastases ≥ 3 (OR=9.225, 95% CI: 1.693-50.263) indicated poor prognosis. Spearman correlation analysis showed that NLR was negatively correlated with the degree of differentiation and the number of lymph node metastases (r=-0.281, P=0.001; r=-0.257, P=0.003), PLR was negatively correlated with the degree of differentiation, depth of invasion and number of lymph node metastasis (r=-0.250, P=0.004; r=0.197, P=0.025; r=-0.194, P=0.027), MLR was positively correlated with the degree of differentiation and the number of lymph node metastasis (r=0.248, P=0.004; r=0.196, P=0.025). ROC curve analysis showed that the areas under the curve of NLR, PLR and MLR in predicting poor prognosis of ESCC were 0.971, 0.925 and 0.834, respectively. The best cut-off value of NLR was 2.87. The sensitivity and specificity of NLR in predicting poor prognosis of ESCC were 90.6% and 87.9%, respectively. The optimal cut-off value of PLR was 141.75. The sensitivity and specificity for predicting poor prognosis of ESCC were 92.2% and 87.9%, respectively. The best cut-off value of MLR was 0.40. The sensitivity and specificity of MLR in predicting poor prognosis of esophageal squamous cell carcinoma were 54.7% and 100.0%, respectively. Conclusions: The degree of differentiation, the degree of invasion and the number of lymph node metastases are closely related to the poor prognosis of patients with esophageal squamous cell carcinoma. NLR, PLR and MLR can provide important information for predicting the poor prognosis of esophageal squamous cell carcinoma.

QSAR Models for the Prediction of Dietary Biomagnification Factor in Fish

Xenobiotics released in the environment can be taken up by aquatic and terrestrial organisms and can accumulate at higher concentrations through the trophic chain. Bioaccumulation is therefore one of the PBT properties that authorities require to assess for the evaluation of the risks that chemicals may pose to humans and the environment. The use of an integrated testing strategy (ITS) and the use of multiple sources of information are strongly encouraged by authorities in order to maximize the information available and reduce testing costs. Moreover, considering the increasing demand for development and the application of new approaches and alternatives to animal testing, the development of in silico cost-effective tools such as QSAR models becomes increasingly important. In this study, a large and curated literature database of fish laboratory-based values of dietary biomagnification factor (BMF) was used to create externally validated QSARs. The quality categories (high, medium, low) available in the database were used to extract reliable data to train and validate the models, and to further address the uncertainty in low-quality data. This procedure was useful for highlighting problematic compounds for which additional experimental effort would be required, such as siloxanes, highly brominated and chlorinated compounds. Two models were suggested as final outputs in this study, one based on good-quality data and the other developed on a larger dataset of consistent Log BMF_L values, which included lower-quality data. The models had similar predictive ability; however, the second model had a larger applicability domain. These QSARs were based on simple MLR equations that could easily be applied for the predictions of dietary BMF_L in fish, and support bioaccumulation assessment procedures at the regulatory level. To ease the application and dissemination of these QSARs, they were included with technical documentation (as QMRF Reports) in the QSAR-ME Profiler software for QSAR predictions available online.

How the Structure of Per- and Polyfluoroalkyl Substances (PFAS) Influences Their Binding Potency to the Peroxisome Proliferator-Activated and Thyroid Hormone Receptors-An In Silico Screening Study

In this study, we investigated PFAS (per- and polyfluoroalkyl substances) binding potencies to nuclear hormone receptors (NHRs): peroxisome proliferator-activated receptors (PPARs) α, β, and γ and thyroid hormone receptors (TRs) α and β. We have simulated the docking scores of 43 perfluoroalkyl compounds and based on these data developed QSAR (Quantitative Structure-Activity Relationship) models for predicting the binding probability to five receptors. In the next step, we implemented the developed QSAR models for the screening approach of a large group of compounds (4464) from the NORMAN Database. The in silico analyses indicated that the probability of PFAS binding to the receptors depends on the chain length, the number of fluorine atoms, and the number of branches in the molecule. According to the findings, the considered PFAS group bind to the PPARα, β, and γ only with low or moderate probability, while in the case of TR α and β it is similar except that those chemicals with longer chains show a moderately high probability of binding.

Predictive role of blood-based indicators in neuromyelitis optica spectrum disorders

Introduction

This study aimed to assess the predictive role of blood markers in neuromyelitis optica spectrum disorders (NMOSD).

Methods

Data from patients with NMOSD, multiple sclerosis (MS), and healthy individuals were retrospectively collected in a 1:1:1 ratio. The expanded disability status scale (EDSS) score was used to assess the severity of the NMOSD upon admission. Receiver operating characteristic (ROC) curve analysis was used to distinguish NMOSD patients from healthy individuals, and active NMOSD from remitting NMOSD patients. Binary logistic regression analysis was used to evaluate risk factors that could be used to predict disease recurrence. Finally, Wilcoxon signed-rank test or matched-sample t-test was used to analyze the differences between the indicators in the remission and active phases in the same NMOSD patient.

Results

Among the 54 NMOSD patients, neutrophil count, neutrophil-to-lymphocyte ratio (NLR), platelet-to-lymphocyte ratio (PLR), and systemic immune-inflammation index (SII) (platelet × NLR) were significantly higher than those of MS patients and healthy individuals and positively correlated with the EDSS score of NMOSD patients at admission. PLR can be used to simultaneously distinguish between NMOSD patients in the active and remission phase. Eleven (20.4%) of the 54 patients had recurrence within 12 months. We found that monocyte-to-lymphocyte ratio (MLR) (AUC = 0.76, cut-off value = 0.34) could effectively predict NMOSD recurrence. Binary logistic regression analysis showed that a higher MLR at first admission was the only risk factor for recurrence (p = 0.027; OR = 1.173; 95% CI = 1.018-1.351). In patients in the relapsing phase, no significant changes in monocyte and lymphocyte count was observed from the first admission, whereas patients in remission had significantly higher levels than when they were first admitted.

