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The present study deals with the application of Electrotopological state (E-State) indices in identifying structural attributes, responsible for exhibiting better antiviral activity for the NNRTI class of the HEPT derivatives. Special thrust is given on the potential of using these simple 2D QSAR models in prefiltering a large chemical database. The relevance of substituents at 1-N, 5-C and thio-aryl ring in enhancing the antiviral activity has been discussed in detail and compared with 3D structural studies. The effects of nearby atomic descriptors on 1-N and 3-N (crucial for the antiviral activity) position have been analyzed with a new insight. Application of similarity-based filtering for data mining of an external database using the best model derived was successfully applied to PubChem dataset, wherein the hits retrieved, not only showed better antiviral activity (predicted), but also complimented with the docking results. [ABSTRACT FROM AUTHOR]

Horizontal geometric characteristics have significant impact on vehicle operating speed of vehicles on two-lane rural highways. Majority of the studies have used the conventional approach of modelling, found to he location specific and provide falsejudgement of determining operating speed. Thus, it becomes important to apply methods based on artificial intelligent for predicting operating speed of vehicles on two-lane roads under mixed traffic conditions. Field data was collected on 40 different locations (curves and tangent sections) that includes free speed of vehicles and geometric parameters. Geometric parameters such as curve radius, curve length, deflection angle, degree of curvature and preceding tangent length were measured in the field with total station, whereas free-flow speed data was collected using radar gun at the mid of the horizontal curves. The statistical analysis concluded that the curve radius, curve length, degree of curvature and preceding tangent length are found significant on the operating speed of vehicle type Car, Two-wheeler, Three-wheeler, Light commercial vehicle and Heavy commercial vehicle and developed MLR model. Further, the data driven soft computing methods such as Artificial Neural Network (AN), Adaptive Neuro-fuzzy Inference System (ANFIS) and Support Vector Regression (SVR) are applied to predict the operating speed of these vehicles and results were compared with MLR. The performance of the models evaluated using various goodness-of-fit measures indicates that the SVR model gives better results in prediction of operating speed in compared to other models. As for future research, further investigation could be conducted to explore uncertainties, and the model could be enhanced by utilizing other geometric and traffic parameters, and techniques like random forest, XGBoost etc. [ABSTRACT FROM AUTHOR]

In the current work, a DWT (Discrete Wavelet Transform) was linked to ANN, MLR, and RF to develop hybrid models WANN, WMLR, and WRF, respectively, for Brahmaputra River flow forecasting. We used ten-year daily flow data from Pandu Station, which was decomposed (up to five levels) into multiresolution time series using DWT and Daubechies wavelets db1, db2, db3, db8, and db10. The predicted discharge values for multiple lead times (2, 3, 4, 7, and 14 days) have been then obtained by feeding multiresolution time series data as the input to MLR, ANN, and RF. It was discovered that the WMLR-db10 model outperformed the WANN and WRF models for all lead times. Throughout the testing phase, the values of Nash-Sutcliffe efficiency (NS) along with RMSE (shown in bracket) for the WMLR-db10 model for lead times 2, 3, 4, 7 and 14 days have been observed to be, respectively, 0.998 (415.18 m3/s), 0.998 (514.21 m3/s), 0.996 (713.62 m3/s), 0.991 (1030.83 m3/s), and 0.977 (1638.64 m3/s). Additionally, it has been observed that WANN performed better for low-order wavelets (db1, db2, db3), WMLR performed better for high-order wavelets (db8, db10), and WRF performed worst of all the wavelets. [ABSTRACT FROM AUTHOR]

Background/Objectives: Advanced calcific aortic stenosis, with or without coronary artery disease [CAD], may lead to severe systolic dysfunction. The aim of the study was to reveal clinical and laboratory parameters that may differentiate patients with severe aortic stenosis with and without systolic dysfunction. Methods: A retrospective, single-center study included all consecutive patients diagnosed with severe aortic stenosis with overt heart failure. Patients with hematological and neoplastic diseases were excluded. Demographic, clinical and laboratory data were analysed. Neutrophil-to-lymphocyte [NLR], monocyte-to-lymphocyte [MLR], and platelet-to-lymphocyte [PLR] ratios were calculated. The study group was divided based on left ventricular ejection fraction [LVEF]. Results: The final study population comprised 301 patients [133 males [44%]; median [Q1–3] age of 80 [75–83] years]. Co-morbidities included CAD [48.8%], arterial hypertension [75.4%], diabetes mellitus [n = 124, 41.2%], atrial fibrillation [39.2%], chronic kidney disease [60.8%]. Fifty-seven patients presented with LVEF ≤ 40% (heart failure with reduced ejection fraction (HFrEF)) and 244 with LVEF > 40%. In the multivariable analysis, N-terminal pro-B-type natriuretic peptide [NTproBNP] [p < 0.001, OR 1.000, 95%CI 1.000–1.000], baseline MLR [p < 0.020, OR 7.393, 95%CI 1.363–40.091] and female sex [p < 0.001, OR 0.308, 95%CI 0.160–0.593] were revealed as significant predictors of HFrEF. Baseline MLR weakly correlated with EuroScore II [Spearman r = 0.141, p = 0.015] and NTproBNP [r = 0.142, p = 0.014]. Cut-off values were established as 0.36 for MLR and 3927 pg/mL for NTproBNP. After excluding 147 patients with CAD, there was still a statistically significant difference in MLR between the subgroups [p = 0.024]. Conclusions: Increased values of MLR and NTproBNP together with female sex are predictive parameters for LVEF ≤ 40% in patients with severe aortic stenosis. [ABSTRACT FROM AUTHOR]

Objective: Inflammatory markers, particularly an elevated NLR level, are recognized indicators of systemic inflammation and have been proposed as practical tools for understanding psychosis. The primary objective of this study is to investigate blood parameters, including neutrophil, lymphocyte, and monocyte counts, as well as their derived ratios (NLR, MLR, PLR), in both affective and nonaffective psychosis. This research aims to shed light on the potential role of these biomarkers in elucidating the pathophysiological underpinnings of psychosis, offering insights that could inform diagnostic and therapeutic approaches. Materials and Methods: This retrospective case-control study focused on patients diagnosed with schizophrenia (SCH), first-episode psychosis (FEP), and bipolar disorder (BD). Digital medical records spanning from 2016 to 2021 were retrospectively analyzed. A total of 437 patients were included in the study, categorized into three groups: schizophrenia (n = 199), first-episode psychosis (n = 127), and bipolar disorder (n = 111), alongside a healthy control (HC) group (n = 200) composed of individuals without prior psychiatric disorders. Results: In this study, we observed significant increases in neutrophil and lymphocyte counts in schizophrenia (SCH) and bipolar disorder (BP) patients. Conversely, monocyte counts were significantly decreased in SCH patients compared to the control group. Notably, the neutrophil-tolymphocyte ratio (NLR) was significantly elevated in first-episode psychosis (FEP) patients compared to controls. Furthermore, no statistically significant differences were found in the monocyte-tolymphocyte ratio (MLR) and platelet-to-lymphocyte ratio (PLR) across the study groups (p > 0.05 for each comparison). Conclusion: Our study highlights elevated neutrophil-to-lymphocyte ratio (NLR) in first-episode psychosis (FEP) patients, suggesting NLR’s potential in distinguishing acute from chronic phases of psychotic disorders. However, we found no significant NLR differences between bipolar disorder (BP) patients and controls, contrary to some previous studies. Further prospective research is needed to understand the role of inflammatory markers in psychiatric disorders fully. [ABSTRACT FROM AUTHOR]

