Prediction Of Parameters Research Articles

Discovery of 1,2,3-triazole incorporated indole-piperazines as potent antitubercular agents: Design, Synthesis, In vitro biological evaluation, molecular docking and ADME studies

In this report, a library consisting of three sets of indole-piperazine derivatives was designed through the molecular hybridization approach. In total, fifty new hybrid compounds (T1-T50) were synthesized and screened for antitubercular activity against Mycobacterium tuberculosis H37Rv strain (ATCC-27294). Five (T36, T43, T44, T48 and T49) among fifty compounds exhibited significant inhibitory potency with the MIC of 1.6 µg/mL, which is twofold more potent than the standard first-line TB drug Pyrazinamide and equipotent with Isoniazid. N-1,2,3-triazolyl indole-piperazine derivatives displayed improved inhibition activity as compared to the simple and N-benzyl indole-piperazine derivatives. In addition, the observed activity profile of indole-piperazines was similar to standard anti-TB drugs (isoniazid and pyrazinamide) against Staphylococcus aureus, Escherichia coli, and Pseudomonas aeruginosa strains, demonstrating the compounds’ selectivity towards the Mycobacterium tuberculosis H37Rv strain. All the active anti-TB compounds are proved to be non-toxic (with IC50 > 300 μg/mL) as verified through the toxicity evaluation against VERO cell lines. Additionally, molecular docking studies against two target enzymes (Inh A and CYP121) were performed to validate the activity profile of indole-piperazine derivatives. Further, in silico-ADME prediction and pharmacokinetic parameters indicated that these compounds have good oral bioavailability.

A Numerical Study on the Energy Dissipation Mechanisms of a Two-Stage Vertical Pump as Turbine Using Entropy Generation Theory

Utilizing a two-stage vertical pump as turbine (TVPAT) is an economically method for constructing small-scale pumping and storage hydropower stations at high head-low discharge sites, such as underground coal mines. The energy dissipation mechanisms in flow passages are theoretically important for performance prediction and geometric parameter optimization. In this paper, the energy dissipation within the TVPAT has been studied using entropy generation theory, which can be applied to visual, locate and quantify energy dissipation. The numerical solution of entropy dissipation components was extracted on turbine modes in different flow rates using the steady-state single-phase SST k-ω turbulence model. The numerical results show that the energy dissipation in TVPAT mainly comes from turbulent fluctuation (43.6%-72.1%) and blade surface friction (27.8%-58.2%). The runners are the main source of turbulent entropy (SD′ ) generation (47.2%-83.3%). The contribution of the return channel and spiral case to the generation under overload conditions is significant, accounting for 33.6% and 14.3 at 1.3QBEP, respectively. Flow field analysis reveals that high generation within a runner are located in the striking flow region of the leading edge, the flow squeezing region in the blade channel, and the wake region of tailing edge. The mismatch between the placement angle of the blades or guide vanes and the liquid flow angle is an important incentive for SD′ generation. Moreover, hydraulic energy is consumed through the interaction between mainstream and local inferior flows such as separation and vortices, as well as the striking and friction between local fluid and wall surfaces.

Kiosk 2R-TA-07 - Cardiovascular Magnetic Resonance and Echocardiography Parameters for Prediction of Health-related Quality of Life Scores in Hypertrophic Cardiomyopathy

Kiosk 2R-TA-07 - Cardiovascular Magnetic Resonance and Echocardiography Parameters for Prediction of Health-related Quality of Life Scores in Hypertrophic Cardiomyopathy

Low-Density Lipoprotein Cholesterol to Triglyceride Ratio and Clinical Outcomes after Acute Ischaemic Stroke or Transient Ischaemic Attack.

