Minimum Akaike Information Criterion Research Articles

Assessing of programmed cell death gene signature for predicting ovarian cancer prognosis and treatment response.

Programmed cell death (PCD) is an overwhelming factor affecting tumor cell metastasis, but the mechanism of PCD in ovarian cancer (OV) is still uncertain. To define the molecular subtypes of OV, we performed unsupervised clustering based on the expression level of prognosis related PCD genes in the Cancer Genome Atlas (TCGA)-OV. COX and least absolute shrinkage and selection operator (LASSO) COX analysis were used to identify the OV prognostic related PCD genes, and the genes identified according to the minimum Akaike information criterion (AIC) were the OV prognostic characteristic genes. According to the regression coefficient in the multivariate COX analysis and gene expression data, the Risk Score of OV prognosis was constructed. Kaplan-Meier analysis was conducted to assess the prognostic status of OV patients, and receiver operating characteristic (ROC) curves were conducted to assess the clinical value of Risk Score. Moreover, RNA-Seq date of OV patient derived from Gene Expression Omnibus (GEO, GSE32062) and the International Cancer Genome Consortium (ICGC) database (ICGC-AU), verifying the robustness of the Risk Score via Kaplan-Meier and ROC analysis.Pathway features were performed by gene set enrichment analysis and single sample gene set enrichment analysis. Finally, Risk Score in terms of chemotherapy drug sensitivity and immunotherapy suitability was also evaluated in different groups. 9-gene composition Risk Score system was finally determined by COX and LASSO COX analysis. Patients in the low Risk Score group possessed improved prognostic status, immune activity. PI3K pathway activity was increased in the high Risk Score group. In the chemotherapy drug sensitivity analysis, we found that the high Risk Score group might be more suitable for treatment with PI3K inhibitors Taselisib and Pictilisib. In addition, we found that patients in the low-risk group responded better to immunotherapy. Risk Score of 9-gene composition of PCD signature possesses promising clinical potential in OV prognosis, immunotherapy, immune microenvironment activity, and chemotherapeutic drug selection, and our study provides the basis for an in-depth investigation of the PCD mechanism in OV.

Prognostic nutritional index combined with NLR to construct a survival prediction model and decision analysis of patients with muscle-invasive bladder cancer after surgery.

To build a nomogram prediction model, assess its predictive ability, and perform a survival decision analysis on patients with muscle-invasive bladder cancer (MIBC) to study risk factors affecting overall survival (OS). A retrospective analysis was performed on the clinical information of 262 patients with MIBC who underwent radical cystectomy (RC) at the Urology Department of the Second Affiliated Hospital of Kunming Medical University between July 2015 and August 2021. The final model variables that were included were chosen using single-factor stepwise Cox regression, optimal subset regression, and LASSO regression + cross-validation with the minimum AIC value. The next step was to do a multivariate Cox regression analysis. The establishment of a nomogram model by fitting and the screening out of independent risk factors impacting the survival of patients with MIBC having radical resection. Receiver Activity Characteristic curves, C-index, and a calibration plot evaluated the prediction accuracy, validity, and clinical benefit of the model. The 1-, 3-, and 5-year survival rates were then computed for each risk factor using a Kaplan-Meier survival analysis. 262 eligible patients in total were enrolled. With a median follow-up of 32 months, the follow-up period ranged from 2 to 83 months. 171 cases (65.27%) survived while 91 cases (34.73%) perished. Age (HR = 1.06 [1.04; 1.08], p = 0.001), preoperative hydronephrosis (HR = 0.69 [0.46, 1.05], p = 0.087), T stage (HR = 2.06 [1.09, 3.93], p = 0.027), lymphovascular invasion (LVI, HR = 1.73 [1.12, 2.67], p = 0.013), prognostic nutritional index (PNI, HR = 1.70 [1.09, 2.63], p = 0.018), and neutrophil-to-lymphocyte ratio (NLR, HR = 0.52 [0.29, 0.93)], p = 0.026) were independent risk factor for the survival of bladder cancer patients. Create a nomogram based on the aforementioned findings, and then draw the 1-year, 3-year, and 5-year OS receiver operating characteristic curves by the nomogram. The AUC values were 0.811 (95% CI [0.752, 0.869]), 0.814 (95% CI [0.755, 0.873]), and 0.787 (95% CI [0.708, 0.865]), respectively, and the calibration plot matched the predicted value well. The 1-year, 3-year, and 5-year decision curve analyses were higher than the ALL line and None line at threshold values of >5%, 5%-70%, and 20%-70% indicating that the model has good clinical applicability. The calibration plot for the Bootstrap 1000-time resampled validation model was similar to the actual value. Patients with preoperative combination hydronephrosis, higher T-stage, combined LVI, low PNI, and high NLR had worse survival, according to Kaplan-Meier survival analysis for each variable. This study might conclude that PNI and NLR were separate risk factors that affect a patient's OS after RC for MIBC. The prognosis of bladder cancer may be predicted by PNI and NLR, but additional confirmation in randomized controlled trials is required.

