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.

Prognostic Utility of Platelet-Lymphocyte Ratio, Neutrophil-Lymphocyte Ratio and Monocyte-Lymphocyte Ratio in Head and Neck Cancers: A Detailed PRISMA Compliant Systematic Review and Meta-Analysis

Simple Summary

Inflammation plays a major role in cancer development and progression and has the potential to be used as a prognostic marker in cancer. Previous studies have attempted to evaluate PLR, NLR and MLR as indicators of inflammation/prognostic markers in cancer, but there is no common consensus on its application in clinical practice. The aim of this systematic review and meta-analysis (a) assess the prognostic efficacy of all three prognostic markers in comparison to each other and, (b) investigate the prognostic potential of these three markers in HNC. The study followed PRISMA guidelines, with literature being collated from multiple bibliographic databases. Preliminary and secondary screening were carried out using stringent inclusion/exclusion criteria.

Abstract

Inflammation plays a major role in cancer development and progression and has the potential to be used as a prognostic marker in cancer. Previous studies have attempted to evaluate Platelet-to-lymphocyte ratio (PLR), neutrophil–lymphocyte ratio (NLR) or monocyte–lymphocyte ratio (MLR) as indicators of inflammation/prognostic markers in cancer, but there is no common consensus on their application in clinical practice. The aim of this systematic review and meta-analysis is to (a) assess the prognostic efficacy of all three prognostic markers in comparison to each other and (b) investigate the prognostic potential of these three markers in HNC. The study followed PRISMA guidelines, with the literature being collated from multiple bibliographic databases. Preliminary and secondary screening were carried out using stringent inclusion/exclusion criteria. Meta-analysis was carried out on selected studies using CMA software and HR as the pooled effect size metric. A total of 49 studies were included in the study. The pooled HR values of PLR, NLR and MLR indicated that they were significantly correlated with poorer OS. The pooled effect estimates for PLR, NLR and MLR were 1.461 (95% CI 1.329–1.674), 1.639 (95% CI 1.429–1.880) and 1.002 (95% CI 0.720–1.396), respectively. Significant between-study heterogeneity was observed in the meta-analysis of all three. The results of this study suggest that PLR, NLR and MLR ratios can be powerful prognostic markers in head and neck cancers that can guide treatment. Further evidence from large-scale clinical studies on patient cohorts are required before they can be incorporated as a part of the clinical method. PROSPERO Registration ID: CRD42019121008

Blood Cell Count Indexes of Systemic Inflammation in Carotid Artery Disease: Current Evidence and Future Perspectives

Carotid artery disease is commonly encountered in clinical practice and accounts for approximately 30% of ischemic strokes in the general population. Numerous biomarkers have been investigated as predictors of the onset and progression of carotid disease, the occurrence of cerebrovascular complications, and overall prognosis. Among them, blood cell count (BCC) indexes of systemic inflammation might be particularly useful, from a pathophysiological and clinical point of view, given the inflammatory nature of the atherosclerotic process. The aim of this review is to discuss the available evidence regarding the role of common BCC indexes, such as the neutrophil to lymphocyte ratio (NLR), monocyte to lymphocyte ratio (MLR), platelet to lymphocyte ratio (PLR), mean platelet volume (MPV), platelet distribution width (PDW), and red cell distribution width (RDW), in the diagnosis and risk stratification of carotid artery disease, and their potential clinical applications.

The Increasing Surface Ozone and Tropospheric Ozone in Antarctica and Their Possible Drivers

A comprehensive analysis of the temporal evolution of tropospheric ozone in Antarctica using more than 25 years of surface ozone and ozonesonde measurements reveals significant changes in tropospheric ozone there. It shows a positive trend in ozone at the surface and lower and mid-troposphere, but a negative trend in the upper troposphere. We also find significant links between different climate modes and tropospheric ozone in Antarctica and observe that changes in residual overturning circulation, the strength of the polar vortex, and stratosphere-troposphere exchange make noticeable variability in tropospheric ozone. Therefore, this study alerts of increasing ozone concentration in Antarctica, which would have a profound impact on the future climate of the region as tropospheric ozone has warming feedback to the Earth's climate.

Tumor Necrosis with Adjunction of Preoperative Monocyte-to-Lymphocyte Ratio as a New Risk Stratification Marker Can Independently Predict Poor Outcomes in Upper Tract Urothelial Carcinoma

OBJECTIVES

This study aimed at investigating the prognostic impact of tumor necrosis and preoperative monocyte-to-lymphocyte ratio (MLR) in patients treated with radical nephroureterectomy (RNU) for upper tract urothelial carcinoma (UTUC).

METHODS

A total of 521 patients with UTUC treated with RNU from January 2008 to June 2019 at our institution were enrolled. Histological tumor necrosis was defined as the presence of microscopic coagulative necrosis. The optimal value of MLR was determined as 0.4 by receiver operating characteristic (ROC) analysis based on cancer-specific mortality. The Kaplan-Meier method with log-rank test and Cox proportional hazards regression models were performed to evaluate the impact of tumor necrosis and MLR on overall (OS), cancer-specific (CSS), and recurrence-free survival (RFS). Furthermore, ROC analysis was used to estimate the predictive ability of potential prognostic factors for oncological outcomes.

RESULTS

Tumor necrosis was present in 106 patients (20%), which was significantly associated with tumor location, high pathological tumor stage, lymph node metastasis, high tumor grade, lymphovascular invasion, tumor size, and increased monocyte counts. On multivariate analysis, the combination of tumor necrosis and preoperative MLR was an independent prognosticator of OS, CSS, and RFS (all p < 0.05). Moreover, ROC analyses revealed the predictive accuracy of a combination of tumor necrosis and preoperative MLR for OS, CSS, and RFS with the area under the ROC curve of 0.745, 0.810, and 0.782, respectively (all p < 0.001).

CONCLUSIONS

The combination of tumor necrosis and preoperative MLR can be used as an independent prognosticator in patients with UTUC after RNU. The identification of this combination could help physicians to recognize high-risk patients with unfavorable outcomes and devise more appropriate postoperative treatment plans.

Cytoreductive Nephrectomy Promoted Abscopal Effect of Camrelizumab Combined With Radiotherapy for Metastatic Renal Cell Carcinoma: A Case Report and Review of the Literature

There is little evidence around Camrelizumab combined with cytoreductive nephrectomy (CN) and radiotherapy (RT) as a treatment option for metastatic renal cell carcinoma (mRCC). The influence of CN on immune responses and the abscopal effect are not well understood. In this paper, we report a case of anti-programmed cell death-1 (PD-1) treated with combined RT once CN reduced the primary tumor burden (TB). This patient also encountered an increased response to targeted radiotherapy after immune resistance. We also observed a macrophage-to-lymphocyte ratio (MLR) peak, which may be correlated with subsequent pseudoprogression after thoracic radiotherapy. Consequently, even with the disease, this patient has remained stable. This peculiar instance suggests there is a need to investigate the underlying mechanisms of CN in promoting the abscopal effect during immunotherapy when combined with RT. It also suggests that there is a need for further investigation into the role of RT in overcoming immune resistance, and the value of MLR in predicting pseudoprogression. We hypothesize that a heavy tumor burden might suppress the abscopal effect, thereby ensuring that CN promotes it. However, radiotherapy may overcome immune resistance during oligoprogression.

Predictive Value of Routine Blood Test in patients with Early Esophageal Cancer: A Matched Case-Control Study

Aims: The present study was to evaluate the diagnostic value of routine blood test as potential inflammatory markers in early esophageal cancer (EEC) patients.

Methods: A matched case-control study was conducted by recruiting 314 patients who were pathologically diagnosed with EEC and then underwent Endoscopic Submucosal Dissection (ESD) from July 2015 to July 2019 in First Affiliated Hospital of Nanjing Medical University. Each EEC patient was matched against one healthy control on the criteria of gender, and age (±2 years). Additionally, a total of 40 subjects (20 cases and 20 controls) were also included in the validation set. Statistical analysis of selected hematological parameters was performed between the two groups. The correlation between preoperative blood indexes and clinicopathological characteristics after ESD in EEC patients were further assessed.

Results: Mono-factor analysis showed that the index of monocyte (p<0.001), MCV (p=0.018), MCH (p=0.01), MPV (p=0.022), PT (p=0.003), PT-INR (p=0.003), PDW (p<0.001) and MLR (p<0.001) were statistically significant in EEC patients when compared with those in healthy controls. Multivariate logistic regression analysis further identified that PDW and MLR was independently associated with the risk of early esophageal cancer (both p<0.001). The higher level of NLR (P=0.007) and MLR (P=0.015) were statistically significant with submucosal invasion in EEC patients and the level of MLR were significantly associated with larger tumor size (P=0.030). The results of the validation group were in consistence with the primary group.

Conclusions: Hematological parameters of MLR and PDW can be used as an adjuvant tool for the diagnosis of EEC. Moreover, the value of MLR can reflect the invasion depth index.

Computer Assisted Models for Blood Brain Barrier Permeation of 1, 5-Benzodiazepines

AIM

To generate and validate predictive models for blood-brain permeation (BBB) of CNS molecules using the QSPR approach.

BACKGROUND

Prediction of molecules crossing BBB remains a challenge in drug delivery. Predictive models are designed for the evaluation of a set of preclinical drugs which may serve as alternatives for determining BBB permeation by experimentation.

OBJECTIVE

The objective of the present study was to generate QSPR models for the permeation of CNS molecules across BBB and its validation using existing in-house leads.