Studies showed that low-density lipoprotein cholesterol (LDL-C) to triglyceride (TG) ratio could be used as a predictive parameter of low-density lipoprotein oxidation in vivo and the level of small dense LDL-C. However, whether LDL-C/TG ratio is associated with stroke prognosis remains unclear. We investigated the associations of LDL-C/TG ratio with outcomes in patients with acute ischaemic stroke (AIS) or transient ischaemic attacks (TIA) and explored whether it produced more predictive value than LDL-C and TG. Data were derived from the Third China National Stroke Registry (CNSR-III). Multivariable Cox regression for stroke recurrence, composite vascular events and all-cause death and logistic regression for the poor functional outcome (modified Rankin Scale score 3-6) were used. A total of 14123 patients were included. After adjusting for confounding factors, quartile 4 of LDL-C/TG ratio was associated with an increased risk of recurrent stroke (hazard ratio [HR], 1.27; 95% confidence interval [CI], 1.03-1.56), composite vascular events (HR,1.23; 95% CI, 1.00-1.52), death (HR,1.70; 95% CI, 1.13-2.54) and poor functional outcome (odds ratio, 1.34; 95% CI, 1.12-1.61) at 3 months follow-up compared with quartile 1. We also found that quartile 4 of LDL-C and TG was positively and negatively associated with poor functional outcome at 3 months, respectively. LDL-C/TG ratio performed better than LDL-C or TG in predicting clinical outcomes. LDL-C/TG ratio was associated with the risk of stroke recurrence, composite vascular events, death and poor functional outcome in patients with AIS or TIA.

Convergence and Stability of Optimal Regulation via Generalized N -Step Value Gradient Learning.

In this article, the generalized N -step value gradient learning (GNSVGL) algorithm, which takes a long-term prediction parameter λ into account, is developed for infinite horizon discounted near-optimal control of discrete-time nonlinear systems. The proposed GNSVGL algorithm can accelerate the learning process of adaptive dynamic programming (ADP) and has a better performance by learning from more than one future reward. Compared with the traditional N -step value gradient learning (NSVGL) algorithm with zero initial functions, the proposed GNSVGL algorithm is initialized with positive definite functions. Considering different initial cost functions, the convergence analysis of the value-iteration-based algorithm is provided. The stability condition for the iterative control policy is established to determine the value of the iteration index, under which the control law can make the system asymptotically stable. Under such a condition, if the system is asymptotically stable at the current iteration, then the iterative control laws after this step are guaranteed to be stabilizing. Two critic neural networks and one action network are constructed to approximate the one-return costate function, the λ -return costate function, and the control law, respectively. It is emphasized that one-return and λ -return critic networks are combined to train the action neural network. Finally, via conducting simulation studies and comparisons, the superiority of the developed algorithm is confirmed.

Predicting effect of anti-PD-1/PD-L1 inhibitors therapy for hepatocellular carcinoma by detecting plasma metabolite based on UHPLC-MS.

Anti-PD-1/PD-L1 inhibitors therapy has become a promising treatment for hepatocellular carcinoma (HCC), while the therapeutic efficacy varies significantly among effects for individual patients are significant difference. Unfortunately, specific predictive biomarkers indicating the degree of benefit for patients and thus guiding the selection of suitable candidates for immune therapy remain elusive.no specific predictive biomarkers are available indicating the degree of benefit for patients and thus screening the preferred population suitable for the immune therapy. Ultra-high-pressure liquid chromatography-mass spectrometry (UHPLC-MS) considered is an important method for analyzing biological samples, since it has the advantages of high rapid, high sensitivity, and high specificity. Ultra-high-pressure liquid chromatography-mass spectrometry (UHPLC-MS) has emerged as a pivotal method for analyzing biological samples due to its inherent advantages of rapidity, sensitivity, and specificity. In this study, potential metabolite biomarkers that can predict the therapeutic effect of HCC patients receiving immune therapy were identified by UHPLC-MS. A partial least-squares discriminant analysis (PLS-DA) model was established using 14 glycerophospholipid metabolites mentioned above, and good prediction parameters (R2 = 0.823, Q2 = 0.615, prediction accuracy = 0.880 and p < 0.001) were obtained. The relative abundance of glycerophospholipid metabolite ions is closely related to the survival benefit of HCC patients who received immune therapy. This study reveals that glycerophospholipid metabolites play a crucial role in predicting the efficacy of immune therapy for HCC.

Bitcoin price prediction based on fear &amp; greed index

This study investigates the Fear &amp; Greed Index, an indicator designed to reflect market sentiment regarding Bitcoin price, intending to utilize it as a predictive parameter for future price fluctuations. Due to the substantial volatility in Bitcoin prices and its significant influence on prediction outcomes, the dataset was preprocessed through monthly filtering and normalization. To forecast Bitcoin prices, an array of machine learning algorithms, including linear regression, random forest, and XGBoost, as well as their enhanced counterparts, were employed. The optimal model was identified by comparing the Grid Search XGBoost analysis results. This research holds implications for accurately predicting Bitcoin prices and underscores the impact of market sentiment on its valuation.