Conclusion

High PLR is a characteristic marker of active NMOSD, while high MLR is a risk factor for disease recurrence. These inexpensive indicators should be widely used in the diagnosis, prognosis, and judgment of treatment efficacy in NMOSD.

The SAFFO Study: Sex-Related Prognostic Role and Cut-Off Definition of Monocyte-to-Lymphocyte Ratio (MLR) in Metastatic Colorectal Cancer

Background: Emerging data suggest that gender-related immune system composition affects both immune response and efficacy of immunotherapy in cancer patients (pts). This study aimed to investigate the sex-related prognostic role of MLR in metastatic colorectal cancer (mCRC) pts. Methods: We analyzed a retrospective consecutive cohort of 490 mCRC patients treated from 2009 to 2018 at the Oncology Departments of Aviano and Pordenone (training set) and Udine (validation set), Italy. The prognostic impact of MLR on overall survival (OS) was evaluated with uni- and multivariable Cox regression models. The best cut-off value to predict survival was defined through ROC analyses. Results: Overall, we identified 288 males (59%) and 202 females (41%); 161 patients (33%) had a right-sided, 202 (42%) a left-sided primary, and 122 (25%) a rectal tumor. Interestingly, gender was associated with MLR (p = 0.004) and sidedness (p = 0.006). The obtained cut-off value for MLR in females and males was 0.27 and 0.49, respectively. According to univariate analysis of the training set, MLR (HR 9.07, p ≤ 0.001), MLR > 0.27 in females (HR 1.95, p = 0.003), and MLR > 0.49 in males (HR 2.65, p = 0.010) were associated with poorer OS, which was also confirmed in the validation set. In multivariate analysis, MLR > 0.27 in females (HR 2.77, p = 0.002), MLR > 0.49 in males (HR 5.39, p ≤ 0.001), BRAF mutation (HR 3.38, p ≤ 0.001), and peritoneal metastases (HR 2.50, p = 0.003) were still independently associated with worse OS. Conclusions: Males and females have a different immune response. Our study showed that high MLR, both in males and females, is an unfavorable Independent prognostic factor. Further prospective studies are needed to confirm these data.

Policy-driven variations in oxidation potential and source apportionment of PM2.5 in Wuhan, central China

China has implemented several control measures to mitigate PM_2.5 pollution and improve air quality, such as the Action Plan for the Prevention and Control of Air Pollution (APPCAP). To comprehensively assess the changes in ambient PM_2.5 concentrations and the corresponding health risk with the implementation of APPCAP, this study examined PM_2.5 samples collected in Wuhan in 2012/2013 and 2018 for water-soluble ions, carbonaceous fractions, and elements, respectively. Dithiothreitol (DTT) assay was used to determine the oxidation potential (OP) of PM_2.5. The positive matrix factorization (PMF) model and the multiple linear regression (MLR) model were used to analyze PM_2.5 sources and the contribution of each source to the OP of PM_2.5. The results showed that PM_2.5 concentrations in Wuhan decreased significantly, however, there was little change in the health risk and a significant increase in intrinsic toxicity. DTT_v (the volume-normalized dithiothreitol) showed high correlations (r > 0.5, p < 0.01) with water-soluble organic carbon (WSOC), organic carbon (OC), secondary ions (NO₃^-, SO₄^2-, and NH₄⁺), and elements. Compared to 2012/2013, the contribution of vehicle emissions and secondary aerosol sources to PM_2.5 increased significantly in 2018. Biomass burning sources significantly contribute to DTT_v in the summer and autumn, and secondary aerosol sources significantly contribute to DTT_v in winter. The human health impacts from coal combustion sources remained high, while vehicle emission sources increased. In the context of decreasing PM_2.5 concentrations, the role of vehicle emissions health impacts is increasingly significant due to the large increment in vehicle ownership and high inherent OP. Therefore, targeting vehicle emissions for control is of great importance for human health and needs to be given great attention in future policymaking.

Blood cell ratios in mood and cognitive outcomes following electroconvulsive therapy

Systemic inflammation is commonly reported in depression, with dysregulation of both the innate and adaptive arms of the immune system documented. Obtaining ratios of neutrophils, platelets, and monocytes to counts of lymphocytes (NLR, PLR, MLR, respectively) represents a low-cost and easily reproducible measure of an individual's inflammatory burden that can be calculated effortlessly from routine clinical full white blood cell counts. Electroconvulsive therapy (ECT) remains the most effective acute antidepressant treatment for depression but is often limited by its cognitive side-effects. Here, we examined differences in blood cell ratios in subgroups of depressed patients (unipolar/bipolar, psychotic/non-psychotic, early-onset/late-onset) and ECT-related subgroups (responder/non-responder, remitter/non-remitter). We also explored the relationships between blood cell ratios and depression severity and immediate cognitive outcomes post-ECT. Our results show baseline NLR was raised in patients with psychotic depression. In the entire group of patients, significant negative correlations were noted between the PLR and SII and baseline HAM-D24 score, signifying that lower systemic inflammation is associated with more severe depressive symptoms. Significant positive correlations were noted between various blood cell ratios and mean time to recovery of orientation in the entire group of patients and in depression subgroups, indicating that increased peripheral inflammation is linked to worse cognitive outcomes post-ECT. Overall, our results suggest that assessment of blood cell ratios could be useful for predicting mood changes in patients at risk of developing depressive episodes or relapse following successful treatment or for identifying those at risk for cognitive side-effects following ECT.