Association of climate factors with dengue incidence in Bangladesh, Dhaka City: A count regression approach

BackgroundIn Bangladesh, particularly in Dhaka city, dengue fever is a major factor in serious sickness and hospitalization. The weather influences the temporal and geographical spread of the vector-borne disease dengue in Dhaka. As a result, rainfall and ambient temperature are considered macro factors influencing dengue since they have a direct impact on Aedes aegypti population density, which changes seasonally dependent on these critical variables. This study aimed to clarify the relationship between climatic variables and the incidence of dengue disease. MethodsA total of 2253 dengue and climate data were used for this study. Maximum and minimum temperature (°C), humidity (grams of water vapor per kilogram of air g.kg−1), rainfall (mm), sunshine hour (in (average) hours per day), and wind speed (knots (kt)) in Dhaka were considered as the independent variables for this study which trigger the dengue incidence in Dhaka city, Bangladesh. Missing values were imputed using multiple imputation techniques. Descriptive and correlation analyses were performed for each variable and stationary tests were observed using Dicky Fuller test. However, initially, the Poisson model, zero-inflated regression model, and negative binomial model were fitted for this problem. Finally, the negative binomial model is considered the final model for this study based on minimum AIC values. ResultsThe mean of maximum and minimum temperature, wind speed, sunshine hour, and rainfall showed some fluctuations over the years. However, a mean number of dengue cases reported a higher incidence in recent years. Maximum and minimum temperature, humidity, and wind speed were positively correlated with dengue cases. However, rainfall and sunshine hours were negatively associated with dengue cases. The findings showed that factors such as maximum temperature, minimum temperature, humidity, and windspeed are crucial in the transmission cycles of dengue disease. On the other hand, dengue cases decreased with higher levels of rainfall. ConclusionThe findings of this study will be helpful for policymakers to develop a climate-based warning system in Bangladesh.

Development of a Multivariate Prognostic Model for Lenvatinib Treatment in Hepatocellular Carcinoma.

Lenvatinib is a first-line agent for advanced hepatocellular carcinoma (HCC), but individual responses to treatment are highly heterogeneous. The aim of this study was to investigate the clinical parameters that influence the efficacy of Lenvatinib and to develop a prognostic model. We retrospectively enrolled 333 Lenvatinib-treated patients with HCC with a median age of 57 years. Two hundred nd sixty-three of these patients had BCLC (2022) stage C. The median overall survival (mOS) time within the cohort was 12.1 months, and the median progression-free survival (mPFS) time was 4.7 months. Univariate Cox regression, best subset regression, and Lasso regression were used to screen primary variables for possible contribution to OS, multivariate Cox analysis was used to fit selected models, and the final model was selected using the maximum area under the curve (AUC) and minimum AIC. Receiver operating curves (ROC), calibration curves, and decision curve analysis were plotted to assess model performance, and 5-fold cross-validation was performed for internal validation. X-tile software was used to select the best cutoff points and to divide the study cohort into 3 different risk groups. Seven variables were included in the final model: BCLC stage, prior transarterial chemoembolization and immunotherapy history, tumor number, prognostic nutritional index, log (alpha-fetoprotein), and log (platelet-to-lymphocyte ratio). We named this final model the "multivariate prognostic model for Lenvatinib" (MPML), and a nomogram was constructed to predict the probability of survival at 6, 9, and 12 months. The MPML had good discrimination, calibration, and applicability. Cross-validation showed mean AUC values of 0.7779, 0.7738, and 0.7871 at 6, 9, and 12 months, respectively. According to nomogram points, mOS time was 21.57, 8.70, and 5.37 months in the low, medium, and high-risk groups, respectively (P < .001), and these differences were also observed in the PFS survival curve (P < .001). The MPML stratified patients according to baseline clinical characteristics had a strong performance in predicting Lenvatinib efficacy and has the potential for use as an auxiliary clinical tool for individualized decision-making.

