Index Test Results Research Articles

BacDive in 2025: the core database for prokaryotic strain data.

In 2025, the bacterial diversity database BacDive is the leading database for strain-level bacterial and archaeal information. It has been selected as an ELIXIR Core Data Resource as well as a Global Core Biodata Resource. Since its initial release more than ten years ago, BacDive (https://bacdive.dsmz.de) has grown tremendously in content and functionalities, and is a comprehensive resource covering the phenotypic diversity of prokaryotes with data on taxonomy, morphology, physiology, cultivation, and more. The current release (2023.2) contains 2.6 million data points on 97334 strains, reflecting an increase by 52% since the previous publication in 2021. This remarkable growth can largely be attributed to the integration of the world-wide largest collection of Analytical Profile Index (API) test results, which are now fully integrated into the database and searchable. A novel BacDive knowledge graph provides powerful search options through a SPARQL endpoint, including the possibility for federated searches across multiple data sources. The high-quality data provided by BacDive is increasingly being used for the training of artificial intelligence models and resulting genome-based predictions with high confidence are now used to fill content gaps in the database.

Study on risk factors of impaired fasting glucose and development of a prediction model based on Extreme Gradient Boosting algorithm.

The aim of this study was to develop and validate a machine learning-based model to predict the development of impaired fasting glucose (IFG) in middle-aged and older elderly people over a 5-year period using data from a cohort study. This study was a retrospective cohort study. The study population was 1855 participants who underwent consecutive physical examinations at the First Affiliated Hospital of Soochow University between 2018 and 2022.The dataset included medical history, physical examination, and biochemical index test results. The cohort was randomly divided into a training dataset and a validation dataset in a ratio of 8:2. The machine learning algorithms used in this study include Extreme Gradient Boosting (XGBoost), Support Vector Machines (SVM), Naive Bayes, Decision Trees (DT), and traditional Logistic Regression (LR). Feature selection, parameter optimization, and model construction were performed in the training set, while the validation set was used to evaluate the predictive performance of the models. The performance of these models is evaluated by an area under the receiver operating characteristic (ROC) curves (AUC), calibration curves and decision curve analysis (DCA). To interpret the best-performing model, the Shapley Additive exPlanation (SHAP) Plots was used in this study. The training/validation dataset consists of 1,855 individuals from the First Affiliated Hospital of Soochow University, yielded significant variables following selection by the Boruta algorithm and logistic multivariate regression analysis. These significant variables included systolic blood pressure (SBP), fatty liver, waist circumference (WC) and serum creatinine (Scr). The XGBoost model outperformed the other models, demonstrating an AUC of 0.7391 in the validation set. The XGBoost model was composed of SBP, fatty liver, WC and Scr may assist doctors with the early identification of IFG in middle-aged and elderly people.

Measuring concrete air-entraining admixture adsorption on coal ash using three-phase equilibrium and fluorescence-based methods

This manuscript presents two novel methods of evaluating the adsorption of air-entraining admixtures (AEAs) by coal ashes used in cementitious mixtures. A developed three-phase equilibrium (TPE) method measures fly ash adsorption capacity accounting for carbon adsorption, Ca2+ interactions with AEA, and the equilibrium between the two. A fluorescence-based method (FBM) was also developed, utilizing a non-ionic NP-10 surfactant as a representative for AEA. This study verifies the applicability and accuracy of the TPE and FBM methods using seven class C and F coal ashes with a wide range of loss on ignition values, varying from 0.2 to 15.6 %, and three commercial AEAs. Verified with foam index test results, the TPE method was applicable to all tested AEAs and coal ashes. The results were consistent between the FBM and TPE methods when comparing rosin- and fatty acid-based AEAs, but less consistent when using a sulfonate-based AEA. These findings help us understand the applicability and limitations of the TPE and FBM methods and provide two methods for quantifying adsorption in fly ash samples.