METHODS

The present study envisaged the determination of the set of molecular descriptors which are considered significant correlative factors for BBB permeation property. Quantitative Structure- Property Relationship (QSPR) approach was followed to describe the correlation between identified descriptors for 45 molecules and highest, moderate and least BBB permeation data. The molecular descriptors were selected based on drug-likeness, hydrophilicity, hydrophobicity, polar surface area, etc. of molecules that served the highest correlation with BBB permeation. The experimental data in terms of log BB were collected from available literature, subjected to 2D-QSPR model generation using a regression analysis method like Multiple Linear Regression (MLR).

RESULTS

The best QSPR model was Model 3, which exhibited regression coefficient as R²= 0.89, F = 36; Q²= 0.7805 and properties such as polar surface area, hydrophobic hydrophilic distance, electronegativity, etc., which were considered key parameters in the determination of the BBB permeability. The developed QSPR models were validated with in-house 1,5-benzodiazepines molecules and correlation studies were conducted between experimental and predicted BBB permeability.

CONCLUSION

The QSPR model 3 showed predictive results that were in good agreements with experimental results for blood-brain permeation. Thus, this model was found to be satisfactory in achieving a good correlation between selected descriptors and BBB permeation for benzodiazepines and tricyclic compounds.

The Pan-Immune-Inflammation-Value Predicts the Survival of Patients with Human Epidermal Growth Factor Receptor 2 (HER2)-Positive Advanced Breast Cancer Treated with First-Line Taxane-Trastuzumab-Pertuzumab

Simple Summary

Although taxane-trastuzumab-pertuzumab combinations in the first-line treatment setting significantly improved clinical outcomes in patients with Human Epidermal growth factor Receptor 2 positive (HER2+) advanced breast cancer (aBC), their clinical efficacy is highly heterogeneous, and reliable biomarkers of benefit from this treatment are lacking. Different peripheral blood parameters have emerged as prognostic biomarkers in breast cancer, but their predictive role in HER2+ BC patients receiving dual anti-HER2 blockade remains unclear. In this work, we identified the Pan-Immune-Inflammatory Value (PIV), a recently defined parameter, taking into account peripheral blood neutrophil, platelet, monocyte and lymphocyte counts, as an independent predictor of worse OS in patients with HER2+ aBC receiving first line trastuzumab-pertuzumab biochemotherapy. The PIV outperforms other well-known peripheral blood parameters, thus potentially representing a new tool to improve the prognostic stratification of HER2+ aBC patients in a first-line treatment setting.

Abstract

Different peripheral blood parameters have emerged as prognostic biomarkers in breast cancer (BC), but their predictive role in Human Epidermal growth factor Receptor 2 positive (HER2+) advanced BC (aBC) patients receiving dual anti-HER2 blockade remains unclear. We evaluated the impact of the Pan-Immune-Inflammatory Value (PIV), defined as the product of peripheral blood neutrophil, platelet, and monocyte counts divided by lymphocyte counts, on the prognosis of HER2+ aBC patients treated with first line trastuzumab-pertuzumab-based biochemotherapy. We also evaluated the association between the neutrophil-to-lymphocyte ratio (NLR), the platelet-to-lymphocyte ratio (PLR), and the monocyte to lymphocyte ratio (MLR) and clinical outcomes. Cox regression models were used to estimate the impact of these variables, as well as of other clinically relevant covariates, on patient survival. We included 57 HER2+ aBC patients treated with taxane-trastuzumab-pertuzumab in our Institution. High baseline MLR, PLR, and PIV were similarly predictive of worse PFS at univariate analysis, but only high PIV was associated with a trend toward worse PFS at multivariable analysis. Regarding OS, both high PIV and MLR were associated with significantly worse patient survival at univariate analysis, but only the PIV was statistically significantly associated with worse overall survival at multivariable analysis (HR 7.96; 95% CI: 2.18–29.09). Our study reveals the PIV as a new and potent predictor of OS in HER2+ aBC patients treated with first line trastuzumab-pertuzumab-containing biochemotherapy. Prospective studies are needed to validate this new prognostic parameter in HER2+ aBC.

Association of Hematologic biomarkers and their combinations with disease severity and mortality in COVID-19- an Indian perspective

BACKGROUND

COVID-19 is a systemic viral infection with a significant impact on the hematopoietic system, hemostasis as well as immune system. It would be of utmost importance to explore if the most routinely used tests could serve as an aid in determining patient's clinical status or predicting severity of the disease.

METHODS

A prospective cross-sectional study was conducted on 506 Covid-19 positive patients and 200 controls over a period of two months (June and July 2020). The cases were sub-classified based on disease severity into mild to moderate (n=337), severe (n=118) and very severe (n=51) and based on survivor status into survivors (n=473) and non-survivors (n=33).

RESULTS

There were statistically significant differences in WBC count, Absolute neutrophil count (ANC), Absolute lymphocyte count (ALC), absolute monocyte count (AMC), neutrophil-lymphocyte ratio (NLR), platelet-lymphocyte ratio (PLR), monocyte-lymphocyte ratio (MLR) Red blood cell distribution width (RDW-SD) and RDW CV between covid cases vs controls; among the clinical subgroups and among the survivors and non-survivors. There was a significant strong positive correlation between various parameters, that is, NLR and MLR (r: 0.852, P=0), MPV and PDW (r: 0.912, P=0), MPV and PLCR (r: 0.956, P=0), PDW and PLCR (r: 0.893, P=0). NLR (AUC: 0.676, P=0) was the best single parameter and NLR+RDW-CV was best combination parameter as per area under curve (0.871) of ROC to distinguish severe from mild to moderate disease.

CONCLUSIONS

Leucocytosis, neutrophilia, lymphopenia and monocytosis were characteristic findings in covid cases while NLR and NLR+RDW-CV emerged as the most effective single and combination CBC parameters in distinguishing mild to moderate and severe cases respectively.

Remote sensing-based water quality index estimation using data-driven approaches: a case study of the Kali River in Uttar Pradesh, India

The present study evaluates the water quality status of 6-km-long Kali River stretch that passes through the Aligarh district in Uttar Pradesh, India, by utilizing high-resolution IRS P6 LISS IV imagery. In situ river water samples collected at 40 random locations were analyzed for seven physicochemical and four heavy metal concentrations, and the water quality index (WQI) was computed for each sampling location. A set of 11 spectral reflectance band combinations were formulated to identify the most significant band combination that is related to the observed WQI at each sampling location. Three approaches, namely multiple linear regression (MLR), backpropagation neural network (BPNN) and gene expression programming (GEP), were employed to relate WQI as a function of most significant band combination. Comparative assessment among the three utilized approaches was performed via quantitative indicators such as R ², RMSE and MAE. Results revealed that WQI estimates ranged between 203.7 and 262.33 and rated as "very poor" status. Results further indicated that GEP performed better than BPNN and MLR approaches and predicted WQI estimates with high R ² values (i.e., 0.94 for calibration and 0.91 for validation data), low RMSE and MAE values (i.e., 2.49 and 2.16 for calibration and 4.45 and 3.53 for validation data). Moreover, both GEP and BPNN depicted superiority over MLR approach that yielded WQI with R ² ~ 0.81 and 0.67 for calibration and validation data, respectively. WQI maps generated from the three approaches corroborate the existing pollution levels along the river stretch. In order to examine the significant differences among WQI estimates from the three approaches, one-way ANOVA test was performed, and the results in terms of F-statistic (F = 0.01) and p-value (p = 0.994 > 0.05) revealed WQI estimates as "not significant," reasoned to the small water sample size (i.e., N = 40). The study therefore recommends GEP as more rational and a better alternative for precise water quality monitoring of surface water bodies by producing simplified mathematical expressions.

Monocyte-to-Lymphocyte Ratio is Associated with Depression 3 Months After Stroke

PURPOSE

To explore the relationship between the monocyte-to-lymphocyte ratio (MLR) and depression three months after acute ischemic stroke.

PATIENTS AND METHODS

From May 2013 to September 2014, 203 patients with acute ischemic stroke were recruited within 7 days post-stroke from Shanghai Ruijin Hospital and blood samples were collected after admission. The Hamilton Depression Scale and Clinical Review were evaluated at 3 months after stroke. Based on the Diagnostic and Statistical Manual of Mental Disorders-IV diagnostic criteria, we divided patients into post-stroke depression (PSD) and non-PSD groups. We analyzed the intergroup difference in MLR and the contributing factors. Moreover, dynamic changes in monocytes, lymphocytes and MLR at four different time intervals for all the stroke patients and their relationship with PSD patients were also studied.

RESULTS

The NIHSS scores and MLR in the PSD group were significantly higher than in the non-PSD group (p<0.05). Logistic regression analysis revealed MLR was an independent risk factor for PSD (odds ratio: 18.020, 95% confidence interval: 1.127‒288.195, p=0.041). MLR correlated negatively with cholesterol and low-density lipoprotein (r=-0.160 and -0.165, respectively, p<0.05). Within 7 days post-acute ischemic stroke, monocytes gradually increased while lymphocytes remained unchanged for all the stroke patients. The MLR value was significantly higher in the PSD group than in the non-PSD group within 24 h post-stroke (p<0.05), but there was no difference in the other three time-intervals between the two groups.

CONCLUSION

The admission MLR, particularly within 24 h post-stroke, was associated with PSD at 3 months, implying that the MLR might be involved in the PSD inflammatory mechanism.

Admission neutrophil-to-lymphocyte ratio and monocyte-to-lymphocyte ratio to predict 30-day and 1-year mortality in geriatric hip fractures

INTRODUCTION

Elderly patients with hip fractures exhibit a high incidence of mortality and morbidity. The aim of this study was to evaluate the factors that may predict mortality in patients with geriatric hip fracture.

