Consistency Index Research Articles

Establishing a prognostic prediction model for patients with septic shock based on the completion time of fluid resuscitation and the negative fluid balance volumes

To explore the relationship between the completion time of fluid resuscitation as well as negative fluid balance volumes and the prognosis of patients with septic shock, and to try to construct a prediction model based on the completion time of fluid resuscitation and negative fluid balance volumes, and to verify the predictive efficacy of the model on the prognosis of patients with septic shock. Patients with septic shock admitted to Wuxi People's Hospital from April 2020 to April 2023 were selected. The general data (gender, age, body mass index, infection site), pathological indicators on admission, the difference of acute physiology and chronic health evaluation II (APACHE II) and sequential organ failure assessment (SOFA) between admission and 24 hours after fluid resuscitation, the completion time of fluid resuscitation and negative fluid balance volume were recorded. Multivariate Logistic analysis was used to screen the influencing factors of the prognosis of patients with septic shock, and a nomogram model was established. Bootstrap method was used for internal validation of the model. The consistency index, calibration curve and receiver operator characteristic curve (ROC curve) were used to evaluate the accuracy and prediction efficiency of the model. A total of 96 patients with septic shock were enrolled, 38 patients died and 58 patients survived at 28 days. Compared with the survival group, the difference of APACHE II score, SOFA score, the proportion of fluid resuscitation completed within 1 to 3 hours, and the proportion of negative fluid balance volume -500 to -250 mL per day in the death group were lower, and the differences were statistically significant (all P < 0.05). Multivariate Logistic analysis showed that the completion time of fluid resuscitation was a risk factor for the prognosis of patients with septic shock [odds ratio (OR) = 26.285, 95% confidence interval (95%CI) was 9.984-76.902, P < 0.05]. The difference of APACHE II score (OR = 0.045, 95%CI was 0.015-0.131), SOFA score (OR = 0.056, 95%CI was 0.019-0.165) between admission and 24 hours after fluid resuscitation, and negative fluid balance volume (OR = 0.043, 95%CI was 0.015-0.127) were protective factors for the prognosis of patients with septic shock (all P < 0.05). The model validation results showed that the consistency index was 0.681 (95%CI was 0.596-0.924), indicating good discrimination. The calibration curve showed that the calibration curve fitted well with the ideal curve. The ROC curve showed that the sensitivity of the nomogram model for predicting the death of patients with septic shock was 83.7%, the specificity was 97.2%, and the area under the ROC curve (AUC) was 0.931 (95%CI was 0.846-0.985), indicating that the model had good prediction efficiency. The completion time of fluid resuscitation and negative fluid balance volumes are related to the prognosis of septic shock patients, and the alignment diagram model improve the identification of the risk of death in septic shock patients.

Development of a prognostic model for long-term survival of young patients with bladder cancer: a retrospective analysis of the SEER Database

ObjectivesThis study aims to present the clinical characteristics of young patients with bladder cancer (YBCa), evaluate related risk factors and construct a nomogram based on data acquired from the Surveillance,...

LASSO regression based risk prediction model for postoperative control in chronic sinusitis with nasal polyps