A quantitative structural analysis of AR-42 derivatives as HDAC1 inhibitors for the identification of promising structural contributors

Alteration and abnormal epigenetic mechanisms can lead to the aberration of normal biological functions and the occurrence of several diseases. The histone deacetylase (HDAC) family of enzymes is one of the prime regulators of epigenetic functions modifying the histone proteins, and thus, regulating epigenetics directly. HDAC1 is one of those HDACs which have important contributions to cellular epigenetics. The abnormality of HDAC is correlated to the occurrence, progression, and poor prognosis in several disease conditions namely neurodegenerative disorders, cancer cell proliferation, metastasis, chemotherapy resistance, and survival in various cancers. Therefore, the progress of potent and effective HDAC1 inhibitors is one of the prime approaches to combat such diseases. In this study, both regression and classification-based molecular modelling studies were conducted on some AR-42 derivatives as HDAC1 inhibitors to elucidate the crucial structural aspects that are responsible for regulating their biological responses. This study revealed that the molecular polarizability, van der Waals volume, the presence of aromatic rings as well as the higher number of hydrogen bond acceptors might affect prominently their inhibitory activity and might be responsible for proper fitting and interactions at the HDAC1 active site to pertain effective inhibition.

Evaluation of weather parameter-based pre-harvest yield forecast models for wheat crop: a case study in Saurashtra region of Gujarat

Wheat is the important food grain and is cultivated in many Indian states: Punjab, Haryana, Uttar Pradesh, and Madhya Pradesh, which contributes to major crop production in India. In this study, popular statistical approach multiple linear regression (MLR) and time series approaches Time Delay Neural Network (TDNN) and ARIMAX models were envisaged for wheat yield forecast using weather parameters for a case study area, i.e., Junagarh district, western Gujarat region situated at the foot of Mount Girnar. Weather data corresponds to 19 weeks (42nd to 8th Standard Meteorological Week, SMW) during crop growing season was used for prediction of wheat yield using these statistical techniques and were evaluated for their predictive capability. Furthermore, trend analysis among weather parameters and crop yield was also carried out in this study using non-parametric Mann-Kendall test and Sen's slope method. Significant negative correlation was observed between wheat yield and some of the weekly weather variables, viz., maximum temperature (48, 49, 50, 51, 52, and 4th SMW), and total rainfall (50, 51, and 1st SMW) while positive correlation was observed with morning relative humidity (49 and 3rd SMW). Study indicated that forecast error varied from 1.80 to 10.28 in MLR, 0.79 to 7.79 in ARIMAX (2,2,2), - 3.09 to 10.18 in TDNN (4,5) during model training period (1985-2014). The MAPE value shows that the time series data predicted less than 5% of variation, whereas the conventional MLR technique indicated more than 7% variation. Both ARIMAX and TDNN approaches indicated better performance during model training periods, i.e., 1985-2014 and 1985-2015, while former performed well during the forecast periods 1985-2016 and 1985-2017. Overall, the study indicated that the ARIMAX approach can be used consistently for 4 years using the same model.

Carotid Plaque Features and Inflammatory Biomarkers as Predictors of Restenosis and Mortality Following Carotid Endarterectomy

BACKGROUND

Carotid endarterectomy (CEA) is the first-line surgical intervention for cases of severe carotid stenoses. Unfortunately, the restenosis rate is high after CEA. This study aims to demonstrate the predictive role of carotid plaque features and inflammatory biomarkers (monocyte-to-lymphocyte ratio (MLR), neutrophil-to-lymphocyte ratio (NLR), platelet-to-lymphocyte ratio (PLR), systemic inflammatory index (SII), Systemic Inflammation Response Index (SIRI), and Aggregate Index of Systemic Inflammation (AISI)) in carotid restenosis and mortality at 12 months following CEA.

METHODS

The present study was designed as an observational, analytical, retrospective cohort study and included all patients over 18 years of age with a minimum of 70% carotid stenosis and surgical indications for CEA admitted to the Vascular Surgery Clinic, Emergency County Hospital of Targu Mures, Romania between 2018 and 2021.

RESULTS

According to our results, the high pre-operative values of inflammatory biomarkers-MLR (OR: 10.37 and OR: 6.11; p < 0.001), NLR (OR: 34.22 and OR: 37.62; p < 0.001), PLR (OR: 12.02 and OR: 16.06; p < 0.001), SII (OR: 18.11 and OR: 31.70; p < 0.001), SIRI (OR: 16.64 and OR: 9.89; p < 0.001), and AISI (OR: 16.80 and OR: 8.24; p < 0.001)-are strong independent factors predicting the risk of 12-month restenosis and mortality following CEA. Moreover, unstable plaque (OR: 2.83, p < 0.001 and OR: 2.40, p = 0.04) and MI (OR: 3.16, p < 0.001 and OR: 2.83, p = 0.005) were independent predictors of all outcomes. Furthermore, AH (OR: 2.30; p = 0.006), AF (OR: 1.74; p = 0.02), tobacco (OR: 2.25; p < 0.001), obesity (OR: 1.90; p = 0.02), and thrombotic plaques (OR: 2.77; p < 0.001) were all independent predictors of restenosis, but not for mortality in all patients. In contrast, antiplatelet (OR: 0.46; p = 0.004), statin (OR: 0.59; p = 0.04), and ezetimibe (OR:0.45; p = 0.03) therapy were protective factors against restenosis, but not for mortality.

CONCLUSIONS

Our data revealed that higher preoperative inflammatory biomarker values highly predict 12-month restenosis and mortality following CEA. Furthermore, age above 70, unstable plaque, cardiovascular disease, and dyslipidemia were risk factors for all outcomes. Additionally, AH, AF, smoking, and obesity were all independent predictors of restenosis but not of mortality in all patients. Antiplatelet and statin medication, on the other hand, were protective against restenosis but not against mortality.