The stability of strip footings subjected to eccentrically inclined loads is critical for reliable foundation design. This study investigates the effect of a circular unlined tunnel in a rock mass on the ultimate bearing capacity (UBC) of a foundation with width B under inclined and eccentric loads. Adaptive finite element limit analysis was employed to evaluate the reduction in UBC of the footing resting above a tunnel. The examined critical parameters include normalized load eccentricity (e/B), load inclination (β), and horizontal and vertical distances of the tunnel from the foundation (P/B and Q/B, respectively), along with rock mass properties. The results reveal that for e/B ≥ 0.25 and β ≤ 60°, the reduction coefficient, Rc ≥ 0.90, suggesting that the presence of a tunnel has a minimal impact on the load-bearing capacity of the footing, with failure primarily governed by load eccentricity and inclination. Additionally, potential failure mechanisms are explored, showing that at lower e/B, higher β, and lower Q/B, the tunnel significantly affects footing's failure envelope. Conversely, at higher e/B and lower β, failure is due to rotational effects of footing, regardless of the tunnel's position. To predict the Rc more accurately, due to the time-consuming nature of direct calculations, both MLR and ANN models were developed. The MLR model provided a baseline for comparison, while the ANN model, with a coefficient of determination (R2) of 0.98, demonstrated superior accuracy compared to the R2 = 0.96 of MLR. Using both approaches ensured robust and efficient predictions of Rc. Since Rc does not directly provide the reduced UBC of footing due to presence of tunnel, the study introduced bearing capacity factor (Nc) to enable direct calculation of the reduced UBC of footing. These findings offer theoretical guidelines for preliminary design and provide practitioners with an effective tool for evaluating UBC reduction in complex loading scenarios involving tunnels. [ABSTRACT FROM AUTHOR]

Objectives: Chronic Kidney Disease (CKD) is a long-standing metabolic disease manifested by renal impairment, high morbidity and mortality, and causing a huge financial burden. Systemic inflammation and local intrarenal inflammation are found to exacerbate this irreversible condition. White blood cells, platelets, and their derived indices may aid in the assessment of the progression of CKD. This study aimed to assess the alterations of complete blood count and their derived indices in the various stages of chronic kidney disease. Methods: The retrospective cross-sectional study was conducted in the Department of Biochemistry at a tertiary care hospital, Chennai, India. The data were collected from the Medical Records Department from July 2022 to June 2023. The study included chronic kidney disease patients aged 35 to 70 years of both genders. Children, pregnant women, and patients with heart and liver diseases were excluded. The data of the renal profile and complete blood count were collected. Statistical analysis was performed using SPSS software version 16. A p≤0.05 was considered statistically significant. Results: Among the study participants, 65% were male and were more than 50 years of age. All the derived in-flammation index parameters, such as neutrophil-to-lymphocyte ratio (NLR), monocyte-to-lymphocyte ratio (MLR), platelet-to-lympho- cyte ratio (PLR), systemic immune-inflammation index (SII), and systemic inflammation response index (SIRI). were significantly increased in stage 5 of CKD. Also, SII and SIRI were found to be correlated with other inflammatory variables. Conclusion: Chronic inflammation is considered to be prevalent among CKD patients. Inflammatory markers such as SII and SIRI are simple and cost-effective parameters to routinely assess the staging of CKD and thus initiate appropriate management to improve the quality of life. [ABSTRACT FROM AUTHOR]

PD-1 blockade therapy has revolutionized melanoma treatment, but still not all patients benefit and pre-treatment identification of those patients is difficult. Increased expression of inflammatory markers such as interleukin (IL)-6 in blood of patients correlates with poor treatment response. We set out to study the effect of inflammatory cytokines on PD-1 blockade in vitro. For this, we studied the effect of IL-6 and type I interferon (IFN) in vitro on human T cells in a mixed leukocyte reaction (MLR) in the absence or presence of PD-1 blockade. While IL-6 reduced IFN-γ secretion by T cells in both the presence and absence of PD-1 blockade, IFN-α specifically reduced the IFN-γ secretion only in the presence of PD-1 blockade. IFN-α reduced T cell proliferation independent of PD-1 blockade and reduced the percentage of cells producing IFN-γ only in the presence of PD-1 blockade. Next we determined the type I IFN score in a cohort of 22 melanoma patients treated with nivolumab. In this cohort, we did not find a correlation between clinical response and type I IFN score, nor between clinical response and IFN-γ secretion in vitro in a MLR in the presence of PD-1 blockade. We conclude that IFN-α reduces the effectiveness of PD-1 blockade in vitro, but that in this cohort, type I IFN score in vivo, nor IFN-γ secretion in vitro in a MLR in the presence of PD-1 blockade correlated to decreased therapy responses in patients. [ABSTRACT FROM AUTHOR]

Background Neonatal sepsis is a major cause of neonatal morbidity and mortality, especially in developing countries. Atypical clinical symptoms lead to delays in diagnosis and treatment. Scoring a combination of routine hematology parameters may be able to predict the occurrence of sepsis in preterm neonates. Objective To formulate a new model for neonatal sepsis scoring from various complete blood count parameters to predict sepsis in preterm neonates. Methods This analytical cross sectional study using secondary data from the Registry of the Neonatology Division was conducted at the RSUP Dr. Hasan Sadikin, Bandung, West Java. Subjects were neonates diagnosed with sepsis, of gestational age 28–36 weeks, who were born at the RSUP Dr. Hasan Sadikin from January to December 2021. Laboratory results of patients who met the inclusion criteria were recorded. Subjects were divided into either proven sepsis and probable sepsis groups, based on blood culture results. Results Of 112 subjects, 35.7% had proven sepsis and 64.3% probable sepsis. In the proven sepsis group, 52.5% of subjects were male, median birth weight was 1,490 grams, median gestational age was 32 weeks, 90% were small for gestational age, and 60% were delivered normally. Multivariable analysis by multiple logistic regression revealed that the parameters associated with the incidence of neonatal sepsis were c-reactive protein (CRP) > 0.18 mg/dL (score 6), hematocrit 10 (score 2). Score >8 had a sensitivity of 85% and specificity of 70.8%, with area under the ROC curve of 0.865 (P8 can be used as a predictor of sepsis in preterm neonates.

The hazelnut possesses a significant economic value and is extensively consumed on a global scale. Physico-mechanical properties such as linear dimensions, deformation, force, stress, and energy play an important role in the processing of hazelnut and hazelnut kernels, quality assessment, and the development of harvesting and post-harvest technologies. The data used in the data set was determined by applying compression tests and artificial neural networks, support vector regression, and multiple linear regression methods were applied to the data obtained. The aim of the study ws to determine the deformation energy of hazelnuts and hazelnut kernels based on some mechanical properties of hazelnuts using nondestructive machine learning methods instead of traditional measurement methods with minimum error, minimum labor, and in the shortest time. The average R2 for kernels and hazelnuts was ANN 95.2%, SVR 89.6%, and MLR 86.1%. The average MSE for kernels and hazelnuts was ANN 0.006, SVR 0.012, and MLR 0.072. The machine learning methods used in the study provided results close to the ideal statistical metrics. According to the analyses of the machine learning methods, results similar to the optimal statistical metrics were obtained. The most successful and least-error methods were the artificial neural network, support vector regression and multiple linear regression, respectively.