METHODS

Included in this retrospective study were 241 patients who were admitted to the hospital with hip fractures between May 2017 and March 2019. The effect of the modified 5-item frailty index (mFI-5) on 30-day and 1-year mortality was evaluated. In addition, admission neutrophil-to-lymphocyte ratios (NLRs) and monocyte-to-lymphocyte ratios (MLRs) were also examined.

RESULTS

The mFI-5 was not found to be a statistically significant predictive indicator for 30-day and 1-year mortality (P = 0.485 and P = 0.484, respectively). Chronic obstructive pulmonary disease or current pneumonia was found to increase mortality by 2.702 times (P = 0.002). The 30-day mortality rates of patients aged ≥80 years were significantly higher than those aged 65-79 years (P < 0.05). However, there was no statistical difference in the mortality rates between the age groups after 30 days postoperatively (P = 0.114). Admission NLRs and MLRs were significantly higher in the 30-day and 1-year mortality groups. The cut-off values of the admission NLRs and MLRs for 30-day mortality were calculated as 6.55 and 0.65, respectively, while the same cut-off values for 1-year mortality were calculated as 6.55 and 0.635, respectively.

CONCLUSIONS

Admission NLRs and MLRs are useful hematological data for the prediction of 30-day and 1-year mortality in geriatric hip fracture patients.

Elevated Platelet Parameter in First-Episode Schizophrenia Patients: A Cross-Sectional Study

Serotonin (5-HT) and inflammation are 2 major hypotheses in schizophrenia (SZ) pathogenesis, both of which involve platelets. However, the association between platelet and SZ has not been well studied. The aim of this study was to evaluate changes of platelet count (PLT), mean platelet volume (MPV), platelet-large cell ratio (P-LCR), platelet distribution width (PDW), and plateletcrit (PCT) in patients with first-episode schizophrenia (FES). Meanwhile, 3 inflammation markers, including neutrophil-lymphocyte ratio (NLR), platelet-lymphocyte ratio (PLR), and monocyte-lymphocyte ratio (MLR), were evaluated. Complete blood count of 106 FES patients, 82 first-episode depression (FED) patients, and 120 healthy controls (HCs) were compared. In addition, PLR, NLR, and MLR were calculated and compared among 3 groups. Our data suggested that PLT, MPV, P-LCR, PDW, PCT, NLR, PLR, and MLR in FES patients were significantly increased than those in the HCs (P < 0.01 or P < 0.05, respectively). PLT, PCT, PLR, and MLR in FED patients were significantly higher than those in the HCs (P < 0.01). However, no significant difference in MPV, P-LCR, and NLR was identified between FED patients and HCs (P > 0.05). Moreover, MPV, P-LCR, PDW, NLR, and MLR in FES patients were significantly higher than those in FED patients (P < 0.01 or P < 0.05, respectively). The elevation of PLT, MPV, P-LCR, PDW, PCT, NLR, PLR, and MLR in FES patients supported 5-HT and inflammation hypotheses in SZ pathogenesis. Further, our data suggested that increasing levels of MPV, P-LCR, PDW, NLR, and MLR might help to distinguish FES from FED. Clinical Trials.gov ID: 2018JJ2580.

Development and Validation of a Nomogram to Predict Lymph Node Metastasis in Patients With T1 High-Grade Urothelial Carcinoma of the Bladder

Purpose

This study aims to develop and validate a nomogram to predict lymph node (LN) metastasis preoperatively in patients with T1 high-grade urothelial carcinoma.

Methods

We retrospectively evaluated the data of 2,689 patients with urothelial carcinoma of the bladder (UCB) treated with radical cystectomy (RC) and bilateral lymphadenectomy in two medical centers. Eventually, 412 patients with T1 high-grade urothelial carcinoma were enrolled in the primary cohort to develop a prognostic nomogram designed to predict LN status. An independent validation cohort (containing 783 consecutive patients during the same period) was subjected to validate the predicting model. Binary regression analysis was used to develop the predicting nomogram. We assessed the performance of the nomogram concerning its clinical usefulness, calibration, and discrimination.

Results

Overall, 69 (16.75%), and 135 (17.24%) patients had LN metastasis in the primary cohort and external validation cohort, respectively. The final nomogram included information on tumor number, tumor size, lymphovascular invasion (LVI), fibrinogen, and monocyte-to-lymphocyte ratio (MLR). The nomogram showed good predictive accuracy and calibration with a concordance index in the primary cohort of 0.853. The application of the nomogram in the external validation cohort still gave good discrimination (C-index, 0.845) and good calibration. The analysis of the decision curve shows that the nomogram has clinical application value.

Conclusion

The nomogram that incorporated the tumor number, tumor size, LVI, fibrinogen, and MLR showed favorable predictive accuracy for LN metastasis. It may be conveniently used to predict LN metastasis in patients with T1 high-grade urothelial carcinoma and be helpful in guiding treatment decisions.

The prognostic and predictive role of the neutrophil-to-lymphocyte ratio and the monocyte-to-lymphocyte ratio in early breast cancer, especially in the HER2+ subtype

Purpose

The aim of this study was to investigate the prognostic impact of two systemic inflammatory markers, the neutrophil-to-lymphocyte ratio (NLR) and the monocyte-to-lymphocyte ratio (MLR), and their possible predictive role regarding the efficacy of adjuvant trastuzumab, in 209 early breast cancer cases, 107 of which were HER2-positive.

Methods

Baseline NLR and MLR values were divided into two groups, high and low, according to cut-off-points determined from the ROC curve (2.2 for NLR and 0.22 for MLR). Cox’s model was utilized for survival analyses.

Results

High NLR and MLR correlated with poor overall survival (OS) and breast cancer specific survival (BCSS) among all the patients (p ≤ 0.030). Among the HER2+ patients whose adjuvant treatment did not include trastuzumab (n = 64), the survival rates were remarkably lower in patients with a high NLR as compared to those with low; 31% vs. 71% for OS and 42% vs. 74% for BCSS (p ≤ 0.014). Similarly, high MLR correlated with poor survival among these patients (p ≤ 0.020). On the contrary, among the patients who had received adjuvant trastuzumab (n = 43), NLR or MLR did not correlate with survival. Furthermore, trastuzumab was beneficial for the HER2+ patients with high NLR/MLR, while the survival of the HER2+ patients with low NLR/MLR was good irrespective if they received adjuvant trastuzumab.

Conclusions

Our results suggest that trastuzumab modulates the systemic inflammatory conditions and overcomes the poor prognostic impact of high NLR/MLR. This finding may also provide a rationale for combining trastuzumab with immuno-oncological treatments in HER2+ breast cancer.

Electronic supplementary material

The online version of this article (10.1007/s10549-020-05925-7) contains supplementary material, which is available to authorized users.

2D-QSAR and docking study of a series of coumarin derivatives as inhibitors of CDK (anticancer activity) with an application of the molecular docking method

Quantitative Structure Activity Relationship (QSAR) analysis techniques are tools largely utilized in many research fields, including drug discovery processes. In this work electronic descriptors are calculated with the Gaussian 03W software using the DFT method with the BecKe 3-parameters exchange functional and Lee-Yang-Parr correlation functional, with Kohn and Sham orbitals (KS) developed on a Gaussian Basis of type 6-31G (d), in combination with five Lipinski parameters that have been calculated with ChemOffice software, in order to develop a statistically verified 2D-QSAR model able to predict the biological activity of new molecules belonging to the same range of coumarins rather than chemical synthesis and biological evaluations that require more time and resources. Two QSAR models against both MCF-7 and HepG-2 cell lines are obtained using the multiple linear regression method. The predictive power of these models has been confirmed by internal and external validation. The Leverage method was used to determine the domain of applicability of the 2D-QSAR models developed. The results indicate that the best QSAR model is the one that links the 2D descriptors with the CDK inhibitory activity of the cell line (HepG-2) R² = 0.748, R²cv = 0.618, MSE = 0.03 for the learning series and R² = 0.73, MSE = 0.18 for the test series. This model implies that coumarin inhibitory activity is strongly related to dipole moment and the number of hydrogen bond donors. The results obtained suggest the importance of studying structure-activity relationships as a principal axis in drug design. The docking procedure using AutoDOCK Tools was also used to understand the mechanisms of molecular interactions and consequently, to develop new inhibitors.

Development of a simple, interpretable and easily transferable QSAR model for quick screening antiviral databases in search of novel 3C-like protease (3CLpro) enzyme inhibitors against SARS-CoV diseases

In the context of recently emerged pandemic of COVID-19, we have performed two-dimensional quantitative structure-activity relationship (2D-QSAR) modelling using SARS-CoV-3CLpro enzyme inhibitors for the development of a multiple linear regression (MLR) based model. We have used 2D descriptors with an aim to develop an easily interpretable, transferable and reproducible model which may be used for quick prediction of SAR-CoV-3CLpro inhibitory activity for query compounds in the screening process. Based on the insights obtained from the developed 2D-QSAR model, we have identified the structural features responsible for the enhancement of the inhibitory activity against 3CLpro enzyme. Moreover, we have performed the molecular docking analysis using the most and least active molecules from the dataset to understand the molecular interactions involved in binding, and the results were then correlated with the essential structural features obtained from the 2D-QSAR model. Additionally, we have performed in silico predictions of SARS-CoV 3CLpro enzyme inhibitory activity of a total of 50,437 compounds obtained from two anti-viral drug databases (CAS COVID-19 antiviral candidate compound database and another recently reported list of prioritized compounds from the ZINC15 database) using the developed model and provided prioritized compounds for experimental detection of their performance for SARS-CoV 3CLpro enzyme inhibition.