Line loss localization diagnosis and management measures of station area combined with data mining

Abstract This study explores an advanced method for improving line loss in station areas, a vital issue in the energy sector. By analyzing line loss data from the X-area station using data mining and a novel convolutional neural network (CNN) model optimized with particle swarm optimization, we aimed to pinpoint and diagnose line loss issues effectively. Our model, which integrates specific line loss predictive parameters, underwent rigorous training and testing with collected data. The results were promising: the model's predictions closely matched actual data, with most errors under 0.1. It outperformed existing models in iteration speed, convergence time, and accuracy, evidenced by lower mean square error (0.0112), root mean square error (0.1023), and average absolute error (3.2514%). This research presents a potent tool for distribution network analysis, offering practical insights for line loss localization and diagnosis.

Numerical investigation of boiler waterwall corrosion by integrated tube temperature prediction and sulfur evolution model

High temperature corrosion has become one of the leading causes of boiler waterwall tube failures because of the wide application of air-staged combustion in coal-fired boilers. High temperature corrosion is strongly affected by the H2S concentration and wall tube temperature distributions in the furnace. Particularly, the corrosion rate increases exponentially with the increase of tube temperature. However, by far most of the numerical studies on high temperature corrosion only considered the effects of H2S concentration while the critical effects of tube temperature were overlooked. Thus, this paper presents a comprehensive corrosion model that incorporates the key effects of both tube temperature and H2S concentration. Specifically, the tube temperature is predicted by a coupled combustion and hydrodynamic model that integrates the critical effects of both the boiler gas flow and steam flow on waterwall tube temperature. The H2S concentration is predicted by a sulfur evolution model describing the devolatilization process of coal sulfur species and their subsequent gaseous reactions. The tube temperature prediction model and H2S evolution model provide all the key parameters for accurate prediction of boiler waterwall corrosion. The corrosion model is applied to a 350 MW supercritical wall-fired boiler. The results show that the tube temperature varies significantly over the boiler waterwall, and hence, has significant impacts on the distribution of wall tube corrosion. Particularly, local high tube temperature zones could form on the waterwall areas of relatively low H2S concentration leading to substantially higher tube corrosion rates than the adjacent wall areas. The results demonstrate the importance of incorporating both the wall tube temperature and H2S concentration in the prediction of boiler waterwall corrosion.

Pre-operative Predictive Factors for Difficult Laparoscopic Cholecystectomy at Tertiary Care Centre

INTRODUCTION Gallstones are present in 10 to 15% of general population and asymptomatic in more than 80% cases. Laparoscopic cholecystectomy is a gold standard treatment for cholelithiasis. The rate of conversion from laparoscopic cholecystectomy to open is about 1-13%. Predicting the difficulty of laparoscopic cholecystectomy is essential to optimize patient outcomes. The levels of difficulties during laparoscopic cholecystectomy can be predicated based on certain preoperative clinical, laboratory or radiological parameters. The main aim of the study was to find out the pre-operative predictive factors for difficult laparoscopic cholecystectomy at a tertiary care centre. MATERIAL AND METHODS This was an observational cross-sectional study that included sixty nine patients who underwent elective laparoscopic cholecys- tectomy in UCMS. Preoperative predictive parameters such as age, gender, BMI, previous abdominal surgery, gallbladder condition, and ultrasound findings were assessed. A scoring system developed by Randhawa and Pujahari was used to predict difficulty in laparoscopic cholecystectomy, and intraoperative criteria were used to categorize cases as easy or difficult. RESULTS History of acute cholecystitis (p=0.026), previous abdominal scar (p=0.024), thick gallbladder wall (p=0.012), impacted stone (p=0.057), pericholecystic fluid collection (p&lt;0.001) and increased BMI were considered as the significant factors that predict difficult laparoscopic cholecystectomy. Sensitivity and specificity for easy - difficult cut off of the scoring method were 75.6% and 67.16%, respectively, with the area under the ROC curve being 0.524. CONCLUSION The difficult laparoscopic cholecystectomy and conversion to open surgery can be predicted preoperatively based on preoperative scoring system.