The hazelnut possesses a significant economic value and is extensively consumed on a global scale. Physico-mechanical properties such as linear dimensions, deformation, force, stress, and energy play an important role in the processing of hazelnut and hazelnut kernels, quality assessment, and the development of harvesting and post-harvest technologies. The data used in the data set was determined by applying compression tests and artificial neural networks, support vector regression, and multiple linear regression methods were applied to the data obtained. The aim of the study ws to determine the deformation energy of hazelnuts and hazelnut kernels based on some mechanical properties of hazelnuts using nondestructive machine learning methods instead of traditional measurement methods with minimum error, minimum labor, and in the shortest time. The average R2 for kernels and hazelnuts was ANN 95.2%, SVR 89.6%, and MLR 86.1%. The average MSE for kernels and hazelnuts was ANN 0.006, SVR 0.012, and MLR 0.072. The machine learning methods used in the study provided results close to the ideal statistical metrics. According to the analyses of the machine learning methods, results similar to the optimal statistical metrics were obtained. The most successful and least-error methods were the artificial neural network, support vector regression and multiple linear regression, respectively. [ABSTRACT FROM AUTHOR]

The speed variation along the successive highway sections is one of the most important factors in assessing geometric design consistency. Therefore, it is necessary to predict operating speed on important highway geometric features involving major safety issues. The present study aims to develop operating speed on the curve and tangent section of four-lane divided highways. For this research, the data is collected on 44 highway sections in India, including 22 curved and 22 tangent sections. The geometric features and free-flow speeds of various vehicles were collected. This study analyzed the speed profiles of vehicles that follow different statistical distribution patterns other than normal distribution. Multiple linear regression (MLR) and Artificial Neural Network (ANN) techniques are adopted to develop operating speed models on curve and tangent sections. The variables like curve radius, curve length and deflection angle are identified as most significant for modelling operating speed on the curves. Similarly, the shoulder width, median width, and access density are found to influence the operating speed on tangent sections. The developed models are successfully validated with field data. The performance measures such as RMSE and MAPE are applied to check degree of accuracy of developed models. The results revealed that the ANN models perform better than MLR models in curved and tangent sections. The developed models are helpful for highway and traffic engineers in establishing posted speed limits on critical sections of highways.

Neutrophil-to-lymphocyte ratio (NLR), platelet-to-lymphocyte ratio (PLR), monocyte-to-lymphocyte ratio (MLR), systemic immune-inflammation index (SII), and systemic inflammatory response index (SIRI) are widely used to assess patient’s outcome in many types of neoplasms including breast cancer. The aim of this study was to select the best parameter with a high prognostic value by defining the optimal cutoff value for each parameter in patients undergoing mastectomy.

Abstract Objective Exploring the predictive value of NLR, PLR, MLR, and SII for the severity of cervical cancer screening abnormalities in patients. Methods A retrospective analysis was conducted on the data of 324 patients suspected of cervical lesions due to abnormal TCT and/or HPV in our hospital from January 2023 to December 2023, who underwent colposcopy. The pathological results of colposcopic biopsy confirmed that there were 140 cases of chronic cervicitis, which classified as the group without cervical lesions. The cervical lesion group included 184 cases, including 91 cases of LSIL, 71 cases of HSIL, and 22 cases of cervical cancer. Compared the differences in preoperative peripheral blood NLR, PLR, MLR, and SII among different groups of patients, and evaluated their predictive value for the severity of cervical lesions using Receiver Operating Characteristic (ROC) curves. Results The levels of NLR, PLR, and SII in the group without cervical lesions were lower than those in the group with cervical lesions (p 0.05). The comparison of NLR among LSIL, HSIL, and cervical cancer groups showed statistically significant differences (p 0.05). The AUC of peripheral blood NLR, PLR, and SII for predicting cervical lesions were 0.569, 0.582, and 0.572, respectively. The optimal cutoff values were 2.3,176.48, and 603.56. The sensitivity and specificity were 38.6% and 73.6%, 28.8% and 85.7%, 37.5% and 76.4%, respectively. At the same time, the joint testing of the three had the highest efficiency, with sensitivity of 69% and specificity of 45%. Conclusion Although the peripheral blood NLR, PLR, and SII of the cervical lesions patients were higher than those without cervical lesions in cervical cancer screening abnormal patients, the predictive ROC curve discrimination was low. Therefore, it is not recommended to use preoperative peripheral blood inflammatory markers as markers for cervical cancer screening abnormal patient diversion.

The analysis of hematological parameters in premature infants with clinical signs of perinatal pathology is important and allows to assess the adaptive response of the body, especially under the conditions of negative effects of hypoxia. Almost half of all preterm births are caused by inflammatory processes in the mother leading to rupture of membranes with or without chorioamnionitis. Due to the functional immaturity of organ systems and regulatory mechanisms, preterm infants have certain problems of adaptation in the neonatal period. Disruption of a balanced adaptive response to changing environmental conditions due to dysregulation of immune defense and immunosuppression is the basis for the initiation of pathophysiological mechanisms of hypoxic stress and damage to the body, starting from the subcellular level. This causes severe dysmetabolic changes with the formation of serious diseases with a high risk of mortality and the formation of long-term consequences of pathology in survivors, reducing the quality of life of patients in later years. Materials and methods. We studied 68 infants with a gestational age of 32 to 33/6 weeks who had clinical signs of perinatal pathology (main study group). Subgroup A included 35 newborns with severe disease, and subgroup B included 33 children whose general condition was considered moderate. The comparison group consisted of 31 infants with a gestational age of 34 to 36/6 weeks (control group). Inclusion criteria were confirmed clinical signs of moderate and severe perinatal pathology; exclusion criteria were gestational age at birth less than 32 weeks and 37 weeks or more, congenital malformations. A comprehensive clinical and laboratory examination of newborns was performed, considering the main and concomitant pathology. Based on the results of the complete blood count, the ratio of indicators was calculated: NLR (neutrophil to lymphocyte ratio), PLR (platelet to lymphocyte ratio), MLR (monocyte to lymphocyte ratio); as well as SII (systemic immune inflammation index); SIRI (systemic inflammation response index); PIV (platelet, neutrophil, monocyte and lymphocyte index). Taking into account the data obtained, the risk of deterioration of newborns’ condition in relation to perinatal risk factors was identified, the limits of indicators and indices of systemic hypoxic inflammation in relation to moderate and severe pathology were determined. Scientific research was conducted in accordance with the basic provisions of GCP (1996), the Convention of the Council of Europe on Human Rights and Biomedicine (April 4, 1997), the Declaration of Helsinki of the World Medical Association on the Ethical Principles for Research on Human Subjects (1964-2008), Order of the Ministry of Health of Ukraine No. 690 dated September 23, 2009 (as amended by Order of the Ministry of Health of Ukraine No. 523 dated July 12, 2012). Protocol of scientific research of the Biomedical Ethics Commission of BSMU dated September 12, 2015. Considering the complex subject of the research work, the informed consent of the parents of newborns was obtained after familiarization with the purpose, objectives and planned methods of laboratory and functional studies. Statistical processing of results was performed using STATISTICA software (StatSoft Inc., USA, version 10). Comparison of quantitative indicators with normal distribution was performed using Student’s t-test, and the probability of differences was considered statistically significant at p<0.001. Results of the study. The results of the study showed that in order to determine the severity of the general condition in premature infants, along with the assessment of clinical signs of pathology, it is advisable to conduct a detailed analysis of the results of a complete blood count with the study of the ratio of individual indicators and the determination of systemic inflammation indices. This allows to improve the approaches to differential diagnosis of the severity of neonatal diseases by using a low-cost conventional method, while obtaining more correct conclusions. The indicators of complete blood count in newborns confirm the presence of compensatory activation of erythroid, myeloid and granulocytic hematopoiesis in response to hypoxia in the early neonatal period. The obtained data showed significant differences from the control values, which correlated with the severity of the newborns’ condition. Reserves for improving the effectiveness of medical care for IPA should include the improvement of diagnostic measures to clarify the pathophysiological mechanisms of hypoxic inflammation in order to reduce mortality and prevent the development of long-term consequences of the pathology, while maintaining the health and quality of life of patients. Conclusions: 1. Determination of the ratio of GBT and systemic inflammatory indices can be a qualitative adjunct to diagnosis to clarify the pathophysiological mechanisms of hypoxic damage to the body and to objectify approaches to assessing the severity of the condition of newborns with various forms of perinatal pathology. 2. The risk of worsening in children with development of moderate pathology should be predicted at the following limits of the ratio of indicators: an increase in NLR from 0.295 to 0.343, a decrease in PLR from 7.982 to 7.290 and MLR from 0.096 to 0.093; an increase in systemic inflammation indices: SII – from 66.546 to 76.386, SIRI – from 0.802 to 0.975, PIV – from 180.825 to 225.130. 3. Severe course of diseases in newborns is predicted on the basis of ratio of indicators: NLR – up to 0.238, PLR – up to 5.923, MLR – up to 0.086 and the presence of systemic inflammation indices: SII – up to 51.230, SIRI – up to 0.745 and PIV – up to 160.310. [ABSTRACT FROM AUTHOR]