Prediksi Dini Penyakit Diabetes Pada Ibu Hamil Dengan Algoritma Random Forest

Diabetes in pregnant women, otherwise known as gestational diabetes, is a serious health condition that can negatively affect the mother and fetus. Early detection and appropriate intervention are essential to reduce the risk of complications during pregnancy and childbirth. This study aims to develop a prediction model that can detect the risk of diabetes in pregnant women using the Random Forest algorithm. The data used in this study included relevant clinical and demographic parameters of pregnant women, such as age, body mass index, family history, and laboratory test results. The Random Forest model was chosen because of its ability to handle complex data and provide accurate prediction results. The results of this study show that the model developed has an accuracy rate of 98% in predicting the risk of gestational diabetes. In addition, important features that contribute to diabetes risk prediction have also been identified, providing additional insights for medical practitioners in conducting risk evaluations. The implementation of this model is expected to help in efforts to prevent and manage gestational diabetes, thereby improving maternal and infant health.

#1906 Lung ultrasound as a valid tool for fluid assessment in dialysis patients: a systematic review

Abstract Background and Aims The evaluation of hydration status in dialysis patients is a crucial aspect of their care. Focused lung ultrasound has garnered attention as a cost-effective and accessible tool for assessing hydration status, particularly through the detection of B-lines. Despite its potential, there is a lack of comprehensive systematic reviews addressing the clinical utility of lung ultrasound in fluid assessment for dialysis patients. Method A systematic search was conducted on PubMed, MEDLINE, EMBASE, Cochrane, and Web of Science. Two independent reviewers screened titles, abstracts, and full texts, with a third reviewer consulted in cases of discrepancy. Inclusion criteria encompassed papers utilizing lung ultrasound to evaluate fluid status in dialysis patients. Exclusion criteria were: abstracts or posters and papers not investigating at least one of the following endpoints: B-type natriuretic peptide, cardiovascular events, inferior vena cava, preload, NYHA classification, ultrafiltration/weight reduction, blood pressure, total body water/bioimpedance, hospitalization, mortality, and clinical evaluations. A modified QUADAS2-score was employed for assessment of risk of bias and applicability by two independent investigators. The protocol was registered at Prospero (CRD42022334147). Results A total of 2543 papers were identified of which 156 underwent full-text screening. Of these, 49 papers were included in the review, comprising seven randomised controlled trials and 41 observational studies. The most common study aim was impact assessment of ultrafiltration on the quantity of B-lines. However, numerous studies investigated the correlation between lung ultrasound and various endpoints such as bioimpedance, chest x-ray, and clinical examination. Overall, the included papers were characterized by noteworthy heterogeneity in terms of ultrasound protocol, encompassing differences in number of scanning zone (8-28), B-lines cut-off points, and definitions of endpoint. The QUADAS assessment revealed risk of bias in the included studies, most commonly related to a lack of blinding and, consequently, an impact on the interpretation of index test results. Only n=24 studies (49%) scored “low” risk of bias. Regarding patient selection, the numbers were even lower with only n=17 studies (34%) scoring “low” on risk of bias. Conclusion The collective evidence on clinical utility of lung ultrasound for fluid assessment of dialysis patients is characterised by notable heterogeneity and risk of bias, precluding meta-analysis. Well-designed large scale studies are still warranted to determine the optimal ultrasound protocol. Future studies should establish guidelines for scanned zones and consensus cut-offs to enhance homogeneity.

Cerebral Blood Flow in Alzheimer's Disease: A Meta-Analysis on Transcranial Doppler Investigations.