The relationship between febrile seizure and hematological parameters in children

Aim: The aim of this study was to investigate whether hematological parameters play a significant role in the relationship between hematological parameters and seizure occurrence in children with febrile seizures (FS) by comparing them to a healthy control group with no fever or seizures. Materials and Methods: One-hundred forty-one patients diagnosed with FS and with available a complete blood count results and a control group of 143 children were finally enrolled. Results: The study group consisted of 141 patients, 57 girls (40.4%) and 84 boys (59.6%) (M/F=1.4). Mean age at the time of first FS was 22.89 ± 13.95 months. Ninety-two (65.2%) of the study group were diagnosed with simple FS, 32 (22.7%) with complex FS, and 17 (12.1%) with febrile status epilepticus (FSE). Conclusion: Since our neutrophil, lymphocyte, eosinophil, and mean platelet volume (MPV) results were statistically significant in patients with FS, it is thought that these markers may represent potential predictive parameters in that condition.

Paclitaxel and Therapeutic Drug Monitoring with Microsampling in Clinical Practice.

Paclitaxel is an anticancer agent efficacious in various tumors. There is large interindividual variability in drug plasma concentrations resulting in a wide variability in observed toxicity in patients. Studies have shown the time the concentration of paclitaxel exceeds 0.05 µM is a predictive parameter of toxicity, making dose individualization potentially useful in reducing the adverse effects. To determine paclitaxel drug concentration, a venous blood sample collected 24 h following the end of infusion is required, often inconvenient for patients. Alternatively, using a microsampling device for self-sampling would facilitate paclitaxel monitoring regardless of the patient's location. We investigated the feasibility of collecting venous and capillary samples (using a Mitra® device) from cancer patients to determine the paclitaxel concentrations. The relationship between the venous plasma and whole blood and venous and capillary blood (on Mitra®) paclitaxel concentrations, defined by a Passing-Bablok regression, were 0.8433 and 0.8569, respectively. Demonstrating a clinically acceptable relationship between plasma and whole blood paclitaxel concentration would reduce the need to establish new target concentrations in whole blood. However, in this study, comparison of venous and capillary blood using Mitra® for sampling displayed wide confidence intervals suggesting the results from the plasma and whole blood on this device may not be interchangeable.

Glycated haemoglobin, HOMA2-B, C-peptide to glucose ratio and type 2 diabetes clusters as predictors for therapy failure in individuals with type 2 diabetes without insulin therapy: A registry analysis.

Some people with type 2 diabetes mellitus (T2D) and declining β-cell function do require insulin over time. Various laboratory parameters, indices of glucose metabolism or phenotypes of T2D (clusters) have been suggested, which might predict future therapy failure (TF), indicating the need for insulin therapy initiation. This analysis evaluated glycated haemoglobin (HbA1c), homeostatic model assessment (HOMA)2-B, C-peptide to glucose ratio (CGR) and diabetes clusters as predictive parameters for the occurrence of glycaemic TF in individuals diagnosed with T2D without previous insulin therapy. In total, 159 individuals with T2D [41% female, median age 50 (IQR: 53-69) years, diabetes duration 9 (5-15) years], without insulin therapy were prospectively evaluated for the occurrence of a composite primary endpoint, including HbA1c increasing or remaining >8.0% (64 mmol/mol) 3 months after baseline on non-insulin glucose-lowering agents, insulin initiation or hospital admissions because of acute hyperglycaemic events. Diabetes clusters were formed according to previously described characteristics. Only severe autoimmune diabetes clusters were excluded because of a small amount of glutamate decarboxylase antibody-positive participants. The other clusters were distributed as mild age-related diabetes 33%; severe insulin-deficient diabetes 31%; mild obesity-related diabetes 20%; and severe insulin-resistant diabetes 15%. During a median observation of 57 months, higher tertiles of HbA1c at baseline, HOMA2-B, as well as a lower CGR were significantly predictive for the occurrence of the primary endpoint. The probability of meeting the primary endpoint was the highest for mild obesity-related diabetes [hazard ratio 3.28 (95% confidence interval 1.75-6.2)], followed by severe insulin-deficient diabetes [hazard ratio 2.03 (95% confidence interval 1.1-3.7)], mild age-related diabetes and the lowest for severe insulin-resistant diabetes. The best performance to predict TF with an area under the curve (AUC) of 0.77 was HbA1c at baseline, followed by HOMA2-B (AUC 0.69) and CGR (AUC 0.64). HbA1c, indices of insulin secretion capacity (HOMA2-B and CGR) and T2D clusters might be applicable tools to guide practitioners in the decision of whether insulin is required in people already diagnosed with T2D. These findings need to be validated in prospective studies.