Objective:To establish a risk prediction model for postoperative control of chronic sinusitis with nasal polyps. Methods:Retrospective analysis was done on the clinical of patients who underwent endoscopic sinus surgery in the Department of Otolaryngology of the First Affiliated Hospital of Soochow University during August 2020 to June 2021. Patients were classified into uncontrolled group（40 cases） and controlled group（104 cases）, based on the European Position Paper on rhinosinusitis and nasal polyps（EPOS 2020）, and the clinical and pathological characteristics of the two groups were compared. The least absolute shrinkage and selection operator（LASSO） regression was used to screen the factors that might affect the prognosis of chronic sinusitis with nasal polyps and multivariate logistic regression was performed. The Receiver operating characteristic curve（ROC） was ploted, the area under curve（AUC） was calculated, and the ability of the prediction model was evaluated using the consistency index（C-index）. Results:A total of 144 patients with CRS with nasal polyps 1 year after operation were enrolled in this study, including 40 patients in the uncontrolled group and 104 patients in the control group（complete control or partial control）. 12 risk factors（allergic rhinitis, allergic dermatitis, olfactory dysfunction, E/M ratio, serum alkaline phosphatase, number of pathological eosinophils, number of pathological lymphocytes, number of plasma cells in pathological tissues, percentage of eosinophils in pathological tissues, stromal edema, basement membrane thickening, and hyperplasia of goblet cells） were found to be associated with postoperative recurrence of chronic sinusitis with nasal polyps. The seven variables（allergic rhinitis, olfactory dysfunction, E/M ratio, pathological eosinophilic percentage, stromal edema, basement membrane thickening, and hyperplasia of goblet cell） were extracted after reduced by LASSO regression. Multivariate logistic regression analysis showed that the 7 variables were risk factors for postoperative recurrence of chronic sinusitis with nasal polyps（P<0.05）. Nomogram prediction model for postoperative recurrence of chronic sinusitis with nasal polyps were established based on the 7 variables above. The verification results of the model showed that the C-index and AUC of the model were 0.937 and 0.937（95%CI 0.901-0.973）, suggesting that the nomogram model had a relatively accurate prediction ability. Conclusion:Combined with the basic clinical data of patients, the prediction model established in this study can facilitate the risk prediction of postoperative control of chronic sinusitis with nasal polyps, and thus help to formulate better therapeutic plans for patients.

Who would be the winner? A prognostic nomogram for predicting the benefit of postoperative radiotherapy ± chemotherapy in patients with locally advanced gastric cancer: TROD-02-01 study

We aimed to develop a basic, easily applicable nomogram to improve the survival prediction of the patients with stage II/III gastric cancer (GC) and to select the best candidate for postoperative radiotherapy (RT). In this multicentric trial, we retrospectively evaluated the data of 1597 patients with stage II/III GC after curative gastrectomy followed by postoperative RT±chemotherapy (CT). Patients were divided into a training set (n=1307) and an external validation set (n=290). Nomograms were created based on independent predictors identified by Cox regression analysis in the training set. The consistency index (C-index) and the calibration curve were used to evaluate the discriminative ability and accuracy of the nomogram. A nomogram was created based on the predictive model and the identified prognostic factors to predict 5-year cancer-specific survival (CSS) and progression-free survival (PFS). The multivariate Cox model recognized lymph node (LN) involvement status, lymphatic dissection (LD) width, and metastatic LN ratio as covariates associated with CSS. Depth of invasion, LN involvement status, LD width, metastatic LN ratio, and lymphovascular invasion were the factors associated with PFS. Calibration of the nomogram predicted both CSS and PFS corresponding closely with the actual results. In our validation set, discrimination was good (C-index, 0.76), and the predicted survival was within a 10% margin of ideal nomogram. In our relatively large cohort, we created and validated both CSS and PFS nomograms that could be useful for underdeveloped or developing countries rather than Korea and Japan, where the D2 gastrectomy is routinely performed. This could serve as a true map for oncologists who must make decisions without an experienced surgeon and a multidisciplinary tumor board.

Assessing the accuracy of health facility typology in representing the availability of health services: a case study in Mali

IntroductionUsing health facility types as a measure of service availability is a common approach in international standards for health system policy and planning. However, this proxy may not accurately reflect...

Validation of the 9th edition of the TNM staging system for non-small cell lung cancer with lobectomy in stage IA-IIIA.