Inflammatory Biomarkers as Prognostic Factors of Acute Deep Vein Thrombosis Following the Total Knee Arthroplasty

Background and objectives: Deep vein thrombosis (DVT) is one of the most serious post-operative complications in the case of total knee arthroplasty (TKA). This study aims to verify the predictive role of inflammatory biomarkers [monocyte-to-lymphocyte ratio (MLR), neutrophil-to-lymphocyte ratio (NLR), platelets-to-lymphocyte ratio (PLR), systemic inflammatory index (SII), systemic inflammation response index (SIRI), and aggregate index of systemic inflammation (AISI)] in acute DVT following TKA. Materials and methods: The present study was designed as an observational, analytical, retrospective cohort study and included all patients over 18 years of age with surgical indications for TKA, admitted to the Department of Orthopedics, Regina Maria Health Network, Targu Mures, Romania, and the Department of Orthopedics, Humanitas MedLife Hospital, Cluj-Napoca, Romania between January 2017 and July 2022. The primary endpoint was the risk of acute DVT following the TKA, and the secondary endpoint was the length of hospital stay, and the outcomes were stratified for the baseline’s optimal MLR, NLR, PLR, SII, SIRI, and AISI cut-off value. Results: DVT patients were associated with higher age (p = 0.01), higher incidence of cardiac disease [arterial hypertension (p = 0.02), atrial fibrillation (p = 0.01)], malignancy (p = 0.005), as well as risk factors [smoking (p = 0.03) and obesity (p = 0.02)]. Multivariate analysis showed a high baseline value for all hematological ratios: MLR (OR: 11.06; p < 0.001), NLR (OR: 10.15; p < 0.001), PLR (OR: 12.31; p < 0.001), SII (OR: 18.87; p < 0.001), SIRI (OR: 10.86; p < 0.001), and AISI (OR: 14.05; p < 0.001) was an independent predictor of DVT after TKA for all recruited patients. Moreover, age above 70 (OR: 2.96; p = 0.007), AH (OR: 2.93; p = 0.02), AF (OR: 2.71; p = 0.01), malignancy (OR: 3.98; p = 0.002), obesity (OR: 2.34; p = 0.04), and tobacco (OR: 2.30; p = 0.04) were all independent predictors of DVT risk. Conclusions: Higher pre-operative hematological ratios MLR, NLR, PLR, SII, SIRI, and AISI values determined before operations strongly predict acute DVT following TKA. Moreover, age over 70, malignancy, cardiovascular disease, and risk factors such as obesity and tobacco were predictive risk factors for acute DVT.

Systemic Inflammatory Biomarkers and Chest CT Findings as Predictors of Acute Limb Ischemia Risk, Intensive Care Unit Admission, and Mortality in COVID-19 Patients

Background: Numerous tools, including inflammatory biomarkers and lung injury severity scores, have been evaluated as predictors of thromboembolic events and the requirement for intensive therapy in COVID-19 patients. This study aims to verify the predictive role of inflammatory biomarkers [monocyte to lymphocyte ratio (MLR), neutrophil to lymphocyte ratio (NLR), platelet to lymphocyte ratio (PLR), systemic inflammatory index (SII), Systemic Inflammation Response Index (SIRI), and Aggregate Index of Systemic Inflammation (AISI)] and the CT Severity Score in acute limb ischemia (ALI) risk, intensive unit care (ICU) admission, and mortality in COVID-19 patients.; Methods: The present study was designed as an observational, analytical, retrospective cohort study and included all patients older than 18 years of age with a diagnosis of COVID-19 infection, confirmed through real time-polymerase chain reaction (RT-PCR), and admitted to the County Emergency Clinical Hospital of Targu-Mureș, Romania, and Modular Intensive Care Unit of UMFST “George Emil Palade” of Targu Mures, Romania between January 2020 and December 2021. Results: Non-Survivors and “ALI” patients were associated with higher incidence of cardiovascular disease [atrial fibrillation (AF) p = 0.0006 and p = 0.0001; peripheral arterial disease (PAD) p = 0.006 and p < 0.0001], and higher pulmonary parenchyma involvement (p < 0.0001). Multivariate analysis showed a high baseline value for MLR, NLR, PLR, SII, SIRI, AISI, and the CT Severity Score independent predictor of adverse outcomes for all recruited patients (all p < 0.0001). Moreover, the presence of AF and PAD was an independent predictor of ALI risk and mortality. Conclusions: According to our findings, higher MLR, NLR, PLR, SII, SIRI, AISI, and CT Severity Score values at admission strongly predict ALI risk, ICU admission, and mortality. Moreover, patients with AF and PAD had highly predicted ALI risk and mortality but no ICU admission.

Chest CT Severity Score and Systemic Inflammatory Biomarkers as Predictors of the Need for Invasive Mechanical Ventilation and of COVID-19 Patients' Mortality