Cerebrovascular hemodynamic impairment has been reported in Alzheimer's disease (AD). We performed a systematic review and meta-analysis to investigate changes in cerebral blood flow (CBF) in AD patients. Data were obtained by searching MEDLINE and Scopus for all investigations published between 1 January 2011 and 1 November 2021, comparing the cerebrovascular hemodynamic between AD patients and cognately healthy age-matched controls, using transcranial Doppler (TCD) ultrasound. Twelve studies, based on 685 patients [395 with AD and 290 age-matched cognitively healthy controls, with a mean age of 71.5 and 72.1 years, respectively] were included in the analysis. A random effect model revealed that AD patients, in the proximal segments of the middle cerebral artery (MCA), have a significantly lower CBF velocity, compared to controls (MD: -7.80 cm/s, 95%CI: -10.78 to -5.13, p < 0.0001, I2 = 71.0%). Due to a significant Egger's test (t = 3.12, p = 0.008), a trim-and-fill analysis was performed, confirming the difference (MD: -11.05 cm/s, 95%CI: -12.28 to -9.82, p < 0.0001). Meta-regression analysis demonstrated that the mean CBF at the proximal MCA was directly correlated with arterial hypertension (p = 0.03) and MMSE score (p < 0.001), but inversely correlated with age (p = 0.01). In AD patients, the pulsatility index was significantly higher compared to controls (MD: 0.16, 95%CI: 0.07 to 0.25, p < 0.0001, I2: 84.5%), while the breath-holding index test results were significant lower (MD: -1.72, 95%CI: -2.53 to -0.91, p < 0.001, I2: 85.4%). AD patients have a significant impairment in relation to their cerebrovascular perfusion, suggesting that cerebrovascular hemodynamic deterioration, evaluated using TCD, may be a useful diagnostic tool.

Analisa Tahanan Isolasi Pada Transformator Daya 20 MVA Di Gardu Induk ULTG Ende

Power transformers are the main equipment in the electrical power system that is directly related to the distribution of electricity. One of the most important parts of a power transformer is its insulation system. Over time and operation, the condition of the insulation can deteriorate, which can cause operational failures and damage to the transformer. The purpose of the study was to determine the condition of the quality of insulation resistance in power transformers. The method was a literature study used to deepen the material related to the research title and data collection at the ULTG Ende Substation. The data taken were the results of polarization index, tangent delta and breakdown voltage tests. Based on the analysis results, the polarization index value in 2020 on the primary-ground side was 1.21 and the primary-secondary side was 1.23, but in 2022 it increased to 1.27 and 1.28. The tangent delta calculation results in 2020 on the secondary side on the CLG insulation was 0.64% and in 2022 it was 0.58%, which showed a decrease in insulation (0.5% - 0.7%). For breakdown voltage in 2020 it was 42.2 kV and OLTC 32.4 kV and in 2022 it was 41.1 kV and OLTC was 31.4 kV.

Clinical factors and major pathological response after neoadjuvant chemoimmunotherapy in potentially resectable lung squamous cell carcinoma.

Major pathological response (MPR) helps evaluate the prognosis of patients with lung squamous cell carcinoma (LUSC). However, the clinical factors that affect the achievement of MPR after neoadjuvant chemoimmunotherapy (NCIO) in patients with LUSC remain unclear. This study aimed to explore the clinical factors affecting the MPR after NCIO in patients with potentially resectable LUSC. This retrospective study included patients with stage IIB-IIIC LUSC who underwent surgical resection after receiving NCIO at a center between March 2020 and November 2022. In addition to the postoperative pathological remission rate, sex, age, body mass index (BMI), smoking history, TNM stage, hematological and imaging test results, and other indicators were examined before NCIO. According to the pathological response rate of the surgically removed tumor tissue, the patients were split into MPR and non-MPR groups. In total, 91 LUSC patients who met the study's eligibility criteria were enrolled: 32 (35%) patients in the non-MPR group and 59 (65%) in the MPR group, which included 43 cases of pathological complete remission (pCR). Pre-treatment lymphocyte level (LY) (odds ratio [OR] =5.997), tumor burden (OR=0.958), N classification (OR=15.915), radiographic response (OR=11.590), pulmonary atelectasis (OR=5.413), and PD-L1 expression (OR=1.028) were independently associated with MPR (all P < 0.05). Based on these six independent predictors, we developed a nomogram model of prediction having an area under the curve (AUC) of 0.914 that is simple to apply clinically to predict the MPR. The MPR group showed greater disease-free survival (DFS) than the non-MPR group, according to the survival analysis (P < 0.001). The MPR rate of NCIO for potentially resectable LUSC was 65%. LY, tumor burden, N classification, radiographic response, pulmonary atelectasis, and PD-L1 expression in patients with LUSC before NCIO were the independent and ideal predictors of MPR. The developed nomogram demonstrated a good degree of accuracy and resilience in predicting the MPR following NCIO, indicating that it is a useful tool for assuring customized therapy for patients with possibly resectable LUSC.