The SENECA study: Prognostic role of serum biomarkers in older patients with metastatic colorectal cancer

BACKGROUND

Aging induces meaningful changes in the immune system and inflammation response with increase in monocyte-lymphocyte ratio (MLR) and serum lactate dehydrogenase (LDH) levels. Aim of this study was to explore the prognostic role of MLR and LDH levels in older patients (pts) with metastatic colorectal cancer (mCRC).

METHODS

We conducted a retrospective analysis of a consecutive cohort of 168 older (>70 years) patients with mCRC. The prognostic impact of MLR and LDH levels on overall survival (OS) was investigated through uni-and multivariate Cox regression analyses. Moreover, we categorized patients into three groups according to MLR and LDH levels (group 1: MLR-low and LDH-low; group 2: MLR-high or LDH-high; group 3: MLR-high and LDH-high).

RESULTS

By univariate analysis, high LDH level (HR 1.74, 95% CI 1.05-2.90) and high MLR level (HR 2.19, 95% CI 1.48-3.44) were significantly associated with a worse OS. Conversely, primary tumor resection and left-sidedness were significantly associated with a longer OS. By multivariate analysis, high LDH level (HR 2.00, 95% CI 1.13-3.55) and high MLR level (HR 2.99, 95% CI 1.68-5.33) were independent prognostic factors of worse prognosis. Compared to group 1, a shorter survival was reported for patients included in group 2 (HR 1.97, 95% CI 1.21-3.23 in univariate; HR 2.54, 95% CI 1.43-4.51 in multivariate) or in group 3 (HR 2.42, 95% CI 24-4.74, p = .010 in univariate; HR 5.59, 95% CI 2.15-14.54 in multivariate) CONCLUSIONS: High baseline levels of LDH, MLR or both are independent unfavorable prognostic factors in older patients treated with first-line chemotherapy for mCRC.

2D QSAR studies on a series of (4S,5R)-5-[3,5-bis(trifluoromethyl)phenyl]-4-methyl-1,3-oxazolidin-2-one as CETP inhibitors

Cardiovascular disease (CVD) is one of the major causes of human death. Preliminary evidence indicates that the inhibition treatment of Cholesteryl Ester Transfer Protein (CETP) causes the most pronounced increase in HDL cholesterol reported so far. Merck has disclosed certain (4S,5R)-5-[3,5-bis(trifluoromethyl)phenyl]-4-methyl-1,3-oxazolidin-2-one derivatives, which show potent CETP inhibitory activity. Therefore, it would be desirable to develop computational models to facilitate the screening of these inhibitors. In the present work, quantitative structure-activity relationship (QSAR) models have been developed to predict the therapeutic potency of 108 derivatives of (4S,5R)-5-[3,5-bis(trifluoromethyl)phenyl]-4-methyl-1,3-oxazolidin-2-one: Multiple Linear Regression (MLR), Support Vector Regression (SVR) and Feedforward Neural Network using Particle Swarm Optimization (FNN-PSO). Six descriptors were selected using genetic algorithms, whereas, internal and external validation of the models was performed according to all available validation strategies. It was shown that CETP inhibitory activity is mainly governed by electronegativity, the structure of the molecule, and the electronic properties. The best results were obtained with the SVR model. The results obtained may assist in the design of new CETP inhibitors.

Development of an Assessment Method for Investigating the Impact of Climate and Urban Parameters in Confirmed Cases of COVID-19: A New Challenge in Sustainable Development

Sustainable development has been a controversial global topic, and as a complex concept in recent years, it plays a key role in creating a favorable future for societies. Meanwhile, there are several problems in the process of implementing this approach, like epidemic diseases. Hence, in this study, the impact of climate and urban factors on confirmed cases of COVID-19 (a new type of coronavirus) with the trend and multivariate linear regression (MLR) has been investigated to propose a more accurate prediction model. For this propose, some important climate parameters, including daily average temperature, relative humidity, and wind speed, in addition to urban parameters such as population density, were considered, and their impacts on confirmed cases of COVID-19 were analyzed. The analysis was performed for three case studies in Italy, and the application of the proposed method has been investigated. The impacts of parameters have been considered with a delay time from one to nine days to find out the most suitable combination. The result of the analysis demonstrates the effectiveness of the proposed model and the impact of climate parameters on the trend of confirmed cases. The research hypothesis approved by the MLR model and the present assessment method could be applied by considering several variables that exhibit the exact delay of them to new confirmed cases of COVID-19.

Predictors for imaging progression on chest CT from coronavirus disease 2019 (COVID-19) patients

OBJECTIVE

This study aimed to investigate the potential parameters associated with imaging progression on chest CT from coronavirus disease 19 (COVID-19) patients.

RESULTS

The average age of 273 COVID-19 patients enrolled with imaging progression were older than those without imaging progression (p = 0.006). The white blood cells, platelets, neutrophils and acid glycoprotein were all decreased in imaging progression patients (all p < 0.05), and monocytes were increased (p = 0.025). The parameters including homocysteine, urea, creatinine and serum cystatin C were significantly higher in imaging progression patients (all p < 0.05), while eGFR decreased (p < 0.001). Monocyte-lymphocyte ratio (MLR) was significantly higher in imaging progression patients compared to that in imaging progression-free ones (p < 0.001). Logistic models revealed that age, MLR, homocysteine and period from onset to admission were factors for predicting imaging progression on chest CT at first week from COVID-19 patients (all p < 0.05).

CONCLUSION

Age, MLR, homocysteine and period from onset to admission could predict imaging progression on chest CT from COVID-19 patients.

METHODS

The primary outcome was imaging progression on chest CT. Baseline parameters were collected at the first day of admission. Imaging manifestations on chest CT were followed-up at (6±1) days.

The MIMIC Study: Prognostic Role and Cutoff Definition of Monocyte-to-Lymphocyte Ratio and Lactate Dehydrogenase Levels in Metastatic Colorectal Cancer

BACKGROUND

Monocyte-to-lymphocyte ratio (MLR) and lactate dehydrogenase (LDH) levels are circulating biomarkers that provide information about tumor-related inflammation and immune suppression. This study aimed to evaluate the prognostic role of MLR and LDH in metastatic colorectal cancer (mCRC).

MATERIAL AND METHODS

This multicentric study analyzed a consecutive cohort of 528 patients with mCRC treated in 2009-2017. The whole population was randomly divided in training and validation cohort. The first was used to identify a threshold for MLR and to create the prognostic model with MLR and MLR-LDH combined (group 1: MLR-LDH low; group 2: MLR or LDH high; group 3: MLR-LDH high). The second cohort was used to validate the model.

RESULTS

At the median follow-up of 55 months, median overall survival (OS) was 22 months. By multivariate analysis, high MLR >0.49 (hazard ratio [HR], 2.37; 95% confidence interval [C.I.], 1.39-4.04), high LDH (HR, 1.73; 95% C.I., 1.03-2.90) in the first model, group 2 (HR, 2.74; 95% C.I.; 1.62-4.66), and group 3 (HR, 3.73; 95% C.I., 1.94-7.18) in the combined model, had a worse prognosis in terms of OS. These data were confirmed both in the validation set and then in the whole cohort.

CONCLUSION

MLR and LDH are circulating cost-effective biomarkers, readily available in clinical practice, that can be useful for predicting the prognosis of patients with mCRC.

IMPLICATIONS FOR PRACTICE

High monocyte-to-lymphocyte ratio (MLR) and lactate dehydrogenase (LDH) levels could be a sign of a tumor's recruitment of suppressive and inflammatory cells worsening prognosis of different types of cancer, including colorectal cancer (CRC). Currently, no data are available for metastatic CRC regarding a cutoff definition for MLR or the prognostic impact of MLR and MLR-LDH combined. The present study showed in the training cohort and confirmed in the validation and whole cohort that MLR is a reliable and independent laboratory biomarker, which is easy to use, to predict clinical outcomes in patients with mCRC. Moreover, MLR and composite MLR-LDH could potentially result in an incremental improvement in the prognostic value of these biomarkers, being used as stratification tools for patients with mCRC.

Fuzzy ensemble system for SSVEP stimulation frequency detection using the MLR and MsetCCA

BACKGROUND

BCI systems based on steady-state visual evoked potentials (SSVEP) have formed an immense contribution to practical applications, due to their high recognition accuracy and ease of use. The MLR method has a better frequency recognition accuracy for short-term windows, and the MsetCCA method works more accurately in long-term windows.

NEW METHOD

The proposed fuzzy ensemble system can analyze the relevant SSVEP signals of each subject from 0.5 to 4 s windows with 0.5 s incremental steps. It is capable of taking decisions to improve the accuracy of SSVEP stimulation frequency recognition using the MLR and MsetCCA methods.

RESULTS

Our fuzzy system provides high-accuracy results for the stimulation frequency recognition in signals with the length of 1 s and more. Specifically, the average accuracy of 2 s windows has improved to 100 percent.

COMPARISON WITH EXISTING METHODS

The recognition accuracy of the presented system is always better than both MLR and MsetCCA methods.

CONCLUSION

One of the capabilities of fuzzy systems is that they can use human information and knowledge to build engineering systems. The fuzzy ensemble system can utilize various methods or classifiers simultaneously. The new system has proposed to combine multiple methods using the fuzzy ensemble, which encompasses the benefits of all the subsystems.