Experimental Study on Coal and Gas Outburst Risk under Different Water Content Rates in Strong Outburst Coal Seams.

To investigate the alleviation potency of coal seam water infusion on coal and gas outburst, this paper focuses on the Qidong coal mine outburst coal seam, where outburst accidents have occurred many times, and obtains the impact of water content on outburst prediction parameters by studying the features of outburst parameters and gas desorption law under different water content rates. How water content affects outburst was also researched through the use of a self-made outburst simulation test system, and the relationship between water content and outburst intensity and critical gas pressure was studied. It can be concluded that with the rise of water content, the initial velocity of gas diffusion, the gas desorption index of drilling cuttings, and the adsorption constant a decrease, but the firmness coefficient (f) increase, and these indicators are exponentially related to the water content. Meanwhile, as the water content raises, the outburst pressure threshold increases, the outburst intensity gradually decreases, and the less likely outburst occurs. Under 0.5 MPa pressure, as the water content arose from 2.02 to 5.14%, the outburst intensity was significantly weakened, while no outburst occurred as the water content reached to 10.25%. Fitting analysis of the influence curve of outburst parameters and comparing the vital values of outburst prediction indexes finally determined that the water content rate of 5.14% could be used as a key index for water injection measures for coal and gas outburst prevention coal seam in Qidong coal mine no. 9. This research offers a guiding significance for the outburst prevention measures of water infusion in outburst coal seams and gives a feasible scheme for the safe mining of outburst coal mines.

Predicting multi-component, high-temperature metallic glasses by coupling empirical models, CALPHAD, and a genetic algorithm

Metallic glasses (MGs) are an emerging class of materials possessing multiple desirable properties including high strength, hardness, and corrosion resistance when compared to their crystalline counterparts. However, most previously studied MGs are not useful in high temperature environments because they undergo the glass transition phenomenon and crystallize below the melting point. In addition, bulk MGs are typically found in multi-component systems, meaning that searching compositional space with a reasonable resolution using computational or experimental methods can be costly. In this study, an in-house developed genetic algorithm-based tool was used to locate alloy compositions with high glass forming ability (GFA) and high-temperature stability in the Ta–Ni–Co–(B) and Ta–Ni–Co–Nb alloy systems. GFA was predicted using an empirical predictive parameter known as P HSS. High-temperature stability was predicted using the CALPHAD method to calculate liquidus temperature. Justification for the use of P HSS to predict GFA of high-temperature MGs, as well as the use of liquidus temperature as a predictor of general high-temperature stability, is given in the form of a meta-analysis of previously reported MG compositions. The predictions made using this algorithm were analyzed and are presented herein. While high-temperature stability was the property of interest for this research, this framework could be used in the future to locate alloys with other application-specific material properties. This genetic algorithm-based tool enables the coupling of empirical parameters and CALPHAD to efficiently search multi-component space to locate glass-forming alloys with desirable properties.

A new predictive parameter for dose-volume metrics in intensity-modulated radiation therapy planning for prostate cancer: Initial phantom study.

Organ-at-risk (OAR) sparing is often assessed using an overlap volume-based parameter, defined as the ratio of the volume of OAR that overlaps the planning target volume (PTV) to the whole OAR volume. However, this conventional overlap-based predictive parameter (COPP) does not consider the volume relationship between the PTV and OAR. We propose a new overlap-based predictive parameter that consider the PTV volume. The effectiveness of proposed overlap-based predictive parameter (POPP) is evaluated compared with COPP. We defined as POPP=(overlap volume between OAR and PTV/OAR volume) × (PTV volume/OAR volume). We generated intensity modulated radiation therapy (IMRT) based on step and shoot technique, and volumetric modulated arc therapy (VMAT) plans with the Auto-Planning module of Pinnacle3 treatment planning system (v14.0, Philips Medical Systems, Fitchburg, WI) using the American Association of Physicists in Medicine Task Group (TG119) prostate phantom. The relationship between the position and size of the prostate phantom was systematically modified to simulate various geometric arrangements. The correlation between overlap-based predictive parameters (COPP and POPP) and dose-volume metrics (mean dose, V70Gy, V60Gy, and V37.5Gy for rectum and bladder) was investigated using linear regression analysis. Our results indicated POPP was better than COPP in predicting intermediate-dose metrics. The bladder results showed a trend similar to that of the rectum. The correlation coefficient of POPP was significantly greater than that of COPP in<62Gy (82% of the prescribed dose) region for IMRT and in<55Gy (73% of the prescribed dose) region for VMAT regarding the rectum (p<0.05). POPP is superior to COPP for creating predictive models at an intermediate-dose level. Because rectal bleeding and bladder toxicity can be associated with intermediate-doses as well as high-doses, it is important to predict dose-volume metrics for various dose levels. POPP is a useful parameter for predicting dose-volume metrics and assisting the generation of treatment plans.