It was not possible to detect BRAF V600E mutation in circulating cell-free DNA from patients with ameloblastoma: A diagnostic accuracy study.

The objective of this study is to evaluate the diagnostic accuracy of plasma-based liquid biopsy for the detection of the BRAF V600E mutation in circulating cell-free DNA from patients with ameloblastoma. This is a prospective diagnostic accuracy study conducted based on the Standards for Reporting Diagnostic Accuracy recommendations. The index test was the plasma-based liquid biopsy, whereas the reference standard was the conventional tissue biopsy. The target condition was the detection of BRAF V600E mutation. The study population consisted of individuals with ameloblastoma recruited from three tertiary hospitals from Brazil. A negative control group composed of three individuals with confirmed wild-type BRAF lesions were included. The participants underwent plasma circulating cell-free DNA and tumor tissue DNA isolation, and both were submitted to using competitive allele-specific TaqMan™ real-time polymerase chain reaction technology mutation detection assays. Sensitivity and specificity measures and positive and negative predictive values were calculated. Twelve patients with conventional ameloblastoma were included. BRAF V600E mutation was detected in 11/12 (91.66%) ameloblastoma tissue samples. However, the mutation was not detected in any of the plasma-based liquid biopsy circulating cell-free DNA samples in both ameloblastomas and negative control group. The sensitivity and specificity of plasma-based liquid biopsy for the detection of the BRAF V600E mutation in circulating cell-free DNA was 0.0 and 1.0, respectively. The agreement between index test and reference standard results was 26.66%. Plasma-based liquid biopsy does not seem to be an accurate method for the detection of the BRAF V600E mutation in circulating circulating cell-free DNA from patients with ameloblastoma, regardless of tumor size, anatomic location, recurrence status, and other clinicopathological features.

Predictive model and risk analysis for peripheral vascular disease in type 2 diabetes mellitus patients using machine learning and shapley additive explanation.

Peripheral vascular disease (PVD) is a common complication in patients with type 2 diabetes mellitus (T2DM). Early detection or prediction the risk of developing PVD is important for clinical decision-making. This study aims to establish and validate PVD risk prediction models and perform risk factor analysis for PVD in patients with T2DM using machine learning and Shapley Additive Explanation(SHAP) based on electronic health records. We retrospectively analyzed the data from 4,372 inpatients with diabetes in a hospital between January 1, 2021, and March 28, 2023. The data comprised demographic characteristics, discharge diagnoses and biochemical index test results. After data preprocessing and feature selection using Recursive Feature Elimination(RFE), the dataset was split into training and testing sets at a ratio of 8:2, with the Synthetic Minority Over-sampling Technique(SMOTE) employed to balance the training set. Six machine learning(ML) algorithms, including decision tree (DT), logistic regression (LR), random forest (RF), support vector machine(SVM),extreme gradient boosting (XGBoost) and Adaptive Boosting(AdaBoost) were applied to construct PVD prediction models. A grid search with 10-fold cross-validation was conducted to optimize the hyperparameters. Metrics such as accuracy, precision, recall, F1-score, G-mean, and the area under the receiver operating characteristic curve (AUC) assessed the models' effectiveness. The SHAP method interpreted the best-performing model. RFE identified the optimal 12 predictors. The XGBoost model outperformed other five ML models, with an AUC of 0.945, G-mean of 0.843, accuracy of 0.890, precision of 0.930, recall of 0.927, and F1-score of 0.928. The feature importance of ML models and SHAP results indicated that Hemoglobin (Hb), age, total bile acids (TBA) and lipoprotein(a)(LP-a) are the top four important risk factors for PVD in T2DM. The machine learning approach successfully developed a PVD risk prediction model with good performance. The model identified the factors associated with PVD and offered physicians an intuitive understanding on the impact of key features in the model.

Vaccinated COVID-19 Index Cases Are Less Likely to Transmit SARS-CoV-2 to Their Household Contacts: A Cohort Study.