Study on the oxidation potential of the water-soluble components of ambient PM2.5 over Xi'an, China: Pollution levels, source apportionment and transport pathways

Reactive oxygen species (ROS) are a class of substances that are of general concern in terms of human health and are used to represent the oxidation potential (OP) of the atmosphere. In this study, the ROS levels in 116 daily fine particulate matter (PM_2.5) samples taken over Xi'an in 2017 were measured with the dithiothreitol (DTT) method. The sources of DTTv (volume-based DTT consumption) in PM_2.5 as well as their contributions were identified by both positive matrix factorization (PMF) and multiple linear regression (MLR) based on the measured chemical species in particulate matter (PM). The results showed that the yearly average DTTv over Xi'an was 0.53 nmol/min/m³ (0.19-1.10 nmol/min/m³). The highest DTTv level occurred in winter, followed by spring, summer and autumn. DTTv was the most strongly correlated with the water-soluble organic carbon (WSOC; r = 0.85), but the effects of WSOC on DTTv were very limited. SO₂, NO₂, CO, elemental carbon (EC) and K⁺ (r > 0.64) had moderate correlations with DTTv and were moderately related to environmentally persistent free radicals (EPFRs) (r = 0.56). The linear mixed-effects model showed that pollutants originating from incomplete combustion had greater effects on DTTv than those from complete combustion. Source apportionment results from PMF showed that motor vehicle emissions (27.4%), secondary sulfates (21.6%) and coal combustion sources (18.8%) were more important contributors to the DTTv in PM_2.5 than dust sources (8.4%), metal processing (4.9%), industrial emissions (11.3%) and secondary nitrates (7.5%). The PMF results for the DTTv were consistent with the MLR results, which verified that both PMF and MLR are feasible methods for source apportionment of PM_2.5 as well as specific species such as ROS and EPFRs. Backward trajectory clusters showed that the dominant cluster groups were local and regional transport, while the OP of the PM_2.5 over Xi'an was affected more by long-range transport than by local transport. As stated above, the improvement of atmospheric oxidation potential require not only regional efforts but also large-scale joint cooperation. Furthermore, this study on the OP of PM as well as the specific source information provides important guidance for health effect research.

Rational design of novel coumarins: A potential trend for antioxidants in cosmetics

Coumarins are well-known for their antioxidant effect and aromatic property, thus, they are one of ingredients commonly added in cosmetics and personal care products. Quantitative structure-activity relationships (QSAR) modeling is an in silico method widely used to facilitate rational design and structural optimization of novel drugs. Herein, QSAR modeling was used to elucidate key properties governing antioxidant activity of a series of the reported coumarin-based antioxidant agents (1-28). Several types of descriptors (calculated from 4 softwares i.e., Gaussian 09, Dragon, PaDEL and Mold2 softwares) were used to generate three multiple linear regression (MLR) models with preferable predictive performance (Q 2 LOO-CV = 0.813-0.908; RMSE LOO-CV = 0.150-0.210; Q 2 Ext = 0.875-0.952; RMSE Ext = 0.104-0.166). QSAR analysis indicated that number of secondary amines (nArNHR), polarizability (G2p), electronegativity (D467, D580, SpMin2_Bhe, and MATS8e), van der Waals volume (D491 and D461), and H-bond potential (SHBint4) are important properties governing antioxidant activity. The constructed models were also applied to guide in silico rational design of an additional set of 69 structurally modified coumarins with improved antioxidant activity. Finally, a set of 9 promising newly design compounds were highlighted for further development. Structure-activity analysis also revealed key features required for potent activity which would be useful for guiding the future rational design. In overview, our findings demonstrated that QSAR modeling could possibly be a facilitating tool to enhance successful development of bioactive compounds for health and cosmetic applications.

Investigation on Quantitative Structure-Activity Relationships of 1,3,4-Oxadiazole Derivatives as Potential Telomerase Inhibitors

BACKGROUND

Telomerase, a reverse transcriptase, maintains telomere and chromosomes integrity of dividing cells, while it is inactivated in most somatic cells. In tumor cells, telomerase is highly activated, and works in order to maintain the length of telomeres causing immortality, hence it could be considered as a potential marker to tumorigenesis.A series of 1,3,4-oxadiazole derivatives showed significant broad-spectrum anticancer activity against different cell lines, and demonstrated telomerase inhibition.

METHODS

This series of 24 N-benzylidene-2-((5-(pyridine-4-yl)-1,3,4-oxadiazol-2yl)thio)acetohydrazide derivatives as telomerase inhibitors has been considered to carry out QSAR studies. The endpoint to build QSAR models is determined by the IC50 values for telomerase inhibition, i.e., the concentration (μM) of inhibitor that produces 50% inhibition. These values were converted to pIC50 (- log IC50) values. We used the most common and transparent method, where models are described by clearly expressed mathematical equations: Multiple Linear Regression (MLR) by Ordinary Least Squares (OLS).

RESULTS

Validated models with high correlation coefficients were developed. The Multiple Linear Regression (MLR) models, by Ordinary Least Squares (OLS), showed good robustness and predictive capability, according to the Multi-Criteria Decision Making (MCDM = 0.8352), a technique that simultaneously enhances the performances of a certain number of criteria. The descriptors selected for the models, such as electrotopological state (E-state) descriptors, and extended topochemical atom (ETA) descriptors, showed the relevant chemical information contributing to the activity of these compounds.

CONCLUSION

The results obtained in this study make sure about the identification of potential hits as prospective telomerase inhibitors.

Combination of Platelet-Lymphocyte Ratio and Monocyte-Lymphocyte Ratio as a New Promising Prognostic Factor in Upper Tract Urothelial Carcinoma With Large Tumor Sizes > 3 cm

PURPOSE

The purpose of this study was to evaluate the prognostic values of pathological tumor size and preoperative blood-based inflammation biomarkers, including the neutrophil-lymphocyte ratio (NLR), platelet-lymphocyte ratio (PLR), and monocyte-lymphocyte ratio (MLR), in upper tract urothelial carcinoma (UTUC).

MATERIALS AND METHODS

From 2007 to 2017, retrospective data of 449 patients with UTUC who underwent radical nephroureterectomy were assessed. Use of Kaplan-Meier and univariable/multivariable analyses evaluated the effect of preoperative blood-based inflammation biomarkers on overall (OS), cancer-specific (CSS), and progression-free survival (PFS) in pathological tumor sizes > and ≤3 cm.

RESULTS

Kaplan-Meier analyses showed that high-level NLR, PLR, or MLR had significantly shorter OS, CSS, and PFS for tumor sizes >3 cm (all P < .05), but not for ≤3 cm. For UTUCs with tumor sizes >3 cm, multivariable analyses showed simultaneously high-level PLR and MLR to be independent predicators of poor OS, CSS, and PFS (all P < .05). Moreover, receiver operating characteristic (ROC) analyses revealed that the predictive accuracy of the combination of PLR and MLR for OS, CSS, and PFS with the area under the ROC curve of 0.836, 0.871, and 0.806, respectively, in tumor sizes >3 cm (all P < .001).

CONCLUSIONS

Our study demonstrated that a high-level PLR and MLR can serve as an independent predicator of worse outcomes in UTUCs with tumor sizes >3 cm. This combination can clinically help enhance the prognostic discrimination of UTUCs with tumor sizes >3 cm and further may guide physicians in selecting patients for postoperatively systemic chemotherapy.

A Comprehensive QSAR Study on Antileishmanial and Antitrypanosomal Cinnamate Ester Analogues

Parasitic infections like leishmaniasis and trypanosomiasis remain as a worldwide concern to public health. Improvement of the currently available drug discovery pipelines for those diseases is therefore mandatory. We have recently reported on the antileishmanial and antitrypanosomal activity of a set of cinnamate esters where we identified several compounds with interesting activity against L. donovani and T. brucei rhodesiense. For a better understanding of such compounds' anti-infective activity, analyses of the underlying structure-activity relationships, especially from a quantitative point of view, would be a prerequisite for rational further development of such compounds. Thus, quantitative structure-activity relationships (QSAR) modeling for the mentioned set of compounds and their antileishmanial and antitrypanosomal activity was performed using a genetic algorithm as main variable selection tool and multiple linear regression as statistical analysis. Changes in the composition of the training/test sets were evaluated (two randomly selected and one by Kennard-Stone algorithm). The effect of the size of the models (number of descriptors) was also investigated. The quality of all resulting models was assessed by a variety of validation parameters. The models were ranked by newly introduced scoring functions accounting for the fulfillment of each of the validation criteria evaluated. The test sets were effectively within the applicability domain of the best models, which demonstrated high robustness. Detailed analysis of the molecular descriptors involved in those models revealed strong dependence of activity on the number and type of polar atoms, which affect the hydrophobic/hydrophilic properties causing a prominent influence on the investigated biological activities.

Prediction of Lipophilicity of some Quinolone Derivatives by using Quantitative Structure-Activity Relationship

OBJECTIVES

Quantitative structure activity relationship (QSAR) was used to study the partition coefficient of some quinolones and their derivatives.

METHODS

These molecules are broad-spectrum antibiotic pharmaceutics. First, data were divided into two categories of train and test (validation) sets using a random selection method. Second, three approaches, including stepwise selection (STS) (forward), genetic algorithm (GA), and simulated annealing (SA) were used to select the descriptors, to examine the effect feature selection methods. To find the relation between descriptors and partition coefficient, multiple linear regression (MLR), principal component regression (PCR) and partial least squares (PLS) were used.

RESULTS

QSAR study showed that both regression and descriptor selection methods have a vital role in the results. Different statistical metrics showed that the MLR-SA approach with (r2=0.96, q2=0.91, pred_r2=0.95) gives the best outcome.

CONCLUSION

The proposed expression by the MLR-SA approach can be used in the better design of novel quinolones and their derivatives.

Association of simple hematological parameters with disease manifestations, activity, and severity in patients with systemic sclerosis

INTRODUCTION/OBJECTIVES

Neutrophil-to-lymphocyte ratio (NLR), monocyte-to-lymphocyte ratio (MLR), mean platelet volume (MPV), and red cell distribution width (RDW) may potentially reflect inflammatory status in systemic autoimmune diseases. The aim of this study is to investigate the association between these proposed markers and disease manifestations, activity, and severity in systemic sclerosis (SSc).