Background: Abortion is defined as the termination of pregnancy (TOP) before the viability of the foetus. Objective: This study aimed to evaluate the association of combination of platelet (PLT) to lymphocyte ratio (PLR), monocyte to lymphocyte ratio (MLR) and neutrophil to lymphocyte ratio (NLR) as markers for predicting missed abortion in 1st trimester. Patients and methods: This cross-sectional study was conducted on 92 women who were divided into two groups: group (A) included 46 women diagnosed with missed abortion in the first trimester and group (B) included 46 women with normal pregnancies in the first trimester as the control group. This study was conducted in Department of Gynecology and Obstetrics, Mansoura University Hospitals, Mansoura University, Egypt, from July 2021 to December 2023. Results: Combination of PLR, MLR and NLR had sensitivity (Sn) and specificity (Sp) of 78.3% and 73.9% respectively to diagnose missed abortion (Area under curve (AUC) = 0.820). A significant difference was recorded between both groups concerning lymphocytes, monocytes, PLR, MLR and NLR. Conclusion: The dependence of PLR, MLR, and NLR on the prediction of missed abortion isn't certain because of moderate sensitivity and specificity of these ratios. However, combination of two ratios led to higher increase in sensitivity, specificity and accuracy in predicting missed abortion than using one ratio. Combination of the three ratios had higher sensitivity and specificity in predicting missed abortion than using two ratios. [ABSTRACT FROM AUTHOR]

This research aims to determine the brittleness index (BI) and engineering properties of limestone specimens. In addition, this study evaluates the effect of moisture on the developed models to predict the BI and shear wave velocity (Vs), based on the point load index (Is50), dry and saturated tensile strength (Ts-d and Ts-s), and porosity. Gaussian process regression (GPR), multilayer feedforward neural network (MFFNN), and multiple linear regression (MLR) predictive models were utilized. Microscopic examination of the limestone specimens revealed that calcite is the predominant mineral. It was observed that samples with higher calcite content exhibited greater brittleness and strength properties while having lower porosity. The results obtained from the MLR analysis demonstrated that it is possible to accurately forecast the brittleness index (BI), as well as the dry and saturated shear wave velocities (Vs-d and Vs-s) at the specific sites under investigation. The moisture effect on developed models showed that Vs prediction in dry conditions (Vs-d) was less accurate compared to the saturated conditions (Vs-s). Conversely, the relationships developed for estimating the BI in dry conditions exhibited higher accuracy. The analysis of all model assumptions using MLR indicated that the models could be reliably utilized. However, the MFFNN and GPR methods were found to be more conservative in estimating these properties. Moreover, the study identified the best transfer function and training algorithm for predicting Vs and BI. The evaluation metrics, such as R² and RMSE revealed that GPR demonstrated higher precision compared to MFFNN and MLR. [ABSTRACT FROM AUTHOR]

Acetylcholinesterase (AChE) is one of the main drug targets for treating Alzheimer's disease. This current study relies on multiple molecular modeling approaches to develop new potent inhibitors of AChE. We explored a 2D QSAR study using the statistical method of multiple linear regression based on a set of substituted 5-phenyl-1,3,4-oxadiazole and N-benzylpiperidine analogs, which were recently synthesized and proved their inhibitory activities against acetylcholinesterase (AChE). The molecular descriptors, polar surface area, dipole moment, and molecular weight are the key structural properties governing AChE inhibition activity. The MLR model was selected based on its statistical parameters: R2 = 0.701, R2test = 0.76, Q2CV = 0.638, and RMSE = 0.336, demonstrating its predictive reliability. Randomization tests, VIF tests, and applicability domain tests were adopted to verify the model's robustness. As a result, 11 new molecules were designed with higher anti-Alzheimer's activities than the model molecule. We demonstrated their improved pharmacokinetic properties through an in silico ADMET study. A molecular docking study was conducted to explore their AChE inhibition mechanisms and binding affinities in the active site. The binding scores of compounds M1, M2, and M6 were (−12.6 kcal/mol), (−13 kcal/mol), and (−12.4 kcal/mol), respectively, which are higher than the standard inhibitor Donepezil with a binding score of (−10.8 kcal/mol). Molecular dynamics simulations over 100 ns were used to validate the molecular docking results, indicating that compounds M1 and M2 remain stable in the active site, confirming their potential as promising anti-AChE inhibitors. [ABSTRACT FROM AUTHOR]

Objective: Exploring the predictive value of NLR, PLR, MLR, and SII for the severity of cervical cancer screening abnormalities in patients. Methods: A retrospective analysis was conducted on the data of 324 patients suspected of cervical lesions due to abnormal TCT and/or HPV in our hospital from January 2023 to December 2023, who underwent colposcopy. The pathological results of colposcopic biopsy confirmed that there were 140 cases of chronic cervicitis, which classified as the group without cervical lesions. The cervical lesion group included 184 cases, including 91 cases of LSIL, 71 cases of HSIL, and 22 cases of cervical cancer. Compared the differences in preoperative peripheral blood NLR, PLR, MLR, and SII among different groups of patients, and evaluated their predictive value for the severity of cervical lesions using Receiver Operating Characteristic (ROC) curves. Results: The levels of NLR, PLR, and SII in the group without cervical lesions were lower than those in the group with cervical lesions (p < 0.05), and there was no statistically significant difference in MLR (p > 0.05). The comparison of NLR among LSIL, HSIL, and cervical cancer groups showed statistically significant differences (p < 0.05), while PLR, MLR, and SII showed no statistically significant differences (p > 0.05). The AUC of peripheral blood NLR, PLR, and SII for predicting cervical lesions were 0.569, 0.582, and 0.572, respectively. The optimal cutoff values were 2.3,176.48, and 603.56. The sensitivity and specificity were 38.6% and 73.6%, 28.8% and 85.7%, 37.5% and 76.4%, respectively. At the same time, the joint testing of the three had the highest efficiency, with sensitivity of 69% and specificity of 45%. Conclusion: Although the peripheral blood NLR, PLR, and SII of the cervical lesions patients were higher than those without cervical lesions in cervical cancer screening abnormal patients, the predictive ROC curve discrimination was low. Therefore, it is not recommended to use preoperative peripheral blood inflammatory markers as markers for cervical cancer screening abnormal patient diversion. [ABSTRACT FROM AUTHOR]

