[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.