The 9th edition of tumour-node-metastasis (TNM) staging for lung cancer was announced by Prof Hisao Asamura at the 2023 World Conference on Lung Cancer in Singapore. The purpose of this study was to externally validate and compare the latest staging of lung cancer. We collected 19193 patients with stage IA-IIIA non-small cell lung cancer (NSCLC) who underwent lobectomy from the Surveillance, Epidemiology and End Results database. Survival analysis by TNM stages was compared using the Kaplan-Meier method and further analysed using univariable and multivariable Cox regression analyses. Receiver operating characteristic curves were used to assess model accuracy, Akaike information criterion, Bayesian information criterion and consistency index were used to compare the prognostic, predictive ability between the current 8th and 9th edition TNM classification. The 9th edition of the TNM staging system can better distinguish between IB and IIA patients on the survival curve (P < 0.0001). In both univariable and multivariable regression analysis, the 9th edition of the TNM staging system can differentiate any 2 adjacent staging patients more evenly than the 8th edition. The 9th and the 8th edition TNM staging have similar predictive power and accuracy for the overall survival of patients with NSCLC [TNM 9th vs 8th, area under the curve: 62.4 vs 62.3; Akaike information criterion: 166182.1 vs 166131.6; Bayesian information criterion: 166324.3 vs 166273.8 and consistency index: 0.650 (0.003) vs 0.651(0.003)]. Our external validation demonstrates that the 9th edition of TNM staging for NSCLC is reasonable and valid. The 9th edition of TNM staging for NSCLC has near-identical prognostic accuracy to the 8th edition.

Risk factors for one-year mortality following discharge in patients with acute aortic dissection: development and validation of a predictive model in a cross-sectional study

PurposeThis study was aimed to identify the risk factors that influence the mortality risk in patients with acute aortic dissection (AAD) within one year after discharge, and aimed to construct a predictive model for assessing mortality risk.MethodsThe study involved 320 adult patients obtained from the Medical Information Mart for Intensive Care (MIMIC) database. Logistic regression analysis was conducted to identify potential risk factors associated with mortality in AAD patients within one year after discharge and to develop a predictive model. The performance of the predictive model was assessed using the receiver operating characteristic curve (ROC), calibration curve, and decision curve analysis (DCA). To further validate the findings, patient data from the First Affiliated Hospital of Guangxi Medical University (157 patients) were analyzed.ResultsUnivariate and multivariate logistic regression analyses revealed that gender, length of hospital stay, highest blood urea nitrogen (BUN_max), use of adrenaline, and use of amiodarone were significant risk factors for mortality within one year after discharge (p < 0.05). The constructed model exhibited a consistency index (C-index) and an area under the ROC curve of 0.738. The calibration curve and DCA demonstrated that these indicators had a good degree of agreement and utility. The external validation results of the model also indicated good predictability (AUC = 0.700, p < 0.05).ConclusionThe personalized scoring prediction model constructed by gender, length of hospital stays, BUN_max levels, as well as the use of adrenaline and amiodarone, can effectively identify AAD patients with high mortality risk within one year after discharge.

A nomogram prediction model for mild cognitive impairment in non-dialysis outpatient patients with chronic kidney disease

Background The high prevalence of mild cognitive impairment (MCI) in non-dialysis individuals with chronic kidney disease (CKD) impacts their prognosis and quality of life. Objective This study aims to investigate the variables associated with MCI in non-dialysis outpatient patients with CKD and to construct and verify a nomogram prediction model. Methods 416 participants selected from two hospitals in Chengdu, between January 2023 and June 2023. They were categorized into two groups: the MCI group (n = 210) and the non-MCI (n = 206). Univariate and multivariate binary logistic regression analyses were employed to identify independent influences (candidate predictor variables). Subsequently, regression models was constructed, and a nomogram was drawn. The restricted cubic spline diagram was drawn to further analyze the relationship between the continuous numerical variables and MCI. Internally validated using a bootstrap resampling procedure. Results Among 416 patients, 210 (50.9%) had MCI. Logistic regression analysis revealed that age, educational level, occupational status, use of smartphones, sleep disorder, and hemoglobin were independent influencing factors of MCI (all p<.05). The model’s area under the curve was 0.926,95% CI (0.902, 0.951), which was a good discriminatory measure; the Calibration curve, the Hosmer–Lemeshow test, and the Clinical Decision Curve suggested that the model had good calibration and clinical benefit. Internal validation results showed the consistency index was 0.926, 95%CI (0.925, 0.927). Conclusion The nomogram prediction model demonstrates good performance and can be used for early screening and prediction of MCI in non-dialysis patients with CKD. It provides valuable reference for medical staff to formulate corresponding intervention strategies.