Background: Numerous tools, including inflammatory biomarkers and lung injury severity scores, have been evaluated as predictors of disease progression and the requirement for intensive therapy in COVID-19 patients. This study aims to verify the predictive role of inflammatory biomarkers [monocyte to lymphocyte ratio (MLR), neutrophil to lymphocyte ratio (NLR), systemic inflammatory index (SII), Systemic Inflammation Response Index (SIRI), Aggregate Index of Systemic Inflammation (AISI), and interleukin-6 (IL-6)] and the total system score (TSS) in the need for invasive mechanical ventilation (IMV) and mortality in COVID-19 patients. Methods: The present study was designed as an observational, analytical, retrospective cohort study and included all patients over 18 years of age with a diagnosis of COVID-19 pneumonia, confirmed through real time-polymerase chain reaction (RT-PCR) and radiological chest CT findings admitted to County Emergency Clinical Hospital of Targu-Mureș, Romania, and Modular Intensive Care Unit of UMFST “George Emil Palade” of Targu Mures, Romania between January 2021 and December 2021. Results: Non-Survivors patients were associated with higher age (p = 0.01), higher incidence of cardiac disease [atrial fibrillation (AF) p = 0.0008; chronic heart failure (CHF) p = 0.01], chronic kidney disease (CKD; p = 0.02), unvaccinated status (p = 0.001), and higher pulmonary parenchyma involvement (p < 0.0001). Multivariate analysis showed a high baseline value for MLR, NLR, SII, SIRI, AISI, IL-6, and TSS independent predictor of adverse outcomes for all recruited patients. Moreover, the presence of AF, CHF, CKD, and dyslipidemia were independent predictors of mortality. Furthermore, AF and dyslipidemia were independent predictors of IMV need. Conclusions: According to our findings, higher MLR, NLR, SII, SIRI, AISI, IL-6, and TSS values at admission strongly predict IMV requirement and mortality. Moreover, patients above 70 with AF, dyslipidemia, and unvaccinated status highly predicted IMV need and fatality. Likewise, CHF and CKD were independent predictors of increased mortality.

Neutrophil-to-Lymphocyte, Monocyte-to-Lymphocyte, Platelet-to-Lymphocyte Ratio and Systemic Immune-Inflammatory Index in Different States of Bipolar Disorder

The neutrophil-to-lymphocyte ratio (NLR), monocyte-to-lymphocyte ratio (MLR), platelet-to-lymphocyte ratio (PLR) and systemic immune-inflammatory (SII) index, which provide a simple, rapid, inexpensive method to measure the level of inflammation, have been examined as potential inflammatory biomarkers of bipolar disorder (BD) in several studies. We conducted a case-control study recruiting 180 BD patients and 407 healthy controls. BD patients who met the inclusion criteria and were hospitalized due to BD at the psychiatry clinic of the University General Hospital of Larisa, Greece, until September 2021 were included in the study. Among them, 111 patients experienced a manic episode and 69 patients experienced a depressive episode. Data including a complete blood count were retrieved from their first admission to the hospital. Bipolar patients had a higher NLR, MLR and SII index compared to healthy controls when they were experiencing a manic episode (p < 0.001) and a depressive episode (p < 0.001). MLR was increased with large effect size only in patients expressing manic episodes. Neutrophils and NLR had the highest area under the curve with a cutoff of 4.38 and 2.15 in the ROC curve, respectively. Gender-related differences were mainly observed in the SII index, with males who were expressing manic episodes and females expressing depressive episodes having an increased index compared to healthy controls. The NLR, MLR and SII index were significantly higher in patients with BD than in healthy controls, which implies a higher grade of inflammation in BD patients.

The Predictive Value of NLR, MLR, and PLR in the Outcome of End-Stage Kidney Disease Patients

Background: Chronic kidney disease (CKD) is a global public health problem with a high mortality rate and a rapid progression to end-stage kidney disease (ESKD). Recently, the role of inflammation and the correlation between inflammatory markers and CKD progression have been studied. This study aimed to analyze the predictive value of the neutrophil−lymphocyte ratio (NLR), monocyte-to-lymphocyte ratio (MLR), and platelet-to-lymphocyte ratio (PLR) in assessing the outcome of ESKD patients. Methods: A retrospective study which included all patients admitted in the Department of Nephrology of the County Emergency Clinical Hospital, Târgu-Mureș, Romania, between January 2016 and December 2019, diagnosed with ESKD. Results: Mortality at 30 days was clearly higher in the case of the patients in the high-NLR groups (40.12% vs. 1.97%; p < 0.0001), high-MLR (32.35% vs. 4.81%; p < 0.0001), and respectively high-PLR (25.54% vs. 7.94%; p < 0.0001). There was also a significant increase in the number of hospital days and the average number of dialysis sessions in patients with high-NLR (p < 0.0001), high-MLR (p < 0.0001), and high-PLR (p < 0.0001). The multivariate analysis showed that a high baseline value for NLR (p < 0.0001), MLR (p < 0.0001), and PLR (p < 0.0001) was an independent predictor of 30-day mortality for all recruited patients. Conclusions: Our findings established that NLR, MLR, and PLR determined at hospital admission had a strong predictive capacity of all-cause 30-day mortality in ESKD patients who required RRT for at least 6 months. Elevated values of the ratios were also associated with longer hospital stays and more dialysis sessions per patient.

A MLR-Based Approach to Analyze Regulators of T Lymphocyte Activation In Vivo

Depending on the context, robust and durable T lymphocyte activation is either desirable, as in the case of anti-tumor responses, or unwanted, in cases of autoimmunity when chronic stimulation leads to self-tissue damage. Therefore, reliable in vivo models are of great importance to identify and validate regulatory pathways of T lymphocyte activation. Here, we describe an in vivo mixed-lymphocyte-reaction (MLR) approach, which is based on the so-called parent-into-F1 (P → F1) mouse model in combination with the congenic marker CD45.1/2 and cell proliferation dye-labeling. This setup allows us to track adoptively transferred allogenic CD4⁺ and CD8⁺ T lymphocytes and analyze their phenotype as well as the proliferation by flow cytometry in the blood and spleen. We could show hypo-reactive responses of T lymphocytes isolated from knockout mice with a known defect in T lymphocyte activation. Thus, this MLR-based in vivo model provides the opportunity to analyze positive regulators of T cell responses under physiological conditions of polyclonal T lymphocyte activation in vivo.

The MLR, NLR, PLR and D-dimer are associated with clinical outcome in lung cancer patients treated with surgery

OBJECTIVE

The study objective was to investigate the use of peripheral blood biomarkers as predictors of patient survival. The aim of this study was to identify the baseline peripheral blood biomarkers associated with clinical outcome in patients with early lung cancer (stage I-II) treated with surgery.