INTRODUCTION: Avascular necrosis of the femoral head (AVNFH) is an osteonecrosis type caused by ischaemic osteocyte loss of femoral head, and its exact pathomechanism is still unknown. Neutrophil, lymphocyte, monocyte, platelet levels in complete blood count and ratios between these levels have been used by almost all medical disciplines as accesible and reliable biomarkers of immune response. Aim of this study is to identify the effects of neutrophil/lymphocyte (NL), monocyte/lymphocyte (ML), platelet/lymphocyte (PLT/L) ratios on prognosis and stage in patients with avascular necrosis of the femoral head (AVNFH). MATERIALS AND METHODS: A total of 106 (30 female; 76 male) patients aged 18 and over diagnosed with avascular necrosis of femoral head between 2012-2022 years were retrospectively evaluated. Study was planned after a total of 106 (30 female, 76 male) healthy patients with consent to participate who were demographically equal to the study group were included in the control group. Patients in the study group were divided into 3 groups as Stage I, II and III according to the Ficat-Arlet classification. RESULTS: In terms of neutrophil counts; neutrophil values of study and control groups were 4.94±1.89 and 4,21±1,17; respectively. There was statistically significant difference between counts (p<0.05). In terms of neutrophil/lymphocyte ratio, NL ratio was statistically significantly higher in study group (2.11±0.85) than control group (1.75±0.44). Cut-off value of NL ratio was 2.13 according to the ROC analysis (sensitivity 47.17% (95% CI (37.4-57.1)); specificity=84.91% 95% GA (76.6-91.1)). Sensitivity and specificity of cut-off value was statistically significant. There was no difference between groups created according to Ficat-Arlet in terms of hemogram parameters. DISCUSSION: NL may indicate AVNFH; however, other parameters are considered as inadequate for identifying an independent marker in AVNFH due to ineffective immune response. Future studies with larger samples which allow standard and multi-dimensional analysis are needed (Tab. 4, Fig. 5, Ref. 20). Text in PDF www.elis.sk [ABSTRACT FROM AUTHOR]

The validation principles on Quantitative Structure Activity Relationship issued by Organization for Economic and Co‐operation and Development describe three criteria of model assessment: goodness of fit, robustness and prediction. In the case of robustness, two ways are possible as internal validation: bootstrap and cross‐validation. We compared these validation metrics by checking their sample size dependence, rank correlations to other metrics and uncertainty. We used modeling methods from multivariate linear regression to artificial neural network on 14 open access datasets. We found that the metrics provide similar sample size dependence and correlation to other validation parameters. The individual uncertainty originating from the calculation recipes of the metrics is much smaller for both ways than the part caused by the selection of the training set or the training/test split. We concluded that the metrics of the two techniques are interchangeable, but the interpretation of cross‐validation parameters is easier according to their similar range to goodness‐of‐fit and prediction metrics. Furthermore, the variance originating from the random elements of the calculation of cross‐validation metrics is slightly smaller than those of bootstrap ones, if equal calculation load is applied. [ABSTRACT FROM AUTHOR]

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Background: Heart failure (HF) and inflammation have a bidirectional relation leading to activation and adaptation of multiple cellular lines, including leucocyte subtypes and platelets. We aimed to assess and compare the predictive value of the neutrophil–lymphocyte (NLR), monocyte-lymphocyte (MLR) and platelet-lymphocyte (PLR) ratios for all-cause long-term mortality in HF. Methods: This is an observational retrospective cohort study that included patients from the HI-HF cohort that survived the initial hospitalization. Vital status and survival time were assessed in June 2020. Results: We analyzed 1018 HF patients with a mean age of 72.32 ± 10.29 years and 53.54 % women. All-cause long-term mortality was 38.21 % after a median follow-up time of 68 [38 – 82] months. NLR (AUC 0.667, 95 %CI 0.637 – 0.697), MLR (AUC 0.670, 95 %CI 0.640 – 0.700) and PLR (AUC 0.606, 95 %CI 0.574 – 0.636) were predictors of all-cause mortality. In multivariable Cox proportional hazards analysis, NLR≥3.56 was the only hematological index independent predictor of fatality (HR 1.36, 95 %CI 1.05 – 1.76). Conclusions: Of the three hematological indices, NLR was the only independent predictor of all-cause long-term mortality of HF patients. We suggest NLR≥3.56 as an auxiliary prognostic biomarker for the evaluation of HF patients.

The incomplete combustion of fossil fuels results in the emission of soot, a carbonaceous, solid fine powder that causes harm to human health and the environment. This study compares multiple linear regression (MLR) with three different machine learning (ML) models for predicting the threshold sooting index (TSI), a commonly employed index for measuring the sooting propensity of fuels. The dataset used for model development consists of experimental TSI data for 342 fuels, including various chemical classes, including oxygenated components like ethers and alcohols. Ten input features were employed, comprising eight functionalities, molecular weight, and the branching index (BI). These parameters used as input features have been demonstrated to affect fuels' physical and thermochemical properties. The ML models employed in this study are support vector regression with Nu parameter (NuSVR), extra trees regression (ETR), and extreme gradient boosting regression (XGBR). The models were trained, validated, and tested using randomly split datasets, with 56 % for training, 14 % for validation, and 30 % for testing. The accuracy of the MLR, NuSVR, ETR, and XGBR models for the entire dataset was 91 %, 96 %, 98 %, and 96 %, respectively. The mean absolute errors (MAE) of prediction were 3.4, 0.022, 0.011, and 0.028 for MLR, NuSVR, ETR, and XGBR respectively. These results highlight the effectiveness of the ML models in making predictions, with error levels similar to the uncertainties observed in experimental measurements. The developed ML models have been validated to ensure generalizability and can be used to predict petroleum fuels' TSI.

Chuang Cai,1,&ast; Wentan Zeng,2,&ast; Hongwei Wang,3 Shuqi Ren2 1Cancer Research Institute of Zhongshan City, Zhongshan City People’s Hospital, Zhongshan City, Guangdong Province, People’s Republic of China; 2Department of Laboratory Medicine, Tanzhou People’s Hospital of Zhongshan, Zhongshan City Hospital of Integration of TCM & Western Medicine, Zhongshan City, Guangdong Province, People’s Republic of China; 3Department of Pediatrics, Tanzhou People’s Hospital of Zhongshan, Zhongshan City hospital of integration of TCM & western medicine, Zhongshan City, Guangdong Province, People’s Republic of China&ast;These authors contributed equally to this workCorrespondence: Shuqi Ren, Department of Laboratory Medicine, Tanzhou People’s Hospital of Zhongshan, Zhongshan City hospital of integration of TCM & western medicine, No. 10 Dexiu road, Zhongshan City, Guangdong Province, People’s Republic of China, Tel +8615521067602, Fax +860760-23639928, Email shuchijen@163.com Chuang Cai, Cancer Research Institute of Zhongshan City, Zhongshan City People’s Hospital, No. 2 Sunwen East Road, Zhongshan City, Guangdong Province, 528404, People’s Republic of China, Tel +86 18826431030, Fax +86 0760-88823566, Email caich6@foxmail.comPurpose: Hierarchical management is advocated in China to effectively manage chronic obstructive pulmonary disease (COPD) patients and reduce the incidence and mortality of acute exacerbation of COPD (AE-COPD). However, primary and community hospitals often have limited access to advanced equipment and technology. Complete blood count (CBC), which is commonly used in these hospitals, offers the advantages of being cost-effective and easily accessible. This study aims to evaluate the significance of routine blood indicators in aiding of diagnosing AE-COPD.Patients and Methods: In this research, we enrolled a total of 112 patients diagnosed with AE-COPD, 92 patients with stable COPD, and a control group comprising 60 healthy individuals. Clinical characteristics, CBC parameters, and serum CRP levels were collected within two hours. To assess the associations between NLR/PLR/MLR and CRP by Spearman correlation test. The diagnostic accuracy of NLR, PLR and MLR in AE-COPD was assessed using Receiver Operating Characteristic Curve (ROC) and the area under the curve (AUC). Binary Logistic Regression analysis was conducted for the indicators of NLR, PLR and MLR.Results: We found that patients with AE-COPD had significantly higher levels of NLR, PLR and MLR in contrast to patients with stable COPD. Additionally, the study revealed a noteworthy correlation between CRP and NLR (rs=0.5319, P< 0.001), PLR (rs=0.4424, P< 0.001), and MLR (rs=0.4628, P< 0.001). By utilizing specific cut-off values, the amalgamation of NLR, PLR and MLR augmented diagnostic sensitivity. Binary logistic regression analysis demonstrated that heightened NLR and MLR act as risk factors for the progression of AE-COPD.Conclusion: The increasing levels of NLR, PLR and MLR could function as biomarkers, akin to CRP, for diagnosis and assessment of acute exacerbations among COPD patients. Further research is required to validate this concept. Keywords: acute exacerbation of chronic obstructive pulmonary disease, AE-COPD, neutrophil-lymphocyte ratio, NLR, platelet-lymphocyte ratio, PLR, monocyte-lymphocyte ratio, MLR