New Insight into Food-Grade Emulsions: Candelilla Wax-Based Oleogels as an Internal Phase of Novel Vegan Creams.

Cream-type emulsions containing candelilla wax-based oleogels (EC) were analyzed for their physicochemical properties compared to palm oil-based creams (EP). The microstructure, rheological behavior, stability, and color of the creams were determined by means of non-invasive and invasive techniques. All the formulations exhibited similar color parameters in CIEL*a*b* space, unimodal-like size distribution of lipid particles, and shear-thinning properties. Oleogel-based formulations were characterized by higher viscosity (consistency index: 172-305 mPa·s, macroscopic viscosity index: 2.19-3.08 × 10-5 nm-2) and elasticity (elasticity index: 1.09-1.45 × 10-3 nm-2), as well as greater resistance to centrifugal force compared to EP. Creams with 3, 4, or 5% wax (EC3-5) showed the lowest polydispersity indexes (PDI: 0.80-0.85) 24 h after production and the lowest instability indexes after environmental temperature changes (heating at 90 °C, or freeze-thaw cycle). EC5 had particularly high microstructural stability. In turn, candelilla wax content ≥ 6% w/w accelerated the destabilization processes of the cream-type emulsions due to disintegration of the interfacial layer by larger lipid crystals. It was found that candelilla wax-based lipids had great potential for use as palm oil substitutes in the development of novel vegan cream analogues.

Development and validation of a cognitive dysfunction risk prediction model for the abdominal obesity population.

This study was aimed to develop a nomogram that can accurately predict the likelihood of cognitive dysfunction in individuals with abdominal obesity by utilizing various predictor factors. A total of 1490 cases of abdominal obesity were randomly selected from the National Health and Nutrition Examination Survey (NHANES) database for the years 2011-2014. The diagnostic criteria for abdominal obesity were as follows: waist size ≥ 102 cm for men and waist size ≥ 88 cm for women, and cognitive function was assessed by Consortium to Establish a Registry for Alzheimer's Disease (CERAD), Word Learning subtest, Delayed Word Recall Test, Animal Fluency Test (AFT), and Digit Symbol Substitution Test (DSST). The cases were divided into two sets: a training set consisting of 1043 cases (70%) and a validation set consisting of 447 cases (30%). To create the model nomogram, multifactor logistic regression models were constructed based on the selected predictors identified through LASSO regression analysis. The model's performance was assessed using several metrics, including the consistency index (C-index), the area under the receiver operating characteristic (ROC) curve (AUC), calibration curves, and decision curve analysis (DCA) to assess the clinical benefit of the model. The multivariate logistic regression analysis revealed that age, sex, education level, 24-hour total fat intake, red blood cell folate concentration, depression, and moderate work activity were significant predictors of cognitive dysfunction in individuals with abdominal obesity (p < 0.05). These predictors were incorporated into the nomogram. The C-indices for the training and validation sets were 0.814 (95% CI: 0.875-0.842) and 0.805 (95% CI: 0.758-0.851), respectively. The corresponding AUC values were 0.814 (95% CI: 0.875-0.842) and 0.795 (95% CI: 0.753-0.847). The calibration curves demonstrated a satisfactory level of agreement between the nomogram model and the observed data. The DCA indicated that early intervention for at-risk populations would provide a net benefit, as indicated by the line graph. Age, sex, education level, 24-hour total fat intake, red blood cell folate concentration, depression, and moderate work activity were identified as predictive factors for cognitive dysfunction in individuals with abdominal obesity. In conclusion, the nomogram model developed in this study can effectively predict the clinical risk of cognitive dysfunction in individuals with abdominal obesity.