A proposed new Japanese classification of synchronous peritoneal metastases from colorectal cancer: A multi-institutional, prospective, observational study conducted by the Japanese Society for Cancer of the Colon and Rectum.

To establish a new Japanese classification of synchronous peritoneal metastases from colorectal cancer. This multi-institutional, prospective, observational study enrolled patients who underwent surgery for colorectal cancer with synchronous peritoneal metastases. Overall survival rates were compared according to the various models using objective indicators. Each model was evaluated by Akaike's information criterion (AIC). The region of peritoneal metastases was evaluated by the peritoneal cancer index (PCI). Between October 2012 and December 2016, 150 patients were enrolled. The AIC of the present Japanese classification was 1020.7. P1 metastasis was defined as confined to two regions. The minimum AIC was obtained with the cutoff number of 10 or less for P2 metastasis and 11 or more for P3 metastasis. As for size, the best discrimination ability between P2 and P3 metastasis was obtained with a cutoff value of 3 cm. The AIC of the proposed classification was 1014.7. The classification was as follows: P0, no peritoneal metastases; P1, metastases localized to adjacent peritoneum (within two regions of PCI); P2, metastases to distant peritoneum, number ≤10 and size ≤3 cm; P3, metastases to distant peritoneum, number ≥11 or size >3 cm; P3a, metastases to distant peritoneum, number ≥11 and size ≤3 cm, or number ≤10 and size >3 cm; P3b, metastases to distant peritoneum, number ≥11 and size >3 cm. This objective classification could improve the ability to discriminate prognosis in patients with synchronous peritoneal metastases from colorectal cancer.

Application of machine learning models to predict cytotoxicity of ionic liquids using VolSurf principal properties

Ionic Liquids (ILs) are considered greener alternatives to traditional organic solvents due to their unique physical and chemical properties. Nevertheless, recent studies showed that ILs can induce toxic effects in ecosystem. Therefore, it is essential to determine the level of risk to the aquatic life to successfully use these ILs. Toxicity measurement of various ILs on a broad spectrum of conditions through experimental techniques is way demanding on time, resources, and is at times impractical. Various research works have been performed in Quantitative Property Relationship (QSAR/QSPR) for IL toxicity prediction expressed as EC50. In this study, five supervised machine learning models were trained and tested using nine Principal Properties (PPs) as descriptors to predict leukemia rat cell line (IPC-81) cytotoxicity. Then eight feature selection techniques were used to preprocess the data to improve the performance of the best machine learning model among the preliminary trained models. Analysis of the performance of the models on predicting the out-of-sample data set showed that the Extreme Gradient Boosting (XGBoost) supervised machine learning model is the best in predicting with the highest test score (R2 = 0.79). This model was the most parsimonious (minimum AIC of 46.50), consistent (minimum RMSE of 0.45), and precise (minimum MAE of 0.32) in predicting IPC-81 cytotoxicity. The feature importance attribute of XGBoost confirmed that the structural features of ILs’ cation like cationic hydrophilicity and the side chain length have significant impact on the toxicity. Nevertheless, the anionic part of IL is also important to their toxicity and needs to be considered in toxicity prediction. Among the tested feature selection techniques, the random forest technique was the best in improving model performance (i.e., the least error matrices: AIC = 41.22, MAE = 0.31 and RMSE = 0.4259 respectively) but at longer execution time. However, the wrapper methods were the most robust in improving computational efficiency (i.e, improved the model performance at the shortest execution time). Therefore, this study improves QSPR studies on toxicity prediction of new ILs with the application of machine learning and feature selection techniques.

Subsampling based variable selection for generalized linear models

A novel variable selection method for low-dimensional generalized linear models is introduced. The new approach called AIC OPTimization via STABility Selection (OPT-STABS) repeatedly subsamples the data, minimizes Akaike's Information Criterion (AIC) over a sequence of nested models for each subsample, and includes in the final model those predictors selected in the minimum AIC model in a large fraction of the subsamples. New methods are also introduced to establish an optimal variable selection cutoff over repeated subsamples. An extensive simulation study examining a variety of proposed variable selection methods shows that, although no single method uniformly outperforms the others in all the scenarios considered, OPT-STABS is consistently among the best-performing methods in most settings while it performs competitively for the rest. This is in contrast to other candidate methods which either have poor performance across the board or exhibit good performance in some settings, but very poor in others. In addition, the asymptotic properties of the OPT-STABS estimator are derived, and its root-n consistency and asymptotic normality are proved. The methods are applied to two datasets involving logistic and Poisson regressions.