METHODS

We included and analysed data from 376 patients with early-stage lung cancer who underwent a standard lobectomy. Univariate and multivariate Cox regression analyses were performed on all patients to assess the relationships between progression-free survival (PFS) and overall survival (OS) and the peripheral blood biomarker metrics measured before surgical treatment. The peripheral blood parameters included monocyte-to-lymphocyte ratio (MLR), neutrophil-to-lymphocyte ratio (NLR), platelet-to-lymphocyte ratio (PLR) and dimeric fibrin fragment D (D-dimer).

RESULTS

After univariate Cox regression analysis, low MLR, low NLR, low PLR and low D-dimer values were significantly associated with both better OS and PFS (all p < 0.05). In multivariate Cox regression analysis, a low MLR was significantly and independently associated with both better overall survival and better progression-free survival (both p <0.05). A low D-dimer level was significantly and independently associated with better overall survival (p <0.05). Furthermore, the categorization of patients according to the number of factors with favourable results revealed that those without favourable results had significantly worse outcomes than that of those patients with at least one.

CONCLUSION

A baseline signature of low MLR, low NLR, low PLR, and low D-dimer values was associated with a better survival outcome for patients treated with surgery. Patients with more favourable results had better survival outcomes.

Mechanical Properties, Crack Width, and Propagation of Waste Ceramic Concrete Subjected to Elevated Temperatures: A Comprehensive Study

Waste ceramic concrete (WOC) made from waste ceramic floor tiles has several economic and environmental benefits. Fire is one of the most common disasters in buildings, and WOC is a brittle construction material; therefore, the mechanical properties of WOC structures under high temperatures should be considered. According to previous studies, hybrid fiber can further reduce damage to concrete under high temperatures. Meanwhile, crack width and propagation are among the key characteristics of concrete materials that need to be considered, but few studies have focused on their behavior when subjected to elevated temperatures. The new concrete materials proposed by the authors are WOC and WOC-Hybrid. WOC was prepared with Natural Coarse Aggregates (NCA), Natural Fine Aggregate (NFA), Ordinary Portland Cement (OPC 43 grade), and ceramic waste tiles with 20% replacements for coarse aggregates, 10% replacements for fine aggregates, and 10% replacement for cement. In contrast, WOC-Hybrid was prepared with the addition of hybrid fiber (1% crimped steel fiber and 1% polyvinyl alcohol fiber) in WOC. The specimens were exposed to temperatures of 100–300 °C, and then the specimens were tested for tensile and compressive strength. The present study aims to find a new method to improve concrete resistance to elevated temperatures at the lowest costs by experimental and computational analysis via machine learning models. The application of machine learning models such as artificial neural networks (ANN) and multiple linear regression (MLR) was employed in this study to predict the compressive and tensile strength of concrete. The linear coefficient correlation (R²) and mean square error (MSE) were evaluated to investigate the performance of the models. Based on the experimental analysis, the results show that the effect of hybrid fiber on the crack width and propagation is greater than that on the crack width and propagation of WOC and PC after exposure to high temperatures. However, the enhanced effect of hybrid fiber on the mechanical properties, rack width, and propagation decreases after subjecting it to a high-temperature treatment, owing to the melting and ignition of hybrid fibers at high temperatures. Regarding the computational analysis, it was found that the developed MLR model shows higher efficiency than ANN in predicting the compressive and tensile strength of PC, WOC, and WOC-Hybrid concrete.

Multi-object optimization of Navy-blue anodic oxidation via response surface models assisted with statistical and machine learning techniques

This study aims to model, analyze, and compare the electrochemical removal of Navy-blue dye (NB, %) and subsequent energy consumption (EC, Wh) using the integrated response surface modelling and optimization approaches. The Box-Behnken experimental design was exercised using current density, electrolyte concentration, pH and oxidation time as inputs, while NB removal and EC were recorded as responses for the implementation and analysis of multiple linear regression, support vector regression and artificial neural network models. The dual-response optimization using genetic algorithm generated multi-Pareto solutions for maximized NB removal at minimum energy cost, which were further ranked by employing the desirability function approach. The optimal parametric solution having total desirability of 0.804 is found when pH, current density, Na₂SO₄ concentration and electrolysis time were 6.4, 11.89 mA cm^-2, 0.055 M and 21.5 min, respectively. At these conditions, NB degradation and EC were 83.23% and 3.64 Wh, respectively. Sensitivity analyses revealed the influential patterns of variables on simultaneous optimization of NB removal and EC to be current density followed by treatment time and finally supporting electrolyte concentration. Statistical metrics of modeling and validation confirmed the accuracy of artificial neural network model followed by support vector regression and multiple linear regression anlaysis. The results revealed that statistical and computational modeling is an effective approach for the optimization of process variables of an electrochemical degradation process.

Prediction of irrigation groundwater quality parameters using ANN, LSTM, and MLR models

Forecasting the irrigation groundwater parameters helps plan irrigation water and crop, and it is commonly expensive because it needs various parameters, mainly in developing nations. Therefore, the present research's core objective is to create accurate and reliable machine learning models for irrigation parameters. To accomplish this determination, three machine learning (ML) models, viz. long short-term memory (LSTM), multi-linear regression (MLR), and artificial neural network (ANN), have been trained. It is validated with mean squared error (MSE) and correlation coefficients (r), root mean square error (RMSE), and mean absolute error (MAE). These machine learning models have been used and applied for predicating the six irrigation water quality parameters such as sodium absorption ratio (SAR), percentage of sodium (%Na), residual sodium carbonate (RSC), magnesium hazard (MH), Permeability Index (PI), and Kelly ratio (KR). Therefore, the two scenario performances of ANN, LSTM, and MLR have been developed for each model to predict irrigation water quality parameters. The first and second scenario performance was created based on all and second reduction input variables. The ANN, LSTM, and MLR models have discovered that excluding for ANN and MLR models shows high accuracy in first and second scenario models, respectively. These model's accuracy was checked based on the mean squared error (MSE), correlation coefficients (r), and root mean square error (RMSE) for training and testing processes serially. The RSC values are highly accurate predicated values using ANN and MLR models. As a result, machine learning models may improve irrigation water quality parameters, and such types of results are essential to farmers and crop planning in various irrigation processes.