Integration of Machine Learning Models into Three-Dimensional Numerical Simulation of Polymer Electrolyte Membrane Fuel Cells

Numerical simulations are powerful tools for understanding phenomena in Polymer Electrolyte Membrane Fuel Cells (PEMFCs) and developing PEMFCs with high performance and durability. We have been developing a three-dimensional (3D) PEMFC simulator P-Stack [1]. P-Stack can perform 3D cell/stack simulations for long-time operation about 10 ~ 100 hours at most. However, fuel cell degradation occurs on longer time scales. To simulate such very long time scales within acceptable computational time, the computational speed need to be improved innovatively. As one of the approaches, we have integrated machine learning models into the simulation.In the power generation calculation of P-Stack, an in-plane current density distribution is determined by calculating a one-dimensional (1D) electrochemical reaction model at each point. In the present study, the 1D electrochemical reaction model is replaced with a machine learning model. The learning data is the result of “pseudo-1D” power generation calculation of 500 cases generated by P-Stack. Here, “pseudo-1D” means the power generation calculation for sampling is performed for small cell geometry, guaranteeing that in-plane distribution is almost homogeneous. It also suitable for sampling calculation because computational cost is very low. In the data generation by P-Stack, the input parameters of P-Stack were determined by Latin hypercube sampling. The input parameters for the machine learning model are the potential, the concentration of each gas species in the anode and cathode catalyst layers, and the temperature. The prediction parameters are current density, cell resistance, overvoltages, molar flux of each gas species and heat flux.As shown in Figure 1, the machine learning model well predicts the target parameters, and the coefficient of determination was about 0.9 for various prediction targets of the verification data. As a result, machine learning could be used to predict power generation. We have implemented this machine learning model into P-Stack. We will also discuss the stability and accuracy of computation coupled with machine learning model and P-Stack, and how much the calculation can be accelerated by our approach.Reference[1] T. Tsukamoto, T. Aoki, H. Kanesaka, T. Taniguchi, T. Takayama, H. Motegi, R. Takayama, S. Tanaka, K. Komiyama, M. Yoneda, J. Power Sources 488 (2021) 229412 Figure 1

Can the Inflammatory Cell Ratio NLR and PLR be Used as a Reliable Marker in Colon Cancer? A Prospective Study.

Simple approaches for detecting the tumor stage of colon cancer patients are required during the preoperative period. In recent years, the neutrophil-lymphocyte ratio (NLR) and platelet-lymphocyte ratio (PLR) have been employed as predictive parameters for systemic inflammatory response and long-term prognosis in a variety of malignancies. The purpose of this study was to determine whether the NLR and PLR correspond with tumor characteristics in colon cancer patients. About 90 patients with colon cancer who reported to our institute during the time interval July 2021 to December 2022 were included in the study. The NLR and PLR were calculated using data obtained from a complete blood count evaluation. The relationship between inflammatory cell ratio and tumor-specific characteristics were analyzed. Neutrophil-lymphocyte ratio and PLR correlated with pTNM staging in 88 patients. Two patients exhibited diffuse peritoneal metastasis. A significant association was found between PLR and early (Tis + T1 + T2) and advanced (T3 + T4) groups. Although the difference was not statistically significant, patients with lymphovascular invasion (LVI) and perineural invasion (PNI) had greater mean NLR and PLR. Platelet-lymphocyte ratio was found to be more accurate than NLR in predicting colon cancer tumor depth/invasion. A high PLR value aids in prognosticating advanced T-stage colon cancer patients and can be used as a valuable tool for preoperative counseling, but it must be validated with a survival analysis. The tumor microenvironment contains a variety of inflammatory cells that contribute to the growth and spread of the neoplasm. The NLR and PLR have been shown to be clinically and prognostically important in a variety of gastrointestinal cancers. The results of this study demonstrate that PLR was more accurate than NLR in predicting colon cancer tumor depth/invasion. Also, a high PLR value aids in prognosticating advanced T-stage colon cancer patients and may be used as a valuable tool for preoperative counseling. Ramesh SK, Swain SK, Munikrishnan V, et al. Can the Inflammatory Cell Ratio NLR and PLR be Used as a Reliable Marker in Colon Cancer? A Prospective Study. Euroasian J Hepato-Gastroenterol 2023;13(2):61-65.