Selena – a new cotton variety

The aim of this research was to study the productive potential and evaluate the fiber properties of the new Selena cotton variety. The variety was studied in competition variety trials conducted in 2017-2022 in the experimental field of the Field Crops Institute in the town of Chirpan. In the IASAS system the variety was tested in 2021-2022. It was approved as a new cotton variety in 2023. Selena variety was created by remote hybridization from the crossing of the allotetraploid Gossypium thurberi Tod. × Gossypium raimondii Ulbr. with Dorina variety and subsequent backcrossing with Darmi variety. Selena variety is an early and productive new cotton variety with improved fiber quality. The vegetation period was 106-111 days. In seed cotton yield of 1663 kg/ha, on average for 6 years, it surpassed the standard cultivar Chirpan-539 by 12.3%. According to the IASAS data, in seed cotton yield of 2163 kg/ha it exceeded the two standard cultivars, Chirpan-539 - by 2.2%, Avangard-264 - by 1.3%, the average standard (average of the two standards) - by 1.8%. In lint yield of 95.5 kg/ha Selena variety was equal to Avangard-264, surpassed Chirpan-539 by 2.9% and respectively the average standard - by 1.4%. Selena variety had a number of valuable technological fiber qualities and in some indicators surpassed both standard cultivars. Compared to the two standard cultivars it had better Spinning Consistency Index (SCI), greater Upper Half Mean Length and fiber strength, lower micronaire, better spectroscopy with RD difference reflection than Chirpan-539, less yellowness than Avangard-264 and better fiber whiteness. Selena variety combines better productivity with better technological fiber qualities than the standard cultivars, which defines it as a new achievement in the selection of cotton in our country.

Effects of pre-formulation and post-cooking method on the rheological and gelation properties of 3D printed chicken products

This study investigated the influence of oil type (olive, soybean, and peanut oil) and post-cooking methods (oven bake and microwave) on the quality of 3D printed chicken meat products. The Ostwald-de-Waele model was used to describe the flow behavior of chicken meat paste (R2 > 0.995). Oil-fortified groups present significantly lower consistency index (K) and flow behavior index (n), indicating better fluidity. A modified Cox-Merz rule was applied by multiplying angular frequency with shift factors (αSF). Surprisingly, the values of αSF are well-correlated with accuracy parameters of 3D printed cubes (|r| >0.8). For post-heating methods, baking results in higher fluid loss but contributes to a smoother surface. The microwaved gels showed better fluid retention ability and higher accuracy but lost the detail shape of the 3D printing model. Overall, the PO (peanut oil) meat emulsion group presented better textural properties and flat surfaces than other oil-added counterparts.

Rheological and sensorial behavior of tomato product enriched with pea protein and olive powder

In this study, a new functional product using Mediterranean ingredients (tomato, tomato peel powder and olive powder) was formulated where two different concentrations of protein (1 and 2%) and peel (2 and 4%) were tested. Olive powder was kept at a constant concentration of 2%. Physico-chemical, Rheological, and Sensorial analysis were carried out on the formulated samples. Soluble protein content was found as the highest in the sample containing 4% peel and 2% protein and it was affected by the pH and tomato peel concentration. Rheological results reveal shear-thinning behavior, as defined by the Herschel-Bulkley model, with protein and peel concentrations having a major influence on yield stress and viscosity. A positive trend was noticed between apparent viscosity and peel concentration, meantime protein concentration affected apparent viscosity adversely. Contrary relation between consistency index (k) values and apparent viscosity illustrate the complex interaction between protein and peel, particularly at higher concentrations. Furthermore, Principal Component Analysis (PCA) was used to investigate the complicated sensory landscape of tomato products with different quantities of pea protein and tomato peel. While higher tomato peel and protein levels have no direct impact on rheological qualities, they do add to astringency and sourness, which influences overall acceptability. Remarkably, the sample with the greatest quantities of peel and protein exhibits a delicate balance, with a loss in perceived tomato taste intensity and overall acceptability offsetting an increase in astringency. In terms of overall acceptability, the most preferred beverage was selected as the sample formulated with 2% peel and 1% protein.

Isolation of Protein and Fiber from Hot Pepper Seed Oil Byproduct To Enhance Rheology, Emulsion, and Oxidative Stability of Low-Fat Salad Dressing.