Impact of different sources on the oxidative potential of ambient particulate matter PM10 in Riyadh, Saudi Arabia: A focus on dust emissions

In this study, we employed Principal Component Analysis (PCA) and Multi-Linear Regression (MLR) to identify the most significant sources contributing to the toxicity of PM10 in the city center of Riyadh. PM10 samples were collected using a medium-volume air sampler during cool (December 2019-March 2020) and warm (May 2020-August 2020) seasons, including dust and non-dust events. The collected filters were analyzed for their chemical components (i.e., water-soluble ions, metals, and trace elements) as well as oxidative potential and elemental and organic carbon (EC/OC) contents. Our measurements revealed comparable extrinsic oxidative potential (P-value = 0.30) during the warm (1.2 ± 0.1 nmol/min-m3) and cool (1.1 ± 0.1 nmol/min-m3) periods. Moreover, we observed higher extrinsic oxidative potential of PM10 samples collected during dust events (~30% increase) compared to non-dust samples. Our PCA-MLR analysis identified soil and resuspended dust, secondary aerosol (SA), local industrial activities and petroleum refineries, and traffic emissions as the four sources contributing to the ambient PM10 oxidative potential in central Riyadh. Soil and resuspended dust were the major source contributing to the oxidative potential of ambient PM10, accounting for 31% of the total oxidative potential. Secondary aerosols (SA) were the next important source of PM10 toxicity in the area as they contributed to about 20% of the PM10 oxidative potential. Results of this study revealed the major role of soil and resuspended road dust on PM10 toxicity and can be helpful in adopting targeted air quality policies to reduce the population exposure to PM10.

Applying comparative molecular modelling techniques on diverse hydroxamate-based HDAC2 inhibitors: an attempt to identify promising structural features for potent HDAC2 inhibition

Histone deacetylase 2 (HDAC2) has been implicated in a variety of cardiovascular and neurodegenerative disorders as well as in cancers. Thus, HDAC2 has become an exclusive target for anticancer drug development. Therefore, the development of newer HDAC2 inhibitors in disease conditions is a prime goal to restrain such a scenario. Although a handful of HDAC inhibitors was accepted for the treatment of HDAC-related disease conditions, the non-selective nature of these entities is one of the major setbacks in the treatment of specific HDAC isoform-related pathophysiology. In this framework, the analyses of pre-existing molecules are essential to identify the important structural features that can fulfil the requirements for the cap and linker moieties to obtain potent and effective HDAC2 inhibition. Thus, in this study, the implementation of a combined comparative 2D and 3D molecular modelling techniques was done on a group of 92 diverse hydroxamate derivatives having a wide range of HDAC2 inhibitory potency. Besides other crucial features, this study upheld the importance of groups like triazole and benzyl moieties along with the molecular fields that are crucial for regulating HDAC2 inhibition. The outcomes of this study may be employed for the designing of HDAC2 inhibitors in future.

Azole Compounds as Inhibitors of Candida albicans: QSAR Modelling

Candida albicans is a pathogenic opportunistic yeast found in the human gut flora. It may also live outside of the human body, causing diseases ranging from minor to deadly. Candida albicans begins as a budding yeast that can become hyphae in response to a variety of environmental or biological triggers. The hyphae form is responsible for the development of multidrug resistant biofilms, despite the fact that both forms have been associated to virulence Here, we have proposed a linear and SPA-linear quantitative structure activity relationship (QSAR) modeling and prediction of Candida albicans inhibitors. A data set that consisted of 60 derivatives of benzoxazoles, benzimidazoles, oxazolo (4, 5-b) pyridines have been used. In this study, that after applying the leverage analysis method to detect outliers' molecules, the total number of these compounds reached 55. SPA-MLR model shows superiority over the multiple linear regressions (MLR) by accounting 90% of the Q ² of anti-fungus derivatives 'activity. This paper focuses on investigating the role of SPA-MLR in developing model. The accuracy of SPA-MLR model was illustrated using leave-one-out (LOO). The mean effect of descriptors and sensitivity analysis show that RDF090u is the most important parameter affecting the as behavior of the inhibitors of Candida albicans.

Pretreatment neutrophil-to-lymphocyte/monocyte-to-lymphocyte ratio as prognostic biomarkers in glioma patients

OBJECTIVES

To evaluate the ability for pre-treatment NLR and MLR to predict overall survival (OS) and modified Rankin Scale (mRS) and to explore their relationship with clinicopathological parameters.

METHODS

Retrospective analysis of pretreatment NLR and MLR from 64 glioma patients.

RESULTS

Higher pretreatment NLR (>4.7) predicted higher mean admission mRS (p < 0.001) and 6-month mRS (p = 0.02). Higher pretreatment MLR (>0.35) was a risk factor for poorer OS in glioma patients (p = 0.024). Higher pretreatment NLR was significantly associated with larger tumor diameter (p = 0.02).

CONCLUSION

NLR and MLR can serve as prognostic markers to predict functional outcomes and OS in glioma patients.