Value of 68Ga-FAPI-04 and 18F-FDG PET/CT in Early Prediction of Pathologic Response to Neoadjuvant Chemotherapy in Locally Advanced Gastric Cancer.

This prospective study investigated whether PET parameters from 18F-FDG and 68Ga-fibroblast activation protein inhibitor (FAPI)-04 PET/CT can predict a pathologic response to neoadjuvant chemotherapy (NAC) early in patients with locally advanced gastric cancer (LAGC). Methods: The study included 28 patients with LAGC who underwent 18F-FDG PET/CT and 68Ga-FAPI-04 PET/CT at baseline and after 1 cycle of NAC. PET parameters including SUV and tumor-to-background ratio (TBR), as well as the change rate of SUV and TBR, were recorded. Patients were classified as major or minor pathologic responders according to postoperative pathology findings. We compared the PET parameters between the 2 pathologic response groups and different treatment regimens and analyzed their predictive performance for tumor pathologic response. Results: Major pathologic responders had significantly lower 68Ga-FAPI change rates (percentage SUVmax [%SUVmax], percentage SUVpeak [%SUVpeak], and percentage TBR [%TBR]) than minor pathologic responders. Among the PET parameters, 68Ga-FAPI %SUVmax (area under the curve, 0.856; P = 0.009), %SUVpeak (area under the curve, 0.811; P = 0.022), and %TBR (area under the curve, 0.864; P = 0.007) were significant parameters for early prediction of pathologic response to NAC in LAGC; they had the same predictive accuracy of 89.29%, with the thresholds of decrease to at least 52.43%, 60.46%, and 52.96%, respectively. In addition, 68Ga-FAPI %SUVmax and %TBR showed significant differences between the different treatment regimens. Conclusion: In this preliminary study, 68Ga-FAPI-04 PET change rate parameters were preferable to 18F-FDG in predicting pathologic response to NAC at an early stage in LAGC. 68Ga-FAPI %SUVmax and %TBR may be better predictors of therapeutic response between different treatment regimens. These findings may help optimize the treatment for patients with LAGC.

A Simple Approach to Modeling Light Attenuation in the Sacramento–San Joaquin Delta Using Commonly Available Data

The diffuse attenuation coefficient of photosynthetically active radiation (KdPAR) is commonly used to predict light attenuation in aquatic productivity models, but obtaining measurements of PAR to compute KdPAR is difficult. In situ calculations of KdPAR require multiple measurements of PAR through the water column, and these measurements are infeasible for real-time recording. Instead, predictive models using surface-water measurements may be used. Traditional KdPAR models are based on open-ocean habitats and rely on chlorophyll—as a proxy measurement for phytoplankton abundance—as the main predictive parameter. However, elevated suspended sediments and dissolved organic materials may also affect KdPAR values of inland water bodies and estuaries. In this study, we leverage KdPAR calculations derived from in situ light measurements collected along with surface-water-quality parameters across the Sacramento-San Joaquin River Delta in California, USA (the Delta). Sampling occurred between January of 2013 and May of 2014. We also explored regional and seasonal effects, but these did not clearly affect the model. Ultimately, the best-performing model included surface-level turbidity only (R2 = 0.91). The simplicity of the model facilitates use of KdPAR estimates for a variety of purposes throughout the Delta, including euphotic depth calculations, and as inputs to primary-productivity and habitat-suitability models. We demonstrate the model’s usability with two open-sources data sets (one spatially dense, and one temporally dense), and estimate KdPAR, euphotic depth, and primary productivity within the Delta. We provide calculations for each estimation, allowing users to easily adopt these models and apply them to their own data or with open-sourced data, which are abundant.