Abstract Objective To explore the application of neutrophils to lymphocytes ratio (NLR), platelet to lymphocyte ratio (PLR) and monocyte to lymphocyte ratio (MLR) in the differential diagnosis of benign uterine diseases and endometrial malignant tumors. Methods 80 patients with endometrial malignant tumor diagnosed in our hospital from January 2019 to December 2022 were selected as the study group, and 74 patients with benign uterine diseases confirmed by pathology in our hospital during the same period were randomly selected as the control group. The differences of NLR, PLR and MLR in the peripheral blood of patients in each group were compared, and the value of individual indicators in the diagnosis of endometrial malignant tumor was evaluated using the Receiver Operating Characteristic (ROC) curve. Results In peripheral blood, the NLR, PLR and MLR value in patients with endometrial cancer (EC) were significantly higher than those in patients with benign uterine diseases (P

Background Diagnosis of tuberculosis (TB) in resource-limited countries relies primarily on bacteriological confirmation using Ziehl-Neelsen (ZN) stain or culture. However, this method has low sensitivity due to suboptimal sampling and techniques. Neutrophils, monocytes, and lymphocytes are crucial in the pathogenesis of granulomatous inflammation and immune reactions. We investigated the usefulness of the haematological parameters and their ratios, like the Neutrophil to Lymphocyte ratio (NLR) and Monocyte to Lymphocyte ratio (MLR), for diagnosing tuberculosis. Methods We retrospectively grouped 114 patients with fever into those diagnosed with TB and control groups. We obtained their haematological data and calculated their derived ratios. The ratios obtained from the two groups were compared. Their sensitivity and specificity were calculated. Results Haematological parameters like MLR were higher in TB patients than in the control group. Although NLR was not significantly increased, MLR was significantly increased with p values Conclusion Serum NLR and MLR emerge as valuable tools in TB diagnosis. Their simplicity and cost-effectiveness render them particularly suitable for screening and recurrence monitoring in rural and remote settings, thereby mitigating loss to follow-up.

The speed variation along the successive highway sections is one of the most important factors in assessing geometric design consistency. Therefore, it is necessary to predict operating speed on important highway geometric features involving major safety issues. The present study aims to develop operating speed on the curve and tangent section of four-lane divided highways. For this research, the data is collected on 44 highway sections in India, including 22 curved and 22 tangent sections. The geometric features and free-flow speeds of various vehicles were collected. This study analyzed the speed profiles of vehicles that follow different statistical distribution patterns other than normal distribution. Multiple linear regression (MLR) and Artificial Neural Network (ANN) techniques are adopted to develop operating speed models on curve and tangent sections. The variables like curve radius, curve length and deflection angle are identified as most significant for modelling operating speed on the curves. Similarly, the shoulder width, median width, and access density are found to influence the operating speed on tangent sections. The developed models are successfully validated with field data. The performance measures such as RMSE and MAPE are applied to check degree of accuracy of developed models. The results revealed that the ANN models perform better than MLR models in curved and tangent sections. The developed models are helpful for highway and traffic engineers in establishing posted speed limits on critical sections of highways. [ABSTRACT FROM AUTHOR]

Background: Periprosthetic joint infection (PJI) is a devastating complication that develops after total joint arthroplasty (TJA), whose incidence is expected to increase over the years. Traditionally, surgical treatment of PJI has been based on algorithms, where early infections are preferably treated with debridement, antibiotics, and implant retention (DAIR) and late infections with two-stage revision surgery. Two-stage revision is considered the "gold standard" for treatment of chronic prosthetic joint infection (PJI) as it enables local delivery of antibiotics, maintenance of limb-length and mobility, and easier reimplantation. Many studies have attempted to identify potential predicting factors for early diagnosis of PJI, but its management remains challenging. In this observational retrospective study, we investigated the potential role of inflammatory blood markers (neutrophil-to-lymphocyte ratio (NLR), monocyte-to-lymphocyte ratio (MLR), platelet-to-lymphocyte ratio (PLR), systemic inflammatory index (SII), systemic inflammatory response index (SIRI), and aggregate index of systemic inflammation (AISI)) as prognostic factors in two-stage exchange arthroplasty for PJI. Methods: A single-center retrospective analysis was conducted, collecting clinical data and laboratory parameters from patients submitted to prosthetic explantation (EP) for chronic PJI. Laboratory parameters (PCR, NLR, MLR, PLR, SIRI, SII, and AISI) were evaluated at the explantation time; at 4, 6, and 8 weeks after surgery; and at reimplantation time. The correlation between laboratory parameters and surgery success was evaluated and defined as infection absence/resolution at the last follow-up. Results: A total of 57 patients with PJI were evaluated (62% males; average age 70 years, SD 12.14). Fifty-three patients with chronic PJI were included. Nine patients underwent DAIR revision surgery and chronic suppressive therapy; two patients died. Nineteen patients completed the two-stage revision process (prosthetic removal, spacer placement, and subsequent replanting). Among them, none showed signs of reinfection or persistence of infection at the last available follow-up. The other twenty-three patients did not replant due to persistent infection: among them, some (the most) underwent spacer retention; others (fewer in number) were submitted to resection arthroplasty and arthrodesis (Girdlestone technique) or chronic suppressive antibiotic therapy; the remaining were, over time, lost to follow-up. Of the patients who concluded the two-stage revision, the ones with high SIRI values (mean 3.08 SD 1.7 and p-value 0.04) and MLR values (mean 0.4 SD 0.2 and p-value 0.02) at the explantation time were associated with a higher probability of infection resolution. Moreover, higher variation in the SIRI and PCR, also defined, respectively, as delta-SIRI (mean −2.3 SD 1.8 and p-value 0.03) and delta-PCR (mean −46 SD 35.7 and p-value 0.03), were associated with favorable outcomes. Conclusions: The results of our study suggest that, in patients with PJI undergoing EP, the SIRI and MLR values and delta-SIRI and delta-PCR values could be predictive of a favorable outcome. The evaluation of these laboratory indices, especially their determination at 4 weeks after removal, could therefore help to determine which patients could be successfully replanted and to identify the best time to replant. More studies analyzing a wider cohort of patients with chronic PJI are needed to validate the promising results of this study. [ABSTRACT FROM AUTHOR]

AbstractThe removal of Acid dyes (Acid Orange 52, AO-52 and Acid Red 87, AR-87) from aqueous solutions in a batch process using Magnetic modified corncob biomass (Corncob@Fe3O4) is reported. The effects of factors such as dyes concentrations, contact time, adsorbent dosage and temperature were investigated. The kinetics and equilibrium data were modeled appropriately while Response Surface Methodology (RSM), Artificial Neural Network (ANN) as well as multiple linear regression (MLR) based on Box–Behnkem design of experiment were used to model the combined effect of the factors. The thermodynamics parameters were obtained from van’t Hoff expression. Although all the kinetic models fitted the data, first order kinetic model best fitted the data with highest R2 and lowest Reduced Chi square (χ2). The monolayer adsorption capacities from Langmuir model are 47.85 and 22.48 mg/g for AO-52 and AR-87 respectively. The thermodynamic parameters obtained showed that the process is spontaneously feasible and entropy driven. Statistical evaluation showed that the process is well modeled in the order of ANN > RSM > MLR for AO-52 and MLR > ANN > RSM for AO-87. The study showed that magnetic modified corncob biomass is effective and competed favorably with other adsorbents for the adsorption of AO-52 and AO-87 dyes from aqueous solution. [ABSTRACT FROM AUTHOR]