QSAR Studies and Structure Property/Activity Relationships Applied in Pyrazine Derivatives as Antiproliferative Agents Against the BGC823

Electronic structures, the effect of the substitution, structure physicochemical property/activity relationships and drug-likeness applied in pyrazine derivatives, have been studied at ab initio (HF, MP2) and B3LYP/DFT (density functional theory) levels. In the paper, the calculated values, i.e., NBO (natural bond orbitals) charges, bond lengths, dipole moments, electron affinities, heats of formation and quantitative structure-activity relationships (QSAR) properties are presented. For the QSAR studies, we used multiple linear regression (MLR) and artificial neural network (ANN) statistical modeling. The results show a high correlation between experimental and predicted activity values, indicating the validation and the good quality of the derived QSAR models. In addition, statistical analysis reveals that the ANN technique with (9-4-1) architecture is more significant than the MLR model. The virtual screening based on the molecular similarity method and applicability domain of QSAR allowed the discovery of novel anti-proliferative activity candidates with improved activity.

Albumin-Bilirubin (ALBI) and Monocyte to Lymphocyte Ratio (MLR)-Based Nomogram Model to Predict Tumor Recurrence of AFP-Negative Hepatocellular Carcinoma

Purpose

In this study, we aimed to develop a novel liver function and inflammatory markers-based nomogram to predict recurrence-free survival (RFS) for AFP-negative (<20 ng/mL) HCC patients after curative resection.

Patients and Methods

A total of 166 pathologically confirmed AFP-negative HCC patients were included at the Ningbo Medical Center Lihuili Hospital. A LASSO regression analysis was used for data dimensionality reduction and element selection. Univariate and multivariate Cox regression analyses were performed to identify the independent risk factors relevant to RFS. Finally, clinical nomogram prediction model for RFS of HCC was established. Nomogram performance was assessed via internal validation and calibration curve statistics. Receiver operating characteristic (ROC) and decision curve analysis (DCA) curve were used to validate the performance and clinical utility of the nomogram.

Results

Multivariate Cox regression analysis indicated that ALBI grade (hazard ratio, [HR] = 2.624, 95% confidence interval [CI]: 1.391-4.949, P = 0.003), INR (HR = 2.605, 95% CI: 1.061-6.396, P = 0.037), MLR (HR = 1.769, 95% CI: 1.073-2.915, P = 0.025) and MVI (HR = 4.726, 95% CI: 2.365-9.444, P < 0.001) were independent prognostic factors of RFS. Nomogram with independent factors was established and achieved a better concordance index of 0.753 (95% CI: 0.672-0.834) for predicting RFS. The ROC found that the area under curve (AUC) was consistent with the C-index and the sensitivity was 85.4%. The risk score calculated by nomogram could divide AFP-negative HCC patients into high-, moderate- and low-risk groups (P < 0.05). DCA analysis revealed that the nomogram could augment net benefits and exhibited a wider range of threshold probabilities by the risk stratification than the AJCC T and BCLC stage in the prediction of AFP-negative HCC recurrence.

Conclusion

The ALBI grade- and MLR-based nomogram prognostic model for RFS showed high predictive accuracy in AFP-negative HCC patients after surgical resection.

Dynamic Changes of the Neutrophil-to-Lymphocyte Ratio, Systemic Inflammation Index, and Derived Neutrophil-to-Lymphocyte Ratio Independently Predict Invasive Mechanical Ventilation Need and Death in Critically Ill COVID-19 Patients

BACKGROUND

Hematological indices can predict disease severity, progression, and death in patients with coronavirus disease-19 (COVID-19).

OBJECTIVES

To study the predictive value of the dynamic changes (first 48 h after ICU admission) of the following ratios: neutrophil-to-lymphocyte (NLR), platelet-to-lymphocyte (PLR), monocyte-to-lymphocyte (MLR), systemic inflammation index (SII), and derived neutrophil-to-lymphocyte (dNLR) for invasive mechanical ventilation (IMV) need and death in critically ill COVID-19 patients.

METHODS

Observational, retrospective, and multicentric analysis on 272 patients with severe or critical COVID-19 from two tertiary centers. Hematological indices were adjusted for confounders through multivariate analysis using Cox regression.

RESULTS

Patients comprised 186 males and 86 females with no difference across groups (p > 0.05). ΔNLR > 2 had the best independent predictive value for IMV need (HR = 5.05 (95% CI, 3.06-8.33, p < 0.0001)), followed by ΔSII > 340 (HR = 3.56, 95% CI 2.21-5.74, p < 0.0001) and ΔdNLR > 1 (HR = 2.61, 95% CI 1.7-4.01, p < 0.0001). Death was also best predicted by an NLR > 11 (HR = 2.25, 95% CI: 1.31-3.86, p = 0.003) followed by dNLR > 6.93 (HR = 1.89, 95% CI: 1.2-2.98, p = 0.005) and SII > 3700 (HR = 1.68, 95% CI: 1.13-2.49, p = 0.01).

CONCLUSIONS

Dynamic changes of NLR, SII, and dNLR independently predict IMV need and death in critically ill COVID-19 patients.

The impacts of the built environment on the incidence rate of COVID-19: A case study of King County, Washington

With COVID-19 prevalent worldwide, current studies have focused on the factors influencing the epidemic. In particular, the built environment deserves immediate attention to produce place-specific strategies to prevent the further spread of coronavirus. This research assessed the impact of the built environment on the incidence rate in King County, US and explored methods of researching infectious diseases in urban areas. Using principal component analysis and the Pearson correlation coefficient to process the data, we built multiple linear regression and geographically weighted regression models at the ZIP code scale. Results indicated that although socioeconomic indicators were the primary factors influencing COVID-19, the built environment affected COVID-19 cases from different aspects. Built environment density was positively associated with incidence rates. Specifically, increased open space was conducive to reducing incidence rates. Within each community, overcrowded households led to an increase in incidence rates. This study confirmed previous research into the importance of socioeconomic variables and extended the discussion on spatial and temporal variation in the impacts of urban density on the spread of COVID, effectively guiding sustainable urban development.