The first aerial survey to estimate abundance of humpback whales (Megaptera novaeangliae) in the breeding ground off Brazil (Breeding Stock A)

In the Southern Hemisphere, humpback whales (Megaptera novaeangliae) were heavily exploited from both coastal stations and in pelagic waters in all major ocean basins. About 200,000 whales were taken after 1900, causing declines of populations to small percentages of their pre-exploitation levels. The study presented here aimed to investigate humpback whale abundance in the Brazilian coastal breeding ground, in order to provide information to support further analysis of the population recovery. Between 25 August and 2 September 2001, a fixed wing, flat window, aircraft was used to survey transect lines along the northern limit of Bahia State (12°10’S), to the southern limit of Espírito Santo State (20°42’S). All on-effort sightings were recorded and abundance was estimated according to standard distance sampling methodology (Burnham et al., 1980; Buckland et al., 1993). Group sizes of humpback whales ranged between 1-5 and the mean group size was 1.52 (±0.06). The model that best fitted the perpendicular distance data, based on the minimum Akaike Information Criterion, was the hazard rate model. The population size estimated using uncorrected data was 1,493 (CV=0.21) whales. Surface time was used to correct the estimates for gˆ(0), resulting in a correction factor of 0.67 (±0.15). The corrected analysis for each block and combined result, increased the population size estimate to 2,229 (CV=0.31) individuals. The data from this study could be used to identify new areas appropriate for whalewatching, to monitor the status and dynamics of the humpback whale population off the Brazilian coast and to provide information for the establishment of new protected areas.

Modelling and Forecasting the Trend in Cryptocurrency Prices

The prediction of cryptocurrency prices is a hot topic among academics. Nevertheless, predicting the cryptocurrency price accurately can be challenging in the real world. Numerous studies have been undertaken to determine the best model for successful prediction. However, they lacked correct results because they avoided identifying the critical features. It is important to remember that trends are critical features in time series to obtain data information. A dearth of research demonstrates that the cryptocurrency trend comprises linear and nonlinear patterns. Therefore, this study attempted to fill this gap and focused on modelling and forecasting trends in cryptocurrency. This study examined the linear and nonlinear dependency trend patterns of the top five cryptocurrency closing prices. The weekly historical data of each cryptocurrency were taken at different periods due to the availability of data on the system. In achieving its goal, this study examined the results by plotting based on residual trend and diagnostic statistic checking using three deterministic methods: linear trend regression, quadratic trend, and exponential trend. Based on the minimum Akaike Information Criterion (AIC), the result showed that the top five cryptocurrency closing price data series contained nonlinear and linear trend patterns. The information of this study will assist traders and investors in comprehending the trend of the top five cryptocurrencies and choosing the suitable model to predict cryptocurrency prices. Additionally, accurately measuring the forecast will protect investors from losing their investment.

Establishment and validation of a nomogram for estimating the overall survival of vulvar squamous cell carcinoma patients aged 50 years or older

This study aimed to develop a nomogram for estimating the overall survival (OS) of vulvar squamous cell carcinoma patients aged ≥50 years based on their clinicopathological characteristics. The Surveillance, Epidemiology and End Results (SEER) database was searched for cases diagnosed with vulvar squamous cell carcinoma from 2000 to 2019. They were randomly grouped into a training and a test cohort in a 7:3 ratio. Cox regression analyses were performed to identify risk factors associated with the overall survival rate of the patients. Then, the nomogram was built based on independent factors selected by the minimum Akaike Information Criterion (AIC) value in multivariate Cox regression analyses. The performance of the nomogram was assessed by its concordance index (C-index) and the area under curve (AUC). Its predictive power was determined by calibration plot and clinical applicability by decision curve analysis (DCA). A total of 3048 patients were identified and randomized into the training (n = 2146) and validation (n = 902) cohort. The validation indicated that the nomogram had good recognition ability for clinical trials, patient counseling, and rationalizing therapeutic modalities. The C-index for OS rates was 0.729 (95%Confidence Interval (CI): 0.715–0.743) in the training cohort and 0.717 (95% CI: 0.693–0.741) in the validation cohort. The AUCs of the 1-, 3- and 5-year OS were 0.789, 0.781 and 0.775 in the training cohort and 0.815, 0.772 and 0.748 in the validation cohort. Calibration plots showed that the nomogram had good predictive power, and DCA demonstrated that the proposed nomogram could provide a net clinical benefit. Our nomogram demonstrated promising accuracy in comprehensively predicting the OS of elderly vulvar squamous cell carcinoma patients. It could be used as a reference to guide individualized treatments and plan the follow-up of elderly patients with vulvar squamous cell carcinoma.