The aim of this study was to evaluate the impact of index case vaccination on SARS-CoV-2 transmission to household contacts. In our epidemiological cohort study (May 2022-November 2023), we surveyed registered index case vaccination status and test results for contacts (testing on day 0, and on day 7 for negative contacts) and calculated the secondary attack rate (SAR), i.e., newly infected contacts/susceptible included contacts. The association of the independent variable, index case COVID-19 vaccination (yes/no), with household contact infection was determined using the adjusted odds ratio (aOR) and its 95% confidence interval (CI). We recorded 181 index cases and 314 contacts, of whom 250 agreed to participate; 16 contacts were excluded upon testing positive on day 0. Of the 234 included contacts, 49.1% were women, and the mean (SD) age was 51.9 (19.8) years. The overall SAR of 37.2% (87/234) was lower in the contacts of both vaccinated index cases (34.9% vs. 63.2%; p = 0.014) and index cases with a previous SARS-CoV-2 infection history (27.0% vs. 46.3%; p = 0.002). Index case vaccination showed a protective effect against infection for their household contacts (aOR = 0.21; 95% CI: 0.07, 0.67). The household SAR was high when the Omicron variant circulated. Vaccinated index cases were less likely to transmit SARS-CoV-2 to their contacts.

Flame-Retardant GF-PSB/DOPO-POSS Composite with Low Dk/Df and High Thermal Stability for High-Frequency Copper Clad Applications.

In the field of high-frequency communications devices, there is an urgent need to develop high-performance copper clad laminates (CCLs) with low dielectric loss (Df) plus good flame retardancy and thermal stability. The hydrocarbon resin styrene-butadiene block copolymer (PSB) was modified with the flame-retardant 9,10-dihydro-9-oxa-10-phosphaphenanthrene-10-oxide/polyhedral oligomeric silsesquioxanes (DOPO-POSS) to meet the demands of high-frequency and high-speed applications. The resulting DOPO-POSS-modified PSB was used as the resin matrix along with other additives to fabricate PSB/DOPO-POSS laminates. At a high-frequency of 10 GHz, the laminates containing 20 wt.% of DOPO-POSS and with a thickness of 0.09 mm exhibited a Df of 0.00328, which is much lower compared with the commercial PSB/PX-200 composite (Df: 0.00498) and the PSB without flame retardancy (Df: 0.00453). Afterwards, glass fiber cloth (GF) was used as a reinforcing material to manufacture GF-PSB/DOPO-POSS composite laminates with a thickness of 0.25 mm. The flame retardancy of GF-PSB/DOPO-POSS composite laminate reached vertical burning (UL-94) V-1 grade, and GF-PSB/DOPO-POSS exhibited higher thermal and dynamic mechanical properties than GF-PSB/PX-200. The results of a limited oxygen index (LOI) and self-extinguishing time tests also demonstrated the superior flame-retardant performance of DOPO-POSS compared with PX-200. The investigation indicates that GF-PSB/DOPO-POSS composite laminates have significant potential for use in fabricating a printed circuit board (PCB) for high-frequency and high-speed applications.

Comparison of the effectiveness of micromaterials and nanomaterials of lime as a stabilizing agent for expansive soils on the value of the plasticity index

Expansive soils are soils that have high shrinkage expansion. This condition is detrimental to buildings on it. Losses due to swelling and shrinkage in expansive soils require stabilization with quicklime with varying particle sizes of materials needed to reduce soil swelling and shrinkage. Stabilization with nano has been widely used. However, the procurement of nanomaterials is very expensive. Therefore, this paper will explain the effectiveness of micromaterials when compared to nanomaterials. The main focus that will be discussed in this paper is how the comparison of plasticity index (PI) values between micromaterials and quicklime nanomaterials on expansive soils. The research was conducted at the Bandung State Polytechnic Laboratory. This research uses a stabilization method by adding 2% lime per particle size. The procurement price of nanomaterial was Rp. 2,500,000 and micromaterial was Rp. 1,500,000. The plasticity index (PI) test results obtained are the original soil is 35.68%; mixed soil 2% micromaterial lime is 11.51%; and mixed soil 2% nanomaterial lime is 7.62%. The results of PI values &lt; 12 both include low expansive, so the more effective result is the PI condition of adding 2% micromaterial lime with the procurement of cheaper prices.