This research aimed to explore the potential utilization of protein (P) and fiber (F) extracted from cold-pressed hot pepper seed oil byproduct (HPOB) in the enhancement of the rheological properties, emulsion stability, and oxidative stability of a low-fat salad dressing with 10% oil content. The assessment involved the examination of several aspects, including the physical qualities such as emulsion stability, rheological behavior, and particle size as well as the microstructure and oxidative stability. It is worth mentioning that all emulsions had desirable characteristics, including shear-thinning behavior characterized by a consistency index ranging from 6.82 to 22.32 Pa s, as well as viscoelasticity and recoverability. These qualities were notably improved with the addition of P and F of HBOP. During the thermal stability testing, it was observed that the low-fat dressing containing 1% P-1F exhibited minor changes in the G* value, indicating its exceptional emulsion stability. The control salad dressings in C1 samples contained 30% oil. (B): C2: samples containing 10% oil (low-fat salad dressing sample) exhibited ζ-potential values of -34.70 and -46.70 mV. The samples 1P-1F and 2P-1F exhibited the highest ζ-potential values. Furthermore, the increase in F resulted in a reduction in droplet size and elicited elevated values for the induction period (IP), with the exception of samples containing 1% protein, 3% fiber, and 10% oil (1P-3F). The salad dressings that included P-F exhibited enhanced oxidative stability, demonstrated by their longer IP (ranging from 5.11 to 7.04 h) compared to the control samples. The formulation consisting of samples contained 1% protein, 1% fiber, and 10% oil (1P-1F) and samples contained 2% protein, 1% fiber, and 10% oil (2P-1F) exhibited superior ζ-potential, emulsion stability, and recovery rate compared to other formulations. The findings of this investigation indicate that the interaction of proteins and fibers extracted from HPOB exhibits the potential to enhance the rheological characteristics, emulsion stability, and oxidative stability of low-fat salad dressing.

SÜKROZUN STEVİA VE SÜKRALOZ İLE KISMEN DEĞİŞTİRİLMESİNİN ELMA MARMELATININ FİZİKOKİMYASAL VE YAPISAL-MEKANİK ÖZELLİKLERİ ÜZERİNDEKİ ETKİSİ

In this study, low-sugar apple marmalade formulations were developed by partial replacement of sucrose with stevia and sucralose. Their rheological, textural, physicochemical properties and microstructures were evaluated. The concentration of sweeteners was found to have a significant effect on the physicochemical and rheological properties of the formulations. The hardness of marmalades decreased with addition of sweeteners. Herschel–Bulkley model was found to be the best model describing rheological behavior. The consistency index decreased with increasing sweeteners substitution, whereas the flow behavior index showed an increasing trend with the increase of the sweeteners content. Additionally, the microstructure of marmalades with sweetener substitution exhibited a porous structure in the gel network. The increase in sucralose concentration resulted in more surface deformation resulting in weaker gel formation than stevia. Marmalade prepared with 50% stevia substitution was found the best combination and resulted in good sensory properties like marmalade samples containing 500 g sugar.

ŞEKER VE YAĞ İÇERİĞİNİ AZALTMANIN TOP KEKLERİN FİZİKSEL VE DUYUSAL ÖZELLİKLERİNE ETKİSİ

In this study, the effect of reducing the sugar (50%, 100%) and fat (50%, 75%) content on the physical and sensory properties of cupcakes was investigated by using stevia and polydextrose as sugar and fat replacers, respectively. While reducing the fat content increased the density of the cake batter, it caused a decrease in the consistency values such as firmness, consistency, cohesiveness and viscosity index. Reducing the sugar content had the opposite effect, decreasing the batter density and significantly increasing the batter consistency. Reducing only sugar and only fat content showed better results than control, while reducing both sugar and fat content together negatively affected the textural properties of cakes. In general, reducing only sugar content decreased sensory satisfaction compared to reducing only fat content. The samples with only 50% reduced fat content were most liked in terms of all sensory parameters. It was concluded that only sugar or only fat contents can be reduced by 50% in cake production, especially considering the specific volume, textural and sensory properties.