Survival analysis of women breast cancer patients in Northwest Amhara, Ethiopia.

Breast cancer, the most common cause of cancer death and the most frequently diagnosed cancer among women worldwide, ranks as the second cause of death next to lung cancer. Thus, the main objective was to assess the factors that affect the survival time of breast cancer patients using the shared frailty model. A retrospective study design was used to collect relevant data on the survival time of breast cancer patients from the medical charts of 322 breast cancer patients under follow-up at the Felege Hiwot Comprehensive Specialized Hospital (FHCSH). The data were explored using the Cox proportional hazard model, the accelerated failure time model, and shared frailty models. The model comparison was done using AIC and BIC. As a result, the Weibull gamma shared frailty model had a minimum AIC and BIC value. From a total of 322 patients, about 95 (29.5%) died and 227 (70.5%) were censored. The overall mean and median estimated survival times of breast cancer patients under study were 43.7 and 45 months, respectively. The unobserved heterogeneity in the population of clusters (residence) as estimated by the Weibull-gamma shared frailty model was 0.002 (p-value = 0.000), indicating the presence of residential variation in the survival time of breast cancer patients. The estimated hazard rate of patients who had not had recurrent breast cancer was 0.724 (95% CI: 0.571, 0.917) times the estimated hazard rate of patients who had had recurrent breast cancer. The prevalence of breast cancer was considerably high. Under this investigation, older patients, patients in stages III and IV, anemic and diabetes patients, patients who took only chemotherapy treatment, metastasized patients, patients with an AB blood type, patients with a positive breast cancer family history, and patients whose cancer was recurrent had high death rates. Patient characteristics such as age, stage, complications, treatment, metastasis, blood type, family history, and recurrence were significant factors associated with the survival time of women with breastcancer.

Trends in forced displacement due to conflict from 2009 to 2021: A decade forecast for effective humanitarian emergency response system in Nigeria

Internal displacement figures in conflicted affected regions of Nigeria is a significant problem that poses a risk of humanitarian crises accounted for by poor and a neglected emergency response system. The present study analyzed annual conflict/violence-induced internal displacement trends in Nigeria from 2009 to 2021 and predicted the annual figures for the next 10 years. Using the R software, the Autoregressive Integrated Moving Average (ARIMA) was adopted for analysis, while model (0, 0, 1) was chosen as the best fitting model with the minimum AIC coefficient. From the ARIMA forecast, 2022 showed a 4% decrease in displacement figures, further decreasing by 7% in 2023. The figures remained constant for the rest of the forecasted years, showing neither increases nor decreases from 2024 to 2031. Close observation revealed the overlapping trends between increased annual internal displacements and pre-election periods in Nigeria. This study will help inform humanitarian bodies on the significance of early detection and preparation for future displacement crises while considering the cycle of ethno-religious-related electoral violence. Furthermore, future studies should compare the accuracy of ARIMA with other models to ensure its validity using a similar sample.