METHOD

We conducted a cross-sectional study of 69 systemic sclerosis (SSc) patients and 50 healthy volunteers in a single center. Adult patients with SSc and healthy controls were compared in terms of NLR, MLR, MPV, RDW, erythrocyte sedimentation rate (ESR), and C-reactive protein (CRP). Venous blood samples were drawn after at least 8 h of fasting in the morning. Extension of skin fibrosis was evaluated by using modified Rodnan skin score (mRSS). Disease severity and activity were assessed by Medsger disease severity and European Scleroderma Trials and Research Group (EUSTAR) disease activity scores, respectively. Associations of disease manifestations, clinical, laboratory, and capillaroscopic findings, mRSS, and the disease activity and severity scores with the proposed hematological markers were evaluated. Multiple regression models were generated for significant associations.

RESULTS

The neutrophil number was higher (p = 0.004) and lymphocyte number was lower (p < 0.001) in SSc group compared to controls. SSc group also had higher NLR, MLR, and RDW. In multiple logistic regression, only the NLR (regression coefficient = 3.49, p = 0.031) and CRP (regression coefficient = 0.17, p = 0.037) remained significantly different between SSc and healthy control groups (Cox and Snell R² = 0.243, Nagelkerke R² = 0.337, p < 0.001). NLR and MLR positively correlated with mRSS, EUSTAR score, and CRP. MLR also positively correlated with Medsger score. Higher monocyte counts independently predicted higher EUSTAR and Medsger scores in multiple linear regressions. Patients with digital ulcers had higher NLR and MLR. We did not find any difference in MPV values between SSc and healthy control groups.

CONCLUSIONS

Globally available and inexpensive hematological tests, particularly the NLR and MLR, may be associated with vascular and cutaneous manifestations as well as disease activity and severity in SSc.Key Points• Monocyte count itself independently predicted higher activity and severity scores in SSc.• Globally available and inexpensive hematological markers, particularly the NLR and MLR, may have an association with vascular and cutaneous manifestations as well as disease activity and severity in patients with SSc.

13C NMR chemical shift prediction of diverse chemical compounds

Selection of key descriptors is very important in QSPR analysis. Presence of noise in the subset of descriptors reduces the quality of predictions. A complete set is considered as perfect when it does not include irrelevant or redundant elements. This paper reports complete sets of descriptors used to develop QSPR models for 1786 ¹³C NMR chemical shifts (δ_C parameters) of carbon atoms in 125 diverse chemical compounds. PBE1PBE/6-311G(2d,2p) and B3LYP/6-31G(d) basis sets were used for quantum chemistry calculations after the molecular structures were optimized with semi-empirical AM1 and B3LYP/6-31G(d). The two complete sets consisting of magnetic shielding elements (σ_XX, σ_YY, σ_ZZ) and the chemical shift principal values (σ₁₁, σ₂₂, σ₃₃) were used as the inputs for support vector machine (SVM) models of δ_C parameters. The four SVM models obtained have the mean root mean square (rms) errors of about 4.5-4.6 ppm. The results suggest that SVM models are accurate and acceptable compared with previous models, although our models are based on a relatively large set of compounds. Our approach is valuable in the selection of important descriptors for QSPR studies of δ_C parameters.

Determination of the superficial citral content on microparticles: An application of NIR spectroscopy coupled with chemometric tools

This work evaluates near-infrared (NIR) spectroscopy coupled with chemometric tools for determining the superficial content of citral (SCCt) on microparticles. To perform this evaluation, using spray drying, citral was encapsulated in a matrix of dextrin using twelve combinations of citral:dextrin ratios (CDR) and inlet air temperatures (IAT). From each treatment, six samples were extracted, and their SCCt and NIR absorption spectral profiles were measured. Then, the spectral profiles, pretreated and randomly divided into modeling and validation datasets, were used to build the following prediction models: principal component analysis-multilinear regression (PCA-MLR), principal component analysis-artificial neural network (PCA-ANN), partial least squares regression (PLSR) and an artificial neural network (ANN). During the validation stage, the models showed R2 values from 0.73 to 0.96 and a root mean squared error (RMSE) range of [0.061–0.140]. Moreover, when the models were compared, the full and optimized ANN models showed the best fits. According to this study, NIR coupled with chemometric tools has the potential for application in determining SCCt on microparticles, particularly when using ANN models.

Bulk Protein and Oil Prediction in Soybeans Using Transmission Raman Spectroscopy: A Comparison of Approaches to Optimize Accuracy

Rapid measurements of protein and oil content are important for a variety of uses, from sorting of soybeans at the point of harvest to feedback during soybean meal production. In this study, our goal is to develop a simple protocol to permit rapid and robust quantitative prediction of soybean constituents using transmission Raman spectroscopy (TRS). To develop this approach, we systematically varied the various elements of the measurement process to provide a diverse test bed. First, we utilized an in-house-built benchtop TRS instrument such that suitable optical configurations could be rapidly deployed and analyzed for experimental data collection for individual soybean grains. Second, we also utilized three different soybean varieties with relatively low (33.97%), medium (36.98%), and high protein (41.23%) contents to test the development process. Third, samples from each variety were prepared using whole bean and three different sample treatments (i.e., ground bean, whole meal, and ground meal). In each case, we modeled the data obtained using partial least squares (PLS) regression and assessed spectral metric-based multiple linear regression (metric-MLR) approaches to build robust prediction models. The metric-MLR models showed lower root mean square errors (RMSEPs), and hence better prediction, compared to corresponding classical PLS regression models for both bulk protein and oil for all treatment types. Comparing different sample preparation approaches, a lower RMSEPs was observed for whole meal treatment and thus the metric-MLR modeling with ground meal treatment was considered to be optimal protocol for bulk protein and oil prediction in soybean, with RMSEP values of 1.15 ± 0.04 (R²= 0.87) and 0.80 ± 0.02 (R²= 0.87) for bulk protein and oil, respectively. These predictions were nearly two- to threefold better (i.e., lower RMSEPs) than the corresponding NIR spectroscopy measurements (i.e., secondary gold standards in grain industry). For content prediction in whole soybean, incorporating physical attributes of individual grains in metric-MLR approach show up to 22% improvement in bulk protein and a relatively mild (up to ∼5%) improvement in bulk oil prediction. The unique combination of metric-MLR modeling approach (which is rare in the field of grain analysis) and sample treatments resulted in improved prediction models; using the physical attributes of individual grains is suggested as a novel measure for improving accuracy in prediction.

Neonicotinoid insecticide design: molecular docking, multiple chemometric approaches, and toxicity relationship with Cowpea aphids

Neonicotinoids are the fastest-growing class of insecticides successfully applied in plant protection, human and animal health care. The significant resistance increases led to the urgent need for alternative new neonicotinoids, with improved insecticidal activity. We performed molecular docking to describe a common binding mode of neonicotinoids into the nicotinic acetylcholine receptor, and to select the appropriate conformations to derive models. These were further used in a QSAR study employing both linear and nonlinear approaches to model the inhibitory activity against the Cowpea aphids. Linear modeling was performed by multiple linear regression and partial least squares and nonlinear modeling by artificial neural networks and support vector machine methods. The OECD principles were considered for QSAR models validation. Robust models with predictive power were found for neonicotinoid diverse structures. Based on our QSAR and docking outcomes, five new insecticides were predicted, according to the model applicability domain, the ligand efficiencies, and the binding mode. Therefore, the developed models can be confidently used for the prediction of the insecticidal activity of new chemicals, saving a substantial amount of time and money and, also, contributing to the chemical risk assessment.

The novel immunosuppressant prenylated quinolinecarboxylic acid-18 (PQA-18) suppresses macrophage differentiation and cytotoxicity in xenotransplantation

Innate immunity plays a major role in xenograft rejection. However, the majority of immunosuppressants focus on inhibiting acquired immunity and not innate immunity. Therefore, a novel immunosuppressant suitable for use in conjunction with xenografts continues to be needed. It has been reported that prenylated quinolinecarboxylic acid-18 (PQA-18), a p21-activated kinase 2 (PAK2) inhibitor, exerts an immunosuppressive function on T cells. Hence, the possibility exists that PQA-18 might be used in conjunction with xenografts, which prompted us to investigate the efficacy of PQA-18 on macrophages compared with Tofacitinib, a janus kinase (JAK) inhibitor. Initial experiments confirmed that PQA-18 is non-toxic to swine endothelial cells (SECs) and human monocytes. Both PQA-18 and Tofacitinib suppressed macrophage-mediated cytotoxicity in both the differentiation and effector phases. Both PQA-18 and tofacitinib suppressed the expression of HLA-ABC by macrophages. However, contrary to Tofacitinib, PQA-18 also significantly suppressed the expression of CD11b, HLA-DR and CD40 on macrophages. PQA-18 significantly suppressed CCR7 expression on day 3 and on day 6, but Tofacitinib-induced suppression only on day 6. In a mixed lymphocyte reaction (MLR) assay, PQA-18 was found to suppress Interleukin-2 (IL-2)-stimulated T cell proliferation to a lesser extent than Tofacitinib. However, PQA-18 suppressed xenogeneic-induced T cell proliferation more strongly than Tofacitinib on day 3 and the suppression was similar on day 7. In conclusion, PQA-18 has the potential to function as an immunosuppressant for xenotransplantation.

Designing Potential Antitrypanosomal Thiazol-2-ethylamines through Predictive Regression Based and Classification Based QSAR Analyses

BACKGROUND

Thiazol-2-ethylamine is recently reported to be an interesting scaffold having antitrypansomal activity for the treatment of sleeping sickness.