Building a sustainable future: The role of additive manufacturing in civil construction

With the advent of Industry 4.0, 3D printing is emerging as a revolutionary technology in various industrial sectors. The ability to gradually deposit material allows the creation of complex shapes without waste and the need for molds. This, coupled with cost reduction, opens up significant opportunities in the construction sector. To minimize waste on the construction site, optimize construction time, and positively contribute to the environment, this work focuses on developing a system capable of three-dimensional printing of objects composed of a desktop-scale 3D printer prototype. The 3D printer developed in this project is integrated with two other crucial subsystems. The first is responsible for extruding a cementitious composite, while the second performs the homogenization of the mixture during material preparation. The Cartesian robot, essential for 3D printing, was constructed based on mechanical and electrical designs and connected to the pumping subsystem via a 1⁄2-inch diameter hose. To ensure the system's viability, it was essential to define flow and printing speed parameters to ensure the extrudability and constructibility of composite materials. The process included preparing a conventional mortar with mixing ratios of 1:0.33:1.33:0.01 (cement, water, sand, and superplasticizer additive). The pumping subsystem was calibrated through flow tests on the injection pump, resulting in mass flows of 4 and 5 Kg/min. Printing speeds on the Cartesian robot were parameterized at 10, 30, and 50 mm/s. This led to the creation of samples named AE1 – 10, AE1 – 30, AE1 – 50 mm/s (for a flow rate of 5 Kg/min) and AE2 – 30, AE2 – 50 mm/s (for a flow rate of 4 Kg/min), along with AR – 1 and AR – 2 samples, which were collected in the mixer without extrusion. The system's validation involved printing an object with dimensions of 400 ×400×80 mm (length x width x height). After printing, the object's measurements were analyzed to assess the cementitious material's extrudability and the constructibility of the layers. Tests were conducted in the fresh and hardened states of the mortar, including measurements of consistency index and tensile and compressive strength. The results demonstrated that replacing traditional methods with 3D printing on the construction site is feasible, reducing waste and decreasing construction time. This directly contributes to a cleaner and more sustainable construction process. 3D printing represents a promising innovation in the construction sector, offering significant advantages in terms of efficiency, waste reduction, and positive environmental impact. This work demonstrates the viability of this technology and its potential to transform the construction industry toward more sustainable and effective practices.

Modeling of a Process Window for Tailored Reinforcements in Overmolding Processes

This study explores cost-effective and customized composite applications by strategically placing carbon fiber-reinforced thermoplastics in multi-material designs. The focus is on developing a model for the simultaneous processing of non-reinforced and reinforced thermoplastic layers, with the aim of identifying essential parameters to minimize insert flow and ensure desired fiber orientation and positional integrity. The analysis involves an analytical solution for two layered power-law fluids in a squeeze flow setup, aiming to model the combined flow behavior of Newtonian and pseudo-plastic fluids, highlighting the impact of the non-Newtonian nature. The behavior reveals a non-linear trend in the radial flow ratio towards the logarithmic consistency index ratio compared to a linear trend for Newtonian fluids. While a plateau regime of consistency index ratios presents challenges in flow reduction for both layers, exceeding this ratio, depending on the height ratio of the layers, enables a viable overmolding process. Therefore, attention is required when selectively placing tailored composites with long-fiber-reinforced thermoplastics or unidirectional reinforcements to avoid operating in the plateau region, which can be managed through appropriate cavity or tool designs.

Predicting the recurrence of usual-type cervical adenocarcinoma using a nomogram based on clinical and pathological factors: a retrospective observational study.

