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Inflammatory response and nutritional status are associated with cancer development and progression. The present study aimed to evaluate the predictive ability of the fibrinogen-albumin ratio index (FARI) to the efficacy of neoadjuvant chemotherapy (NAC) for osteosarcoma. A retrospective analysis involving 752 consecutive osteosarcoma patients between 2012 and 2020 was performed. Data on serum fibrinogen, albumin levels, white blood cell count, platelet count, and alkaline phosphatase (ALP) before and after NAC were collected. The predictive value of the NAC efficacy in osteosarcoma was assessed by constructing a receiver operating characteristic (ROC) curve and calculating the area under the curve (AUC). Prognosis and its predictive factors were analyzed by Kaplan-Meier method and COX regression analysis. Nomogram was established according to selected variables. The predictive performance of the nomogram model was assessed using C-statistics. A total of 203 patients were included. ROC analysis showed that both FARI before NAC (preFARI; AUC = 0.594, = 0.032) and the change in FARI before and after NAC (dfFARI = preFARI-postFARI; AUC = 0.652, = 0.001) exhibited more favorable predictive ability than ALP and other inflammation markers. The preFARI was divided into the high group (>6.1%) and the low group (≤6.1%) based on the optimal cut-off value of 6.1%. Patients with a high preFARI showed significantly decreased metastasis-free survival (MFS) and disease-free survival (DFS) (all <0.01). In multivariable analysis, preFARI was an independent prognostic marker for patients with osteosarcoma. Predictive nomograms exhibited good ability to predict MFS (C-index = 0.748, se = 0.028) and DFS (C-index=0.727, se = 0.030). Our findings indicated that FARI exhibits the favorable predictive ability for the efficacy of NAC for osteosarcoma, which could support clinicians and patients in clinical decision-making and treatment optimization.

Lake Urmia is the 20th largest lake and the second largest hyper saline lake (before September 2010) in the world. It is also the largest inland body of salt water in the Middle East. Nevertheless, the lake has been in a critical situation in recent years due to decreasing surface water and increasing salinity. This study modeled the spatiotemporal changes of Lake Urmia in the period 2000–2013 using the multi-temporal Landsat 5-TM, 7-ETM+ and 8-OLI images. In doing so, the applicability of different satellite-derived indexes including Normalized Difference Water Index (NDWI), Modified NDWI (MNDWI), Normalized Difference Moisture Index (NDMI), Water Ratio Index (WRI), Normalized Difference Vegetation Index (NDVI), and Automated Water Extraction Index (AWEI) were investigated for the extraction of surface water from Landsat data. Overall, the NDWI was found superior to other indexes and hence it was used to model the spatiotemporal changes of the lake. In addition, a new approach based on Principal Components of multi-temporal NDWI (NDWI-PCs) was proposed and evaluated for surface water change detection. The results indicate an intense decreasing trend in Lake Urmia surface area in the period 2000–2013, especially between 2010 and 2013 when the lake lost about one third of its surface area compared to the year 2000. The results illustrate the effectiveness of the NDWI-PCs approach for surface water change detection, especially in detecting the changes between two and three different times, simultaneously.

Objective To establish nomogram based on inflammatory indices for differentiating intrahepatic cholangiocarcinoma (ICC) from hepatocellular carcinoma (HCC). Methods A cohort of 422 patients with HCC or ICC hospitalized at Xiangya Hospital between January 2014 and December 2018 was included in the study. Univariate and multivariate analysis was performed to identify the independent differential factors. Through combining these independent differential factors, a nomogram was established for differential diagnosis between ICC and HCC. The accuracy of nomogram was evaluated by using receiver operating characteristic (ROC) curve, calibration curve, and decision curve analysis (DCA). The results were validated using a prospective study on 98 consecutive patients operated on from January 2019 to November 2019 at the same institution. Results Sex (OR = 9.001, 95% CI: 3.268‐24.792, P < .001), hepatitis (OR = 0.323, 95% CI: 0.121‐0.860, P = .024), alpha‐fetoprotein (AFP) (OR = 0.997, 95% CI: 0.995‐1.000, P = .046), carbohydrate antigen 19‐9 (CA199) (OR = 1.016, 95% CI: 1.007‐1.025, P < .001), and aspartate transaminase‐to‐neutrophil ratio index (ANRI) (OR = 0.904, 95% CI: 0.843‐0.969, P = .004) were the independent differential factors for ICC. Nomogram was established with well‐fitted calibration curves through incorporating these 5 factors. Comparing model 1 including gender, hepatitis, AFP, and CA199 (C index = 0.903, 95% CI: 0.849‐0.957) and model 2 enrolling AFP and CA199 (C index = 0.850, 95% CI: 0.791‐0.908), the nomogram showed a better discrimination between ICC and HCC, with a C index of 0.920 (95% CI, 0.872‐0.968). The results were consistent in the validation cohort. DCA also confirmed the conclusion. Conclusion A nomogram was established for the differential diagnosis between ICC and HCC preoperatively, and better therapeutic choice would be made if it was applied in clinical practice. Nomogram based on inflammatory indices was established for the differential diagnosis between ICC and HCC preoperatively, and better therapeutic choice would be made if it was applied in clinical practice.