Dissipative unified dark fluid: Observational constraints

We adopt a standard FRW cosmology with a unified scenario, where the usual dark matter and dark energy sectors are replaced by a single dissipative unified dark fluid (DUDF). The equation of state of such fluid can asymptote between two power laws. As a result, it enables fluid to have a smooth transition from dust at early times to dark energy at late times. The dissipation is represented by a bulk viscosity with a constant coefficient, whereas shear viscosity is excluded due to the isotropy of the universe. We performed a likelihood analysis using recent observational datasets from local H0 measurements, Type Ia supernovae, observational Hubble data, baryon acoustic oscillations, and cosmic microwave background to put cosmological constraints on the model. The special case of the non-dissipative unified dark fluid (UDF) is also studied, while a similar analysis is implemented to the ΛCDM model for comparison. Our analysis revealed that between the three analyzed models, the DUDF has the lower χmin2-value. Based on model selection statistics in the form of the Akaike Information Criterion (AIC) and the Bayesian Information Criterion (BIC), we compared different models to select the favored one due to the observational data used. Our results revealed that the UDF model has the minimum AIC, whence, due to the AIC, it is the most favorable model for the data. ΔAIC values of models are, then, measured to this model. This difference indicated that the DUDF model is substantial on the level of empirical support. On the other hand, the BIC results still prefer the ΛCDM model. Our dark fluid also alleviates the tension in the Hubble parameter. We got an H0 value of 70.02 kms−1Mpc−1 for the DUDF model and 70.25 kms−1Mpc−1 for the UDF model. These results alleviate the tension to ∼2.2σ for the DUDF model and ∼2.1σ for the UDF model. In addition, we studied the evolution of the deceleration parameter, the effective equation of state parameter, and the density parameter. We also estimated a value for the viscosity of the cosmic fluid. We found that our unified fluid doesn't deviate from the standard ΛCDM model at early times, with the ability to play the role of the cosmological constant by accelerating the universe at late times.

Causes of the recent decline of a Lesser Kestrel (Falco naumanni) population under an enhanced conservation scenario

Lesser Kestrel Falco naumanni (Fleischer, 1818) populations have been one of the best monitored bird populations in Spain over the last 70 years. These populations suffered a sharp decline between the 1950s and 1990s. Since then, periodic censuses showed a population increase that lasted until the 2010s. In those years, numerous projects for the recovery of the species were also initiated (some EU LIFE Projects, among others), which have continued to the present day. However, despite conservation efforts, the Spanish Lesser Kestrel population, which includes about 40% of the European breeding population, has declined at a rate of 6% per year since 2012. In this study, we analysed changes in habitat and population size in 12 colonies located in La Mancha between 2003 and 2021 in order to identify possible causes of the current decline. This colonial species breeds in old buildings, and roof area (a proxy for house size) was the predictor that best accounted for the number of pairs in a colony in a given year. In addition, the extent of herbaceous crops (related to prey availability) explained a similar amount of colony size variance in 2021, whereas in 2003, the availability of large Orthoptera itself had a significant effect but a much lower effect size. The number of nestboxes affected positively the number of breeding pairs in 2021 but explained only 1% of the variance. The decline of the Lesser Kestrel population between 2003 and 2021 was largely explained by the decrease of the density of large orthopterans, their main prey. The model with the minimum Akaike Information criterion adjusted for small sample size (AICc) value also included a positive association with changes in roof area and a negative association with rabbit density (possibly through a hyperpredation phenomenon). Other top models (i.e. ΔAICc ≤ 2 units) also showed negative effects of the loss of favourable land uses (pasture lands and herbaceous crops) on colony size, probably because this caused a reduction in the availability of large Orthoptera. These changes in land use occurred less often in areas protected by the Natura 2000 network, which may thus have contributed to the conservation of the Lesser Kestrel population. However, other conservation measures such as the installation of nestboxes were not as efficient as expected.

Parametric Models for Survival Analysis of Childhood Cancer Patients' Data

Background: The rate of childhood cancer death has dropped steadily over the past 50 years. The pediatric cancer risk has remained under investigation. Objectives: This study aims at investigating the associated factors with the survival of pediatric patients with retinoblastoma, sarcoma, brain tumor, and leukemia cancer. Methods: The cohort study of 1879 children with retinoblastoma, sarcoma, brain tumor, and leukemia aged &lt; 1, 1 - 5, 6 - 10, 11 - 15, and &gt; 15 years in Mahak Hospital and Rehabilitation Complex from 2007 to 2016 were enrolled in the study. Median survival time was reported for each cancer. Parametric survival models including Gompertz, Weibull, lognormal, and log‑logistic models were fitted. Then, the model with almost minimum Akaike information criterion (AIC) was chosen. The hazard ratio (HR) and the analysis were performed by R3.5.1. Results: Totally, 270 (14.37%) patients with Retinoblastoma, 667 (35.5%) with leukemia, 625 (33.26%) with a Brain tumor, and 317 (16.87%) with Sarcoma were included in this study; 815 (43.37%) patients were female. Gompertz's model was chosen to fit the data due to the minimum AIC. The associated factors with the survival of childhood cancers were as follows: age &lt; 1 year, parental relation, loco-regional relapse and chemotherapy alone (HR: 7.63, 1.56, 4.61, 1.12) in leukemia, other nationalities, metastasis or metastasis and loco-regional relapse and chemotherapy alone (HR = 3.74, 5.75, 2.12) in retinoblastoma, loco-regional relapse and metastasis (HR = 2.40, 3.71) in brain tumor, other ages except for 5 - 10 years, parental relation, chemotherapy alone, and metastasis (HR = 33.3, 1.80, 3.57, 3.8) in sarcoma. Conclusions: Age, parental familial relationships, combination therapy, and metastasis of primary cancer were the risk factors for survival of children with 4 common cancers of leukemia, retinoblastoma, brain tumors, and sarcoma, using the Gompertz model.