Usual-type cervical adenocarcinoma is the most frequent type of adenocarcinoma, and its prevalence is increasing worldwide. Tumor recurrence is the leading cause of mortality; therefore, recognizing the risk factors for cervical cancer recurrence and providing effective therapy for recurrent cervical cancer are critical steps in increasing patient survival rates. This study aimed to retrospectively analyze the clinicopathological data of patients with usual-type cervical adenocarcinoma by combining the diagnosis and treatment records after the initial treatment and recurrence. We retrospectively analyzed patients diagnosed with usual-type cervical adenocarcinoma who underwent radical hysterectomy and pelvic lymph node dissection at Shengjing Hospital of China Medical University between June 2013 and June 2022. We constructed a nomogram-based postoperative recurrence prediction model, internally evaluated its efficacy, and performed internal validation. This study included 395 participants, including 87 individuals with recurrence. At a 7:3 ratio, the 395 patients were divided into two groups: a training set (n = 276) and a validation set (n = 119). The training set was subjected to univariate analysis, and the risk variables for recurrence included smoking, ovarian metastasis, International Federation of Gynaecology and Obstetrics (FIGO) staging, lymphovascular space invasion, perineural invasion, depth of muscular invasion, tumor size, lymph node metastasis, and postoperative HPV infection months. The aforementioned components were analyzed using logistic regression analysis, and the results showed that the postoperative HPV infection month, tumor size, perineural invasion, and FIGO stage were independent risk factors for postoperative recurrence (p<0.05). The aforementioned model was represented as a nomogram. The training and validation set consistency indices, calculated using the bootstrap method of internal validation, were 0.88 and 0.86, respectively. The model constructed in this study predicted the postoperative recurrence of usual-type cervical cancer, as indicated by the receiver operating characteristic curve. The model demonstrated good performance, as evidenced by the area under the curve, sensitivity, and specificity values of 0.90, 0.859, and 0.844, respectively. Based on the FIGO staging, peripheral nerve invasion, tumor size, and months of postoperative HPV infection, the predictive model and nomogram for postoperative recurrence of usual-type cervical adenocarcinoma are precise and effective. More extensive stratified evaluations of the risk of cervical adenocarcinoma recurrence are still required, as is a thorough assessment of postoperative recurrence in the future.

Regional detection and assessment of chilling damage on maize considering land surface temperature, crop growth status and solar radiation changes

AbstractIncreased frequency and severity of chilling damage events pose potential risks to crop performance and productivity due to climate change. Accurate and real‐time access to chilling damage is important for crop growth and yield stability based on field's actual environment. To precisely identify regional chilling events and evaluate the impacts on crops, this study presents a model to estimate field air temperature in view of field crop situations. Land surface temperature, enhanced vegetation index, solar‐induced chlorophyll fluorescence and solar declination were involved in the model. With field simultaneous continuous monitoring and multisource fused remote sensing data, the model was calibrated and validated in Jiefangzha Irrigation Area (JIA) and Changchun City (CC) in North China, accompanied by the determination coefficient ≥0.756, root mean square error ≤0.782°C, relative error ≤0.041 and consistency index ≥0.902. Meanwhile, sensitivities of the model factors were determined through path analysis, where the factors performed according to the order solar‐induced chlorophyll fluorescence &gt;solar declination &gt;land surface temperature &gt; enhanced vegetation index. Using the validated model, chilling damage to maize was further detected in JIA and CC from 2010 to 2020. Results showed that the severity of chilling damage was greater in CC than in JIA, along with the sterile‐type occurring three events in JIA and seven in CC, while the delayed‐type only twice in JIA in 2012 and 2016, but five times in CC in 2013, 2014, 2016, 2017 and 2019, respectively, being consistent with local statistics. In response to chilling damage, enhanced vegetation index and solar‐induced chlorophyll fluorescence demonstrated the negative chilling effects on greenness and light use efficiency for fluorescence. Serious yield losses were caused, with yield‐reducing by 5.00% (Dehui, 2013), 19.00% (Jiutai, 2014), 21.65% (Suburban district, 2016), 8.83% (Shuangyang, 2017) and 2.19% (Jiutai, 2019) in CC. The linear relationship between yield and growing degree days was a bit weakened by chilling damage, with the determination coefficient varying from 0.614 to 0.531. The increasing rate of yield with growing degree days decreased from 20.365 kg/(°C·d) in non‐chilling damage years to 9.670 kg/(°C·d) in chilling damage years. These findings indicate that the presented model is especially adaptive for agricultural field environments, enabling rapid precision detection of chilling damage on crops at regional scales. It will provide references for gauging the impact of chilling damage on crops, finding efficient solutions to the stress and ensuring sustainable development of agriculture.