This study presents the development of rainfall forecast models using potential climate indices for the Kimberley region of Western Australia, using 100 years of rainfall and climate indices data for four rainfall stations. Three different modeling techniques: multiple linear regression (MLR), autoregressive moving average with exogenous input (ARIMAX), and gene-expression programming (GEP) were applied to develop prediction models. Preliminary analysis suggests that Western Tropical Indian Ocean (WTIO) and Southern Oscillation Index (SOI) have significant impacts on summer rainfall generation for the region. Developed models' performances were evaluated using Pearson correlation coefficient (r ), root mean square error ( RMSE ), mean absolute error (M A E) , Nash–Sutcliffe efficiency (N S E) , and refined Willmot index of agreement ( d r ). It is found that the GEP model exclusively outperforms the other two alternatives. In the calibration period, the GEP model resulted in a Pearson correlation coefficient (r) values ranging from 0.76 to 0.85, which are significantly higher than that achieved from MLR (0.32 to 0.44) and ARIMAX (0.53 to 0.83) models, while for the validation period, the correlation values for the models ranged from 0.74 to 0.87 for GEP, 0.35 to 0.51 for MLR and 0.59 to 0.77 for ARIMAX models. Considering other statistical error statistics it can be concluded that the GEP model is the best representative seasonal rainfall forecasting model for the region. [ABSTRACT FROM AUTHOR]

Particulate pollution has become one of the major issues in the mega-cities of Pakistan. As with the increase in rapid urbanization, poor air quality, climate change, and health-related issues are increasing gradually in Lahore. Therefore, the implications for the variability of air pollution need to be better understood for the improvement of air quality. So, in this article, we used aerosol robotic network (AERONET) and moderate resolution imaging spectroradiometer (MODIS) datasets along with the variability of different meteorological parameters (temperature, wind speed, relative humidity, dew point, and sea level pressure) over Lahore during 2006 to 2022. Moreover, the multi-linear regression model is used to analyse the linear relation of AERONET-retrieved aerosol optical depth (AOD) and particulate matter (PM2.5) with MODIS-retrieved AOD during the time period. Both AOD and PM2.5 increase gradually throughout the time period. AERONET-retrieved AOD showed a significant variability during the time period where each meteorological parameter gives a significant value (p < 0.05) except pressure (p > 0.05). The AERONET-retrieved AOD and PM2.5 give a strong positive value (0.78 and 0.63) of the coefficient of correlation. Seasonally, the value of the coefficient of correlation is observed high during summer (0.92) followed by autumn, spring, and winter. Considering the outcomes of this study, different methods like using better quality of fuel, use of public transport, plantation of trees, etc., can be employed to reduce air pollution. [ABSTRACT FROM AUTHOR]

Copyright of EMERG: Energy. Environment. Efficiency. Resources. Globalization is the property of Romanian National Committee of World Energy Council and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Objective: To explore the application of neutrophils to lymphocytes ratio (NLR), platelet to lymphocyte ratio (PLR) and monocyte to lymphocyte ratio (MLR) in the differential diagnosis of benign uterine diseases and endometrial malignant tumors. Methods: 80 patients with endometrial malignant tumor diagnosed in our hospital from January 2019 to December 2022 were selected as the study group, and 74 patients with benign uterine diseases confirmed by pathology in our hospital during the same period were randomly selected as the control group. The differences of NLR, PLR and MLR in the peripheral blood of patients in each group were compared, and the value of individual indicators in the diagnosis of endometrial malignant tumor was evaluated using the Receiver Operating Characteristic (ROC) curve. Results: In peripheral blood, the NLR, PLR and MLR value in patients with endometrial cancer (EC) were significantly higher than those in patients with benign uterine diseases (P < 0.05). The area under the curve (AUC) of NLR, PLR, MLR in peripheral blood were 0.777, 0. 675 and 0.698. The best cutoff values were 2.02, 132.06 and 0.166. The sensitivity and specificity were 62.5% and 79.7%, 62.5% and 63.5%, 81 3% and 47.3%. The combination of these three indicators can significantly improved the diagnostic efficiency in endometrial cancer (AUC = 0.780), and the sensitivity and specificity were 60% and 83.8%. Conclusions: In peripheral blood, NLR, PLR and MLR have certain diagnostic value in the differential diagnosis of endometrial cancer. When NLR, PLR and MLR are elevated, we should be alert to the occurrence of endometrial malignant tumors, and the combined diagnostic efficiency is high. [ABSTRACT FROM AUTHOR]

BackgroundRecent research indicates that the monocyte lymphocyte ratio (MLR), neutrophil lymphocyte ratio (NLR), platelet lymphocyte ratio (PLR), C-reactive protein (CRP), and systemic immune-inflammation index (SII) may serve as valuable predictors of early postoperative mortality in elderly individuals with hip fractures. The primary objective of the study was to examine the association between preoperative MLR, NLR, PLR, CRP, and SII levels and 3-year mortality risk in geriatric patients after hip fracture surgery.Patients and methodsThe study included patients aged 65 years or older who underwent hip fracture surgery between November 2018 and November 2019. Admission levels of MLR, NLR, PLR, CRP, and SII were measured. The median follow-up period was 3.1 years. Cox proportional hazards models were used to calculate the hazard ratio (HR) for mortality with adjusting for potential covariates. Time-dependent receiver operating characteristic (ROC) curves were employed to assess the predictive capability of inflammatory indicators for mortality.ResultsA total of 760 patients completed the follow-up (79.4 ± 7.8 years, 71.1% female). A higher preoperative MLR was found to be significantly associated with an increased 3-year postoperative mortality risk (HR 1.811, 95% CI 1.047–3.132, P = 0.034). However, no significant correlations were observed between preoperative NLR, PLR, CRP, SII and 3-year mortality. The areas under the ROC curve (AUCs) of MLR for predicting 30-day, 120-day, 1-year, and 3-year mortality were 0.74 (95% CI 0.53–0.95), 0.70 (95% CI 0.57–0.83), 0.67 (95% CI 0.60–0.74), and 0.61 (95% CI 0.56–0.66), respectively.ConclusionPreoperative MLR is a useful inflammatory marker for predicting 3-year mortality in elderly hip fracture patients, but its predictive ability diminishes over time.