Development of a machine learning tool to predict the risk of incident chronic kidney disease using health examination data.

Chronic kidney disease (CKD) is characterized by a decreased glomerular filtration rate or renal injury (especially proteinuria) for at least 3 months. The early detection and treatment of CKD, a major global public health concern, before the onset of symptoms is important. This study aimed to develop machine learning models to predict the risk of developing CKD within 1 and 5 years using health examination data. Data were collected from patients who underwent annual health examinations between 2017 and 2022. Among the 30,273 participants included in the study, 1,372 had CKD. Demographic characteristics, body mass index, blood pressure, blood and urine test results, and questionnaire responses were used to predict the risk of CKD development at 1 and 5 years. This study examined three outcomes: incident estimated glomerular filtration rate (eGFR) <60 mL/min/1.73 m2, the development of proteinuria, and incident eGFR <60 mL/min/1.73 m2 or the development of proteinuria. Logistic regression (LR), conditional logistic regression, neural network, and recurrent neural network were used to develop the prediction models. All models had predictive values, sensitivities, and specificities >0.8 for predicting the onset of CKD in 1 year when the outcome was eGFR <60 mL/min/1.73 m2. The area under the receiver operating characteristic curve (AUROC) was >0.9. With LR and a neural network, the specificities were 0.749 and 0.739 and AUROCs were 0.889 and 0.890, respectively, for predicting onset within 5 years. The AUROCs of most models were approximately 0.65 when the outcome was eGFR <60 mL/min/1.73 m2 or proteinuria. The predictive performance of all models exhibited a significant decrease when eGFR was not included as an explanatory variable (AUROCs: 0.498-0.732). Machine learning models can predict the risk of CKD, and eGFR plays a crucial role in predicting the onset of CKD. However, it is difficult to predict the onset of proteinuria based solely on health examination data. Further studies must be conducted to predict the decline in eGFR and increase in urine protein levels.

The effect of various spices on functional properties of plant-based cheese analogue

This research purpose was to study the effects of various spices on functional properties of plant-based cheese analogue (PBCA). PBCA was made from Geotrichum candidum (Gc: 3%), glucono-δ-lactone (GDL: 0.25%), and bromelain: 8 × 105 U/g. The sample was commercial dairy cheese (control), PBCA 1 (Gc + bromelain), PBCA 2 (GDL + bromelain), PBCA 3 (Gc + GDL), and PBCA 4 (Gc + GDL + bromelain). The results of observations processed using ANOVA and DMRT. In this study, different spices affect the chemical and sensory features of PBCA (p<0.05). The chemical features results were ash content (1.92%; 1.51%; 1.57%; 1.74%; 1.39%), fat content (29.7%; 25.0%; 24.9%; 26.7%; 24.7%), moisture content (55.4%; 49.6%; 50.1%; 47.3%; 50.3%), protein content (6.39%; 17.5%; 17.2%; 19.5%; 17.0%), and salt content (0.69%; 0.29%; 0.31%; 0.32%; 0.28%), respectively. The sensory features results were appearance (7.52; 7.18; 7.27; 7.48; 6.85), flavor (8.05; 7.68; 7.33; 7.67; 7.71), creaminess (7.73; 7.35; 7.33; 7.67; 7.25), firmness (6.40; 7.04; 6.37; 6.66; 6.58), spread ability (7.82; 7.48; 7.40; 7.78; 7.25), and overall (8.44; 7.91; 7.80; 7.48; 8.03), respectively. The effectiveness index test result was 1.00; 0.45; 0.53; 0.00; and 1.00, respectively. In conclusion, PBCA 4 was the best treatment.