METHODS

Statistically significant, robust and validated regression-based QSAR models are constructed for a series of antitrypansomal thiazol-2-ethylamines. Moreover, classification-based QSAR analyses (linear discriminant analysis and Bayesian classification modelling) are also performed to identify the important structural features controlling antitrypanosomal activity.

RESULTS

Molecular fingerprints such as N-piperidinyl and 2-fluorophenyl functions may be responsible for higher antitrypanosomal activity whereas compounds with chlorophenyl moiety and compounds with unsaturated nitrogen atom possess poor activity. These results are supported by the regression-based QSAR model as well as the SAR observations.

CONCLUSION

Finally, fifteen new compounds bearing thiazol-2-ethylamine scaffold are designed and predicted along with their drug-likeness properties. Therefore, this study may provide important structural aspects of designing new antitrypansomal agents with higher activity.

Neuron-targeted caveolin-1 improves neuromuscular function and extends survival in SOD1G93A mice

Interventions that preserve motor neurons or restore functional motor neuroplasticity may extend longevity in amyotrophic lateral sclerosis (ALS). Delivery of neurotrophins may potentially revive degenerating motor neurons, yet this approach is dependent on the proper subcellular localization of neurotrophin receptor (NTR) to plasmalemmal signaling microdomains, termed membrane/lipid rafts (MLRs). We previously showed that overexpression of synapsin-driven caveolin-1 (Cav-1) (SynCav1) increases MLR localization of NTR [e.g., receptor tyrosine kinase B (TrkB)], promotes hippocampal synaptic and neuroplasticity, and significantly improves learning and memory in aged mice. The present study crossed a SynCav1 transgene-positive (SynCav1⁺) mouse with the mutant human superoxide dismutase glycine to alanine point mutation at amino acid 93 (hSOD1^G93A) mouse model of ALS. When compared with hSOD1^G93A, hSOD1^G93A/SynCav1⁺ mice exhibited greater body weight and longer survival as well as better motor function. Microscopic analyses of hSOD1^G93A/SynCav1⁺ spinal cords revealed preserved spinal cord α-motor neurons and preserved mitochondrial morphology. Moreover, hSOD1^G93A/SynCav1⁺ spinal cords contained more MLRs (cholera toxin subunit B positive) and MLR-associated TrkB and Cav-1 protein expression. These findings demonstrate that SynCav1 delays disease progression in a mouse model of ALS, potentially by preserving or restoring NTR expression and localization to MLRs.-Sawada, A., Wang, S., Jian, M., Leem, J., Wackerbarth, J., Egawa, J., Schilling, J. M., Platoshyn, O., Zemljic-Harpf, A., Roth, D. M., Patel, H. H., Patel, P. M., Marsala, M., Head, B. P. Neuron-targeted caveolin-1 improves neuromuscular function and extends survival in SOD1^G93A mice.

Source apportionment of the oxidative potential of fine ambient particulate matter (PM2.5) in Athens, Greece

The main objective of this study was chemical characterization and source apportionment of the oxidative potential of ambient PM_2.5 samples collected in an urban background area in Athens, Greece. Ambient PM_2.5 samples were collected during the summer (June-September) of 2017 and winter (February-March) of 2018 at a residential, urban background site in the outlying neighborhood of the Demokritos National Laboratory in Athens, Greece. The collected PM samples were analyzed for their chemical constituents including metals and trace elements, water-soluble organic carbon (WSOC), elemental and organic carbon (EC/OC), and marker of biomass burning (i.e., levoglucosan). In addition, the DCFH in vitro assay was performed to determine the oxidative potential of the PM_2.5 samples. We performed a series of statistical analyses, including Spearman rank-order correlation analysis, principal component analysis (PCA), and multi linear regression (MLR) to determine the most significant species (as source tracers) contributing to the oxidative potential of PM_2.5. Our findings revealed that the intrinsic (per PM mass) and extrinsic (per m³ of air volume) oxidative potentials of the collected ambient PM_2.5 samples were significantly higher than those measured in many urban areas around the world. The results of the MLR analyses indicated that the major pollution sources contributing to the oxidative potential of ambient PM_2.5 were vehicular emissions (characterized by EC) (44%), followed by secondary organic aerosol (SOA) formation (characterized by WSOC) (16%), and biomass burning (characterized by levoglucosan) (9%). The oxidative potential of the collected ambient PM_2.5 samples was also higher in summer compared to the winter, mainly due to higher concentrations of EC and WSOC during this season. Results from this study corroborate the impact of traffic and SOA on the oxidative potential of ambient PM_2.5 in greater Athens area, and can be helpful in adopting appropriate public health policies regarding detrimental outcomes of exposure to PM_2.5.

Impact of emissions from the Ports of Los Angeles and Long Beach on the oxidative potential of ambient PM0.25 measured across the Los Angeles County

In this study, weekly samples of ambient PM_0.25 (particulate matter with an aerodynamic diameter <0.25 μm) were collected in three contrasting locations, including central Los Angeles (USC), north Long Beach (NLB), and the Port of Long Beach (PRT), during June and July of 2017 to evaluate the chemical composition of ambient PM_0.25 and identify the sources that contribute to the oxidative potential of ambient PM_0.25 in these locations. Special focus was given in exploring the impact of emissions from the Ports of Los Angeles and Long Beach on the oxidative potential of ambient PM_0.25 measured across these sites. The oxidative potential of the collected samples was quantified by means of an in vitro cell-based alveolar macrophage (AM) assay. We used multiple linear regression (MLR) analysis to link individual measured species, used as source markers, to the oxidative potential of the ambient PM_0.25 across the monitoring sites. Results from the MLR analysis indicated that vehicular emissions and secondary organic aerosols (SOA) were the major contributors to the oxidative potential of ambient PM_0.25 across the three sites, with corresponding contributions of 40 ± 2% and 39 ± 3%, respectively. Emissions of PM_0.25 related to port activities, including emissions from ships, locomotives, and heavy-duty vehicles (HDVs) operating at the port, accounted for 16 ± 3% of the overall oxidative potential of the ambient PM_0.25 samples. The concentrations of the marker species at the three different sites suggested that the contributions of port-related emissions to the oxidative potential of PM_0.25 decreased from the port area to central Los Angeles, underscoring the greater impact of these emissions on the PM_0.25 toxicity in the communities near the Ports of Los Angeles and Long Beach, whereas we observed larger impact of SOA formation and vehicular emissions on the oxidative potential of ambient PM_0.25 in the receptor sites located further inland.

The Case of a Micellar Liquid Chromatography Descriptor (log KW SDS )

Improving the interpretability of multivariate QSPR models is a major issue in modern drug discovery. In this study we applied three strategies to model and deconvolute the balance of intermolecular forces governing log K_W ^SDS , a chromatographic descriptor of potential relevance in the prediction of ADME phenomena. A dataset of 77 compounds was set-up and an ad hoc pool of VS+ descriptors calculated. The data matrix was firstly submitted to a PCA run for a preliminary analysis and outliers detection. To model and interpret log K_W ^SDS three chemoinformatic approaches implementing either variable selection or grouping tools were used: a) MLR and GA, b) PLSR combined with BR analysis and c) MBPLSR. Results provided by the three methods were largely superposable both in terms of prediction performances and mechanistic interpretation. Overall, they showed that log K_W ^SDS is a complex descriptor mainly governed by the dimension, polarity and HBD solutes' properties. Chemoinformatic strategies as those reported in this paper might be applied to any chromatographic system and thus represent a potent tool to exploit the full potential of chromatographic descriptors in pharmaceutical, toxicological and related sciences.

Evaluation of Chemotherapeutic Activity of the Selected Bases' Analogues of Nucleic Acids Supported by ab initio Various Quantum Chemical Calculations

BACKGROUND

Pharmacological and physicochemical classification of bases' selected analogues of nucleic acids is proposed in the study.

OBJECTIVE

Structural parameters received by the PCM (Polarizable Continuum Model) with several types of calculation methods for the structures in vacuo and in the aquatic environment together with the huge set of extra molecular descriptors obtained by the professional software and literature values of biological activity were used to search the relationships.

METHODS

Principal Component Analysis (PCA) together with Factor Analysis (FA) and Multiple Linear Regressions (MLR) as the types of the chemometric approach based on semi-empirical ab initio molecular modeling studies were performed.

RESULTS

The equations with statistically significant descriptors were proposed to demonstrate both the common and differentiating characteristics of the bases' analogues of nucleic acids based on the quantum chemical calculations and biological activity data.

CONCLUSION

The obtained QSAR models can be used for predicting and explaining the activity of studied molecules.

An Approach to Identify New Insecticides Against Myzus Persicae. In silico Study Based on Linear and Non-linear Regression Techniques

Neonicotinoids are known to have high insecticidal potency, low mammalian toxicity and relatively tough activity for the development of resistance against aphids. A series of guadipyr insecticides, active against Myzus persicae was engaged in silico studies, based on Multiple Linear Regression (MLR), Partial Least Squares regression (PLS), Artificial Neural Networks (ANN), Support Vector Machine (SVM) and Pharmacophore modeling. Robust and predictive models were built using correlations between the insecticidal profile, expressed by experimental pLC₅₀ values, and molecular descriptors, calculated from the energy optimized structures. Four new potential insecticides active against Myzus persicae and their predicted pLC₅₀ toxicity values were reported for the first time. The models presented here can be used as an approach in the screening and prioritization of chemicals in a scientific and regulatory frame and for toxicity prediction.