ObjectiveLeukocyte parameters are associated with cardiovascular diseases. The aim of the present study was to investigate the role of leukocyte parameters in patients with ST-segment elevation myocardial infarction (STEMI) undergoing primary percutaneous coronary intervention (PPCI) with high thrombus burden (HTB).MethodsA total of 102 consecutive STEMI patients with HTB who underwent PPCI within 12 h from the onset of symptoms between June 2020 and September 2021 were enrolled in this study. In addition, 101 age- and sex-matched STEMI patients with low thrombus burden (LTB) who underwent PPCI within 12 h from the onset of symptoms were enrolled as controls. Leukocyte parameters, such as neutrophil to lymphocyte ratio (NLR), platelet to lymphocyte ratio (PLR), and monocyte to lymphocyte ratio (MLR), were calculated at the time of admission.ResultsThe value of NLR and MLR were significantly higher in the HTB group than in the LTB group (6.24 ± 4.87 vs. 4.65 ± 3.47, p = 0.008; 0.40 ± 0.27 vs. 0.33 ± 0.20, p = 0.038). A cutoff value of >5.38 for NLR had a sensitivity and specificity of 53.9% and 74.3%, respectively, and MLR >0.29 had a sensitivity and specificity of 60.8% and 55.4%, respectively, for determining the STEMI patients with HTB [area under the receiver operating characteristic curve (AUC): 0.603, 95% confidence interval (CI): 0.524–0.681, p = 0.012; AUC: 0.578, 95% CI: 0.499–0.656, p = 0.046]. There was no significant difference of all-cause mortality rate and major adverse cardiac events (MACEs) between the STEMI patients with HTB or with LTB (3.92% in HTB group vs. 2.97% in LTB group, p = 0.712; 10.78% in HTB group vs. 8.91% in LTB group, p = 0.215). Compared with the HTB patients in the low NLR group, C-reactive protein, baseline troponin I, baseline brain natriuretic peptide, and leukocyte parameters, such as white blood cell, neutrophil, lymphocyte, NLR, PLR, and MLR, were also significantly higher in the high NLR group in STEMI patients who underwent PPCI with HTB (18.94 ± 19.06 vs. 35.23 ± 52.83, p = 0.037; 10.99 ± 18.07 vs. 21.37 ± 19.64, p = 0.007; 199.39 ± 323.67 vs. 430.72 ± 683.59, p = 0.028; 11.55 ± 3.56 vs. 9.31 ± 2.54, p = 0.001; 9.77 ± 3.17 vs. 5.79 ± 1.97, p = 0.000; 1.16 ± 0.44 vs. 2.69 ± 1.23, p = 0.000; 9.37 ± 4.60 vs 1.31 ± 2.58, p = 0.000; 200.88 ± 89.90 vs. 97.47 ± 50.99, p = 0.000; 0.52 ± 0.29 vs. 0.26 ± 0.14, p = 0.000, respectively). MACEs and heart failure in the high NLR group were significantly higher than that in the low NLR group of STEMI patients who underwent PPCI with HTB (20.45% vs. 4.25%, p = 0.041; 10.91% vs. 2.13%, p = 0.038).ConclusionThe value of NLR and MLR were higher in STEMI patients who underwent PPCI with HTB. In STEMI patients who underwent PPCI with HTB, a raised NLR could effectively predict the occurrence of MACEs and heart failure.

Abstract Background Inflammation is a cardinal mechanism of diabetic kidney disease (DKD). Interleukin-6 (IL6) is a reliable indicator that plays a role in the progression of DKD. Monocyte-to-lymphocyte ratio (MLR) is also implicated in this inflammatory process. The progression of DKD is associated with increased carotid intima-media thickness (CIMT), which is an independent predictor of atherosclerosis. Aim The role of IL6 and MLR ratio influencing the progression of DKD was assessed using the urinary albumin creatinine ratio (UACR) and glomerular filtration rate (GFR). Moreover, their contribution to increasing CIMT in DKD was assessed. Methods An observational prospective study was conducted on ninety diabetic patients presented to the Internal Medicine Clinic at Kasr AlAiny. The subjects were classified into three groups, thirty patients for each, according to UACR: with normoalbuminuria, microalbuminuria, and macroalbuminuria. A history and clinical assessment, CBC, MLR, HbA1c, lipid profile, IL6, creatinine, and eGFR were carried out. Furthermore, CIMT was measured using Doppler ultrasound. Results The results showed that IL6 and MLR were significantly higher in the macroalbuminuria group compared to the other two groups with p 0.001. IL6 and MLR were positively correlated with CIMT in micro and macroalbuminuric patients. Conclusion Both MLR and IL-6, as simple biomarkers associated with increased CIMT, play an important role in predicting the nephropathy of DKD patients.

This paper proposes a new method for predicting the energy generated by Photovoltaic (PV) panels with coolant Calcium Chloride (CaCl2). The study seeks to address heat-related issues that can affect the performance of PV panels by reducing their operational temperature. High operational temperatures can decreased electricity production, potentially cause permanent damage to solar cells, and shorten the panel’s lifespan. Calcium Chloride compound possesses hygroscopic properties, meaning it can absorb water from the surrounding air, thus serving as a cooling material. Calcium Chloride compound is applied as crystalline granules placed in a container beneath the PV panel. This research seeks to evaluate the effect of Calcium Chloride usage on reducing the operational temperature of PV panels and its influence on increasing PV efficiency. Subsequently, using the temperature data of the PV panel, modeling is performed to predict the PV output power using three predictive models: Multiple Linear Regression (MLR), Temperature Coefficient of Power (TC-P), and Artificial Neural Network (ANN). These three models are then compared to test their effectiveness. The research results indicate that using Calcium Chloride as a coolant is highly effective in improving the performance and efficiency of PV panels. Additionally, the ANN model accuratelly predicts power output based on temperature condition variations, with a Coefficient of Determination ( $R^{2} =0.98$ ).

Climate change is having a significant impact on weather variables like temperature, humidity, precipitation, solar radiation, daylight duration, wind speed, etc. These weather variables are key indicators that affect electricity demand and consumption. Hence, understanding the significance of weather elements on energy needs and consumption is important to be able to adapt, strategize, and predict the effect of the changing climate on the required energy of an organization. This study aims to investigate the relationship between changing weather elements and electricity consumption, employing Multivariate Linear Regression (MLR), Support Vector Regressions (SVR), and Artificial Neural Network (ANN) models to predict the effect of weather changes on energy consumption. The following approaches were engaged for this study: Creating a catalog of weather elements and parameters of energy need or its consumption; analyzing and correlating electrical power consumption to weather factors; and developing prediction models—MLR, SVR, and ANN to predict the significance of the change in the variables of weather on the electrical energy consumption. Among the weather variables considered, temperature emerged as the most influential factor affecting electricity consumption, displaying the highest correlation. The monthly total pattern for electricity use for the case study area followed a similar pattern as the mean apparent temperature. Of the three models (MLR, SVR, and ANN) developed in this study, the ANN model yielded the best predictive performance, with Mean Square Error (MSE), Mean Absolute Error (MAE), and Mean Absolute Percentage Error (MAPE) of 2.733%, 1.292%, and 4.66%, respectively. Notably, the ANN model outperformed the other models (MLR and SVR) by more than 20% across the predictive performance metrics employed.

Background: The prevalence of obesity and overweight in Indonesian women will continue to increase. Obesity and overweight conditions are often associated with low-grade inflammatory conditions and metabolic syndrome. The ratio of monocytes to HDL cholesterol (RMH) and the ratio of monocytes to lymphocytes (RML) are easy tests to monitor inflammation associated with increased body mass index and cardiovascular disorders due to metabolic syndrome. Objective: To analyze the differences in RMH and RML in obese and overweight women. Methods: The cross-sectional study was conducted in the outpatient laboratory at the Diponegoro National Hospital (RSND). This study included 55 overweight women (BMI 25.00 – 29.99 kg/m2) and 55 obese women (BMI ≥ 30 kg/m2), healthy, aged 25-45 years. Research subjects were excluded with the criteria of not having hypertension, liver disorders, and diabetes mellitus. RMH is obtained by dividing the absolute monocyte count by HDL cholesterol. RML is obtained by dividing the absolute monocyte and lymphocyte counts. Monocyte and lymphocyte numbers were obtained by examining a Complete Blood Count (CBC) using an automatic hematology analyzer. HDL cholesterol levels were measured by enzymatic methods using a clinical chemistry analyzer. Statistical analysis with the Mann-Whitney test. Results: There is a difference in RMH between obese and overweight women (p = 0.003). There was no difference in RML (p = 0.342) between the two groups. Conclusion: Obese women have a significantly higher RMH than overweight. RMH can be used as an assessment of inflammatory conditions and is used as a clinical alert for various health problems in obese and overweight women.