Mixture design for eco-friendly hybrid clay treated with two stabilizers based on water absorption and retention of stabilizers

Major challenges are encountered when using high-water-content clays as construction materials owing to their low shear strength and high compressibility. Processes, such as sun drying and stabilization using chemical agents, can address the problems met with these clays during their conveyance and reuse. However, natural dehydration is a time-consuming process, and chemical stabilization can lead to an increase in the hydrogen ion index (pH) of the surrounding environment. Thus, the aim of this study is to improve the performance of high-water-content clay using a combination of green and byproduct materials, specifically bamboo chips and fly ash, which are cost-effective and eco-friendly. No previous study has attempted to design a mixture for the purpose of improving the performance of clay based on the water absorption and retention performance of stabilizers. In this study, the water absorption and retention rate (Wab), which is the mass of the water absorbed and retained by 1 g of each stabilizer, was measured by removing the unabsorbed and unretained water through filtration under vacuum suction. A series of Cone Index tests were conducted on clay, separately treated with bamboo chips and fly ash, and the tests demonstrated a correlation among the Cone Index (qc), Wab, and amount of stabilizer added (Ast). A novel mixture design was proposed for hybrid-treated clay, using the two types of stabilizers in the composite, based on parameter β, which is defined as the sum of the product (Wab/100)×(Ast/100) of each stabilizer used in the hybrid-treated clay. The results of the Cone Index tests performed on the hybrid-treated clay showed that the proposed mixture design can predict the combinations of certain amounts of each stabilizer needed to attain the target qc with minimal errors. The results of pH measurement tests indicated that the hybrid-treated clays produced by the proposed mixture design were more eco-friendly than fly ash-treated clays. Furthermore, the theoretical maximum CO2 fixation capacities of the hybrid-treated clays were evaluated to assess their feasibility as CO2-fixing materials.

Predictors of Organizational Commitment and its Impact on Organizational Citizenship Behavior in the Chemical Manufacturing Industry

Purpose: The purpose of this research is to determine the effect of predictors of organizational commitment which consist of job satisfaction, perceived organizational support and organizational culture as well as their impact on organizational citizenship behavior in the chemical manufacturing industry sector in Indonesia. Design/Methodology/Approach: The research uses a quantitative approach through survey methods and data analysis methods using SEM PLS. The population in this study were employees at seven chemical manufacturing factories in Cilegon City, which is the largest chemical manufacturing cluster area in Indonesia. The number of samples analyzed was 223 employees who worked in middle management positions. The research results show that of the 43 indicators used to measure each latent variable, there are 3 indicators of organizational culture variables that are invalid. Meanwhile, for reliability, everything is included in the reliable criteria. Findings: The goodness of fitness index test results show that the model has a good GoF index. The magnitude of the influence of job satisfaction, perceived organizational support and organizational culture on organizational commitment is 88.10%, while the influence of job satisfaction, perceived organizational support, organizational culture and organizational commitment on organizational citizenship behavior is 93.6%. Of the seven direct hypotheses tested, it was found that all hypotheses were accepted, while of the three indirect hypotheses, one hypothesis was rejected, namely the mediating influence of organizational commitment in mediating organizational culture on OCB. Research, Practical &amp; Social Implications: In further research, in order to conduct a broader research with other objects. The research is expected to be used for all companies. Originality/Value: This research contributes to improving human resource management in the chemical manufacturing sector so that it can increase OCB through increasing the predictor variables tested in the research, including job satisfaction, organizational support, organizational culture and organizational commitment.

Insights into durability assessment for compacted soils treated with paper sludge ash-based stabilizers

To utilize industrial by-products in construction projects, soil stabilization using paper sludge ash-based stabilizers (PSASs) has recently been developed. PSASs can be manufactured by insolubilizing the heavy metals in original paper sludge (PS) ash, which is a waste product emitted from paper mills. This study assessed the durability of clays treated with PSASs in wet-dry or dry-wet environments through various evaluation tests. Initially, the particle size distributions (PSDs) in the clays that had undergone various pretreatments were assessed. Then, unconfined compression tests were performed on the treated clays that had undergone dry and wet curing cycles using demolded specimens. Finally, cone index tests were carried out on the treated clays that had undergone dry and wet curing cycles while the samples were constrained in molds. Based on the test results, the durability assessment of the clays treated with PSASs was discussed, considering the specifics of each evaluation test and contrasting the findings with those obtained for clays treated with cement. The generated PSDs were found to contain more fine particles as the time spent washing the samples prior to sieving was increased. The findings indicated that PSAS-treated clays will eventually become muddy, even though it is unlikely that these treated clays will ever be subjected to washing with water while being stirred after construction. The unconfined compression test results also demonstrated that, after several dry-wet cycles, the strength of the PSAS-treated specimens had decreased. It was revealed that clays treated with PSASs might be less resistant to dry-wet curing cycles than those treated with cement. However, PSAS-treated samples using unconfined compression test specimens are still being investigated and are a topic of debate because the strength development mechanisms of PSASs and cement are not similar. To address this issue, a series of cone index tests were conducted on samples treated with PSASsto examine the change in strength caused by the dry-wet curing process. The results of the cone index tests were different from those of the unconfined compression tests. The cone index test results revealed that the samples must be constrained during the assessment tests in order to assess the durability of PSAS-treated soils subjected to dry-wet curing. In addition, it was proposed in this study that the idea of maturity would be applicable to PSAS-treated soils with a range of curing temperatures and curing times as long as the soils were constrained during the assessment tests.