Poor medical equipment may lead to misdiagnosis and missed diagnosis by doctors, leading to medical accidents. Given the differences in imaging methods, the performance determination method for conventional computed tomography (CT) does not apply to dental cone-beam computed tomography (CBCT). Therefore, a detection method that is more suitable for the characteristics of dental CBCT and more convenient for on-site operation in hospitals needs to be urgently developed. Hence, this study aimed to design a robust and convenient detection method to control the quality of dental CBCT, grasp the safety information of the equipment in a timely and effective manner, discover and evaluate equipment risks, and take reasonable and necessary countermeasures, thereby, reducing the risk of medical malpractice. This study adopted dose-area product to measure dose parameters and used objective quantitative evaluation methods instead of subjective evaluation methods for spatial resolution, contrast-to-noise ratio index, and uniformity. The dental CBCT of 10 dental hospitals and clinics were tested, and the findings revealed that the testing methods used had good accuracy and applicability.

Revealing the principle of distributed electric propulsion (DEP) plane aerodynamic design under the effect of strong aerodynamic coupling of distributed power-wing and the change law of total gain is the key to carrying out the design of distributed electric propulsion plane. For the distributed electric propulsion plane aerodynamic layout, the aerodynamic performance of the DEP configuration is studied based on the VLM-ADT aerodynamic characteristics fast solver method. This paper gives a set of fast parametric methods to research the aerodynamic performance of DEP layout, which mainly includes three comprehensive indexes such as lift-to-drag ratio L/D, power loading T/P, and lift loading L/P—focused on; the factor of propeller number, which affects the aerodynamic performance of DEP aerodynamic layout. As the propeller number increases, the lift-to-drag ratio decreases, but the wing’s lift efficiency increases.

Machine learning (ML) algorithms provide effective ways to build prediction models using longitudinal information given their capacity to incorporate numerous predictor variables without compromising the accuracy of the risk prediction. Clinical risk prediction models in chronic hepatitis C virus (CHC) can be challenging due to non-linear nature of disease progression. We developed and compared two ML algorithms to predict cirrhosis development in a large CHC-infected cohort using longitudinal data. We used national Veterans Health Administration (VHA) data to identify CHC patients in care between 2000-2016. The primary outcome was cirrhosis development ascertained by two consecutive aspartate aminotransferase (AST)-to-platelet ratio indexes (APRIs) > 2 after time zero given the infrequency of liver biopsy in clinical practice and that APRI is a validated non-invasive biomarker of fibrosis in CHC. We excluded those with initial APRI > 2 or pre-existing diagnosis of cirrhosis, hepatocellular carcinoma or hepatic decompensation. Enrollment was defined as the date of the first APRI. Time zero was defined as 2 years after enrollment. Cross-sectional (CS) models used predictors at or closest before time zero as a comparison. Longitudinal models used CS predictors plus longitudinal summary variables (maximum, minimum, maximum of slope, minimum of slope and total variation) between enrollment and time zero. Covariates included demographics, labs, and body mass index. Model performance was evaluated using concordance and area under the receiver operating curve (AuROC). A total of 72,683 individuals with CHC were analyzed with the cohort having a mean age of 52.8, 96.8% male and 53% white. There are 11,616 individuals (16%) who met the primary outcome over a mean follow-up of 7 years. We found superior predictive performance for the longitudinal Cox model compared to the CS Cox model (concordance 0.764 vs 0.746), and for the longitudinal boosted-survival-tree model compared to the linear Cox model (concordance 0.774 vs 0.764). The accuracy of the longitudinal models at 1,3,5 years after time zero also showed superior performance compared to the CS model, based on AuROC. Boosted-survival-tree based models using longitudinal information are statistically superior to cross-sectional or linear models for predicting development of cirrhosis in CHC, though all four models were highly accurate. Similar statistical methods could be applied to predict outcomes in other non-linear chronic disease states.

Management of drained peatlands may pose a risk or a solution on the way towards climate change mitigation, which creates a need to evaluate the current state of forestry-drained peatlands, the magnitude of degradation processes and indicators for carbon (C) loss. Using a large dataset (778 profiles, 891 peat samples, collected between 1977 and 2017) from peatlands having different fertility classes across Finland, we investigate whether the surface peat profiles of undrained and forestry-drained peatlands differ in C:N, von Post and dry bulk density. The utility of element ratios (C:N:H stoichiometry) as site indicators for degradation were further analyzed from a subsample of 16 undrained and 30 drained peat profiles. This subsample of drained sites had carbon dioxide (CO2) and methane (CH4) fluxes measured allowing us to link peat element ratios to annual C gas effluxes. Element ratios H:C, O:C and C:N and degree of unsaturation (combining C, N, H changes) were found widely valid: they captured both differences in the botanical origin of peat as well as its potential decomposition pathway (C lost via a combination of dissolved organic C and C gas loss and/or the gaseous loss predominantly as CO2). Of the stoichiometric indexes, peat H:C ratio seemed to be the best proxy for degradation following drainage, it indicated not only long-term degradation but also explained 48% of the variation in annual CO2 emission. The O:C ratio positively correlated with annual CH4 flux, presumably because high O:C in peat reflected the availability of easily degradable substrate for methanogenesis. The differences in C:N ratio indicated notable decomposition processes for Sphagnum-dominated peatlands but not in Carex-dominated peatlands. Degree of unsaturation showed potential for an integrative proxy for drainage-induced lowering water table and post-drainage changes in peat substrate quality.