Improving one-dimensional pollution dispersion modeling in rivers using ANFIS and ANN-based GA optimized models

Simulation and prediction of the pollution transport is one of the major problems in environmental and rivers engineering studies. The numerical tools have been used in simulation of the concentration profile transmission for description of river water quality. The one-dimensional advection-dispersion equation (ADE) is used in applied water quality modeling and requires the accurate estimation of longitudinal dispersion coefficient (D_x). This paper develops a hybrid numerical-intelligence model for dispersion modeling in open-channel flows. The main contribution of this paper is to improve the results of 1D numerical simulation of pollutant transport in steady flows by estimation of dispersion coefficient (D_x) based on artificial intelligence models and subset selection of maximum dissimilarity (SSMD). The developed hybrid model uses an intelligence module based on optimized adaptive neuro fuzzy inference system (ANFIS) and artificial neural networks (ANNs) for longitudinal dispersion estimation, in which their structures are optimized by genetic algorithm (GA). Intelligence estimates of D_x by ANN, ANFIS, ANFIS-GA, ANN-GA, multiple linear regression (MLR), and empirical equation are compared with observed values of D_x available in 505 river section, and the ANFIS-GA, as the most accurate, is incorporated and integrated with developed 1D-ADE numerical module. The numerical solution of 1D-ADE is done using physically influenced scheme (PIS) for face flux estimation in finite volume method. The performance of hybrid models PIS-ANFIS-GA, PIS-ANFIS, and PIS-empirical is compared using the R², RMSE, MAE, and NSE values in comparison with analytical solution and measured concentration hydrographs. The results revealed that the hybrid numerical-intelligence model is more accurate than the other classical methods for sediment/pollutant dispersion prediction in open-channel flows. The developed hybrid numerical-intelligence model can accurately simulate the dispersion processes in rivers and is a novel step in applicability of ANFIS-GA and ANN-GA models. Graphical abstract ᅟ.

Multiwavelength Regression Algorithm for Eliminating Chamber Surface Effects of Microfluidic Chips

An ultraviolet visible (UV-Vis) spectrophotometric and multiwavelength linear regression (MLR) method was developed for eliminating the influence of the surface quality of centrifugal microfluidic chips on the accuracy of their absorbance detection. The regression model is based on scalar scattering theory. The method was validated with cuvettes with different surface quality and Orange G (orange gelb) dye. The coefficients of variation (CVs) of the predicted solution concentration ratios in different cuvettes were < 1%, and the relative errors were < 1.5%. The model was shown to have higher accuracy and precision than that of traditional methods.

Influence of volatile fatty acids in different inoculum to substrate ratio and enhancement of biogas production using water hyacinth and salvinia

The aim of the study was to explore the role of volatile fatty acids in batch scale anaerobic digestion of two potential aquatic weeds (water hyacinth and salvinia), as substrates for anaerobic digestion. Inoculum (cow dung) to biomass (aquatic weeds) ratio of 0.25:1, 0.5:1, 1:1 and 3:1 were taken and monitored in a 2 L digester for 30 days. The maximum amount of biogas produced for was 406 L kg^-1 VS for water hyacinth in 0.5:1 and 330 L kg^-1 VS for salvinia in 1:1 and 3:1 ratio for both. The total VFA for water hyacinth and salvinia were 410 mgL^-1 & 365 mg L^-1, 424 mg L^-1 & 316 mg L^-1, 272 mg L^-1 & 234 mg L^-1 and 158 mg L^-1 & 94 mg L^-1 in 0.25:1, 0.5:1, 1:1 and 3:1 ratios respectively. Multiple linear regression was performed to find the relationship between the methane from biogas, total VFA, acetic acid and propionic acid for enhanced biogas production. It was observed that total VFA significantly influenced methane content in 1:1 (SC) and 3:1 (WC).

QSAR modeling of adipose/blood partition coefficients of Alcohols, PCBs, PBDEs, PCDDs and PAHs: A data gap filling approach

Physiologically-based toxicokinetic (PBTK) model has immense role to play in the risk assessment process due to specified mathematical representation of the absorption, Distribution, Metabolism, Excretion and Toxicity (ADMET) of chemicals in diverse environmental compartment. Determination of adipose/blood partition coefficient [logP_{(adipose/blood)}] is regarded as one of the crucial constraints of PBTK models. In respect to the challenge for identifying the chemical-definite parameters for these models, especially within short time frame and with limited resources, quantitative structure-activity relationship (QSAR) models are beneficial for providing the chemical-specific parameters of PBTK models. In the present study, we have developed robust, statistically highly significant (R² = 0.92, Q_LOO² = 0.90, R_Pred² = 0.92) and mechanistically interpretable three descriptors QSAR models for 67 environmental chemicals [Alcohols, polybrominated diphenyl ethers (PBDEs), polychlorinated dibenzodioxins (PCDDs), polychlorinated biphenyls (PCBs), and polycyclic aromatic hydrocarbons (PAHs)] employing the experimental values of adipose/blood partition coefficient for human. The partitioning of chemicals into adipose tissue and blood offers information related to distribution and toxicological effects of these molecules in to the mammal system. The developed models are helpful to understand the mechanistic basis of toxicokinetic processes into the mammal system followed by risk assessment and risk management process. The applicability domain (AD) of the developed model was checked and followed by its employment to predict adipose/blood partition coefficient of 513 environmental contaminants consist of PCBs, PBDEs, PCDDs and PAHs from USA Environmental protection agency (US EPA) site.

Correlation between macroscopic characteristics and tissue-specific chemical profiling of the root of Salvia miltiorrhiza

BACKGROUND

Macroscopic identification has been widely used as a convenient method for herbal authentication and quality assessment. However, sensory evaluation heavily relied on personal experience and lacked enough evidence-based validations.

PURPOSE

We aim to reveal the correlation between macroscopic characteristics and tissue-specific chemical composition of the root of Salvia miltiorrhiza (SMR), and then develop a rapid method for quality assessment.

METHODS

Thirty-two batches of SMR were collected and evaluated. The outer-surface color and diameter as the representative tissue features of SMR were selected as the macroscopic indexes. SMR were then divided into three parts along transverse section as outer bark, middle part and central part, to explore the spatial distribution of chemicals. Outer-surface color information was converted into RGB values, while the diameter data were expressed by mean distance, respectively. Thirteen major components including eight salvianolic acids and five tanshinones in each part were determined by liquid chromatography tandem mass spectrometry. Finally, several mathematical models were established and optimized to evaluate the correlation between outer-surface color, size and chemical distribution.

RESULT

All five tanshinones mainly distributed in the outer bark while salvianolic acids were averagely existed among three parts. Correlational studies revealed that the surface color depth was significantly and positively correlated with tanshinone contents in the outer bark, while the size showed poor correlation in any chemicals. A color-oriented model was thus developed for the prediction of tanshinone contents in SMR, and a 9 × 9 standard color chart was created for easily use.

CONCLUSION

This study contributes an alternative method for macroscopic features-based quality evaluation of herbs, and also complements some scientific data for traditional knowledge.

Arterial stiffness and white matter integrity in the elderly: A diffusion tensor and magnetization transfer imaging study

BACKGROUND AND PURPOSE

The stiffness of large arteries and increased pulsatility can have an impact on the brain white matter (WM) microstructure, however those mechanisms are still poorly understood. The aim of this study was to investigate the association between central artery stiffness, axonal and myelin integrity in 54 cognitively unimpaired elderly subjects (65-75 years old).

METHODS

The neuronal fiber integrity of brain WM was assessed using diffusion tensor metrics and magnetization transfer imaging as measures of axonal organization (Fractional anisotropy, Radial diffusivity) and state of myelination (Myelin volume fraction). Central artery stiffness was measured by carotid-femoral pulse wave velocity (cfPWV). Statistical analyses included 4 regions (the corpus callosum, the internal capsule, the corona radiata and the superior longitudinal fasciculus) which have been previously denoted as vulnerable to increased central artery stiffness.

RESULTS

cfPWV was significantly associated with fractional anisotropy and radial diffusivity (p < 0.05, corrected for multiple comparisons) but not with myelin volume fraction. Findings from this study also show that improved executive function performance correlates with Fractional anisotropy positively (p < 0.05 corrected) as well as with myelin volume fraction and radial diffusivity negatively (p < 0.05 corrected).

CONCLUSIONS

These findings suggest that arterial stiffness is associated with axon degeneration rather than demyelination. Controlling arterial stiffness may play a role in maintaining the health of WM axons in the aging brain.

A Comparative Study on Selective PPAR Modulators through Quantitative Structure-activity Relationship, Pharmacophore and Docking Analyses

BACKGROUND

Metabolic syndrome is a matrix of different metabolic disorders which are the leading cause of death in human beings. Peroxysome proliferated activated receptor (PPAR) is a nuclear receptor involved in metabolism of fats and glucose.

OBJECTIVE

In order to explore structural requirements for selective PPAR modulators to control lipid and carbohydrate metabolism, the multi-cheminformatics studies have been performed.

METHODS

In silico modeling studies have been performed on a diverse set of PPAR modulators through quantitative structure-activity relationship (QSAR), pharmacophore mapping and docking studies.

RESULTS

It is observed that the presence of an amide fragment (-CONHRPh) has a detrimental effect while an aliphatic ether linkage has a beneficial effect on PPARα modulation. On the other hand, the presence of an amide fragment has a positive effect on PPARδ modulation, but the aliphatic ether linkage and substituted aromatic ring in the molecular scaffold are very much essential for imparting potent and selective PPARγ modulation. Negative ionizable features (i.e. polar fragments) must be present in PPARδ and α modulators, but a hydrophobic feature is the prime requirement for PPARγ modulation.

CONCLUSION

Here, the essential structural features have been explored for selective modulation of each subtype of PPAR in order to design new modulators with improved activity/selectivity.