Prediction of acute lung injury assessed by chest computed tomography, oxygen saturation/fraction of inspired oxygen ratio, and serum lactate dehydrogenase in patients with COVID-19

IntroductionIn treating acute hypoxemic respiratory failure (AHRF) caused by coronavirus disease 2019 (COVID-19), clinicians choose respiratory therapies such as low-flow nasal cannula oxygenation, high-flow nasal cannula oxygenation, or mechanical ventilation after assessment of the patient's condition. Chest computed tomography (CT) imaging contributes significantly to diagnosing COVID-19 pneumonia. However, the costs and potential harm to patients from radiation exposure need to be considered. This study was performed to predict the quantitative extent of COVID-19 acute lung injury using clinical indicators such as an oxygenation index and blood test results. MethodsWe analyzed data from 192 patients with COVID-19 AHRF. Multiple logistic regression was used to determine correlations between the lung infiltration volume (LIV) and other pathophysiological or biochemical laboratory parameters. ResultsAmong 13 clinical parameters, we identified the oxygen saturation/fraction of inspired oxygen ratio (SF ratio) and serum lactate dehydrogenase (LD) concentration as factors associated with the LIV. In the binary classification of an LIV of ≥20 % or not and with the borderline LD = 2.2 × [SF ratio]−182.4, the accuracy, precision, diagnostic odds ratio, and area under the summary receiver operating characteristic curve were 0.828, 0.818, 23.400, and 0.870, respectively. ConclusionsThese data suggest that acute lung injury due to COVID-19 pneumonia can be estimated using the SF ratio and LD concentration without a CT scan. These findings may provide significant clinical benefit by allowing clinicians to predict acute lung injury levels using simple, minimally invasive assessment of oxygenation capacity and biochemical blood tests.

Enhancing canola oil's shelf life with nano-encapsulated Mentha aquatica extract for optimal antioxidant performance.

Incorporation of antioxidants, such as phenolic compounds into edible oils has limitations such as rapid release of phenolic compounds, low solubility, low penetration, low accessibility, and rapid degradation by environmental compounds. To solve this problem, the nano-encapsulation process is offering promising opportunities. In this research, for the first time, the phenolic extract of Mentha aquatica was nano-encapsulated in nano-emulsions coated with chitosan, Lepidium perfoliatum gum (LPG), and complex of chitosan and LPG (CCL) (1:1 ratio). Based on various tests (particle size measurement, ζ-potential, polydispersity index, encapsulation efficiency index, and intensity curve), the LPG coating was the most optimum option for nano-encapsulation compared to the other coatings. Thus, the LPG-assisted nano-encapsulated phenolic extract of M. aquatica was used to improve the oxidative stability of canola oil at three concentrations (100, 200, and 300 ppm). The results of peroxide value and anisidine index tests (as initial and secondary oxidation indicators, respectively) showed that the nano-encapsulation improved the antioxidant effect of M. aquatica when compared with free extract in canola oil. In a comparative approach, the best sample was obtained from the LPG-assisted nano-encapsulated extract (200 ppm) due to the release of more phenolic compounds. The findings from this study showcase how nano-encapsulation enhances the efficacy of antioxidants in edible oils.