Mountain areas in the southern western corner of the Kingdom of Saudi Arabia frequently suffer from various types of landslides due to rain storms and anthropogenic activities. To resolve the problem related to landslides, landslide susceptibility map is important as a quick and safe mitigation measure and to help making strategic planning by identifying the most vulnerable areas. This paper summarizes findings of landslide susceptibility analysis at Al-Hasher area, Jizan, KSA, using two statistical models: frequency ratio and index of entropy models with the aid of GIS tools and remote sensing data. The landslide locations (inventory map) were identified in the study area using historical records, interpretation of high-resolution satellite images that include Geo-Eye in 2.5 m and Quickbird in 0.6m resolution, topographic maps of 1:10,000 scale, and multiple field investigations. A total of 207 landslides (80% out of 257 detected landslides) were randomly selected for model training, and the remaining 50 landslides (19%) were used for the model validation. Ten landslide conditioning factors including slope angle, slope-aspect, altitude, curvature, lithology, distance to lineaments, normalized difference vegetation index (NDVI), distance to roads, precipitation, and distance to streams, were extracted from spatial database. Using these conditioning factors and landslide locations, landslide susceptibility and weights of each factor were analyzed by using frequency ratio and index of entropy models. Our findings showed that the existing landslides of high and very high susceptibility classes cover nearly 80.4% and 79.1% of the susceptibility maps produced by frequency ratio and index of entropy models respectively. For verification, receiver operating characteristic (ROC) curves were drawn and the areas under the curve (AUC) were calculated for success and prediction rates. For success rate the results revealed that for the index of entropy model (AUC = 77.9%) is slightly lower than frequency ratio model (AUC = 78.8%). For the prediction rate, it was found that the index of entropy model (AUC = 74.9%) is slightly lower than the frequency ratio model (AUC = 76.7%). The landslide susceptibility maps produced from this study could help decision makers, planners, engineers, and urban areas developers to make suitable decisions.

Rice blast is a highly damaging disease that greatly impacts both the quality and yield of rice. Timely identification and monitoring of this disease are essential for effective agricultural management and for ensuring optimal crop performance. The spectral vegetation index has been widely used in the identification of crop diseases. However, a limitation of these indices is that they cannot identify diseases at different scales. This study aimed to address these issues by developing the rice blast-specific hyperspectral Geometry Ratio Vegetation Index (GRVIRB) for monitoring rice blast disease at the leaf and canopy scales. The sensitive bands for identifying rice blast disease were 688 nm, 756 nm, and 1466 nm using the successive projection algorithm. Based on these three sensitive bands and the spectral response mechanism of rice blast, the GRVIRB was designed. GRVIRB demonstrated high classification accuracy using SVM (support vector machine) and LDA (Linear Discriminant Analysis) models in leaf-scale and canopy-scale datasets from 2020 and 2021, surpassing the current vegetation indices of rice blast detection. It is demonstrated that the GRVIRB has excellent robustness and universality for rice blast detection from leaf to canopy scales in different years. Additionally, the research suggests that the new hyperspectral vegetation index can serve as a valuable reference for studies conducted at both unmanned aerial vehicle and satellite scales.

The aim of this study was to investigate risk factors, develop, and assess the predictive nomogram for low appendicular skeletal muscle mass index (ASMI) in middle-aged and elderly populations. A total of 3,209 inpatients were divided into a Training Set ( n  = 2,407) and a Validation Set ( n  = 802). A nomogram was developed using R software for internal validation, and external validation was performed using the Validation Set. Gender (male), age, height, weight, triglyceride levels, alanine aminotransferase levels, alcohol consumption, and the triglyceride-glucose index to body-mass index ratio (TyG/BMI) were identified as predictors for the nomogram of low ASMI. In the Training Set, Q1-Q4 subgroups were performed for TyG/BMI, and logistic regression analysis showed that a TyG/BMI ratio greater than 0.37 was significantly associated with an increased risk of developing low ASMI ( P  < 0.001), with an area under the receiver operating characteristic curve (AUC) of 0.879 for the nomogram. In the Validation Set, the nomogram also demonstrated excellent calibration and discrimination, with an AUC of 0.881. Decision curve analysis (DCA) indicated excellent clinical utility of the nomogram. The study innovatively used TyG/BMI to predict low ASMI, which can reduce the impact of obesity on the diagnosis of sarcopenia. The nomogram can be effectively used to screen for possible sarcopenia in community settings. Due to the cross-sectional study design and unable to obtain complete data on the assessment of muscle strength, the predictive efficacy of our nomogram model requires further confirmation through external validation by large, multicenter prospective studies on sarcopenia population.