Development of a nomogram for predicting grade 2 or higher acute hematologic toxicity of cervical cancer after the pelvic bone marrow sparing radiotherapy.

BackgroundAcute hematologic toxicity (HT) is a common complication during radiotherapy of cervical cancer which may lead to treatment delay or interruption. Despite the use of intensity-modulated radiation therapy (IMRT) with the pelvic bone marrow (PBM) sparing, some patients still suffer from acute HT. We aimed to identify predictors associated with HT and develop a nomogram for predicting grade 2 or higher (G2+) acute HT in cervical cancer following the PBM sparing strategy.MethodsThis study retrospectively analyzed 125 patients with cervical cancer who underwent IMRT with the PBM sparing strategy at our institution. Univariate and multivariate logistic regression, best subset regression, and least absolute shrinkage and selection operator (LASSO) regression, respectively, were used for predictor screening, and Akaike information criterion (AIC) was used to determine the best model for developing the nomogram. Finally, we quantified the risk of G2+ acute HT based on this model to establish a risk stratification.ResultsThe independent predictors used to develop the nomogram were histological grade, pre-radiotherapy chemotherapy, pre-radiotherapy HT, and radiotherapy [IMRT alone vs. concurrent chemoradiotherapy (CCRT)] which were determined by the univariate and multivariate logistic regression with the minimum AIC of 125.49. Meanwhile, the heat map showed that there is no multicollinearity among the predictors. The nomogram was well-calibrated to reality, with a Brier score of 0.15. The AUC value was 0.82, and the median Brier score and AUC in 1000 five-fold cross-validation were 0.16 and 0.80, respectively. The web version developed together was very easy to use. The risk stratification indicated that high-risk patients (risk point > 195.67) were more likely to develop G2+ acute HT [odds ratio (OR) = 2.17, 95% confidence interval (CI): 1.30–3.05].ConclusionThis nomogram well-predicted the risk of G2+ acute HT during IMRT in cervical cancer after the PBM sparing strategy, and the constructed risk stratification could assist physicians in screening high-risk patients and provide a useful reference for future prevention and treatment strategies for acute HT.

A national surveillance of eosinophilic meningitis in Thailand

IntroductionEosinophilic meningitis (EOM) is an emerging infectious disease worldwide. The most common cause of EOM is infection with Angiostrongylus cantonensis One possible method of monitoring and control of this infection is surveillance and prediction. There are limited data on national surveillance and predictive models on EOM. This study aimed to develop an online surveillance with a predictive model for EOM by using the national database. MethodsWe retrospectively retrieved reported cases of EOM from all provinces in Thailand and quantified them by month and year. Data were retrieved from Ministry of Public Health database. We developed a website application to explore the EOM cases in Thailand including regions and provinces using box plots. The website also provided the Autoregressive Integrated Moving Average (ARIMA) models and Seasonal ARIMA (SARIMA) models for predicting the disease cases from nation, region, and province levels. The suitable models were considered by minimum Akaike Information Criterion (AIC). The appropriate SARIMA model was used to predict the number of EOM cases. ResultsFrom 2003 to 2021, 3330 EOM cases were diagnosed and registered in the national database, with a peak in 2003 (median of 22 cases). We determined SARIMA(1,1,2)(2,0,0)[12] to be the most appropriate model, as it yielded the fitted values that were closest to the actual data. A predictive surveillance website was published on http://202.28.75.8/sample-apps/NationalEOM/. ConclusionsWe determined that web application can be used for monitoring and exploring the trend of EOM patients in Thailand. The predictive values matched the actual monthly numbers of EOM cases indicating a good fit of the predictive model.

A predictive model for first-trimester pregnancy inception after IVF-ET based on multimodal ultrasound evaluation of endometrial receptivity

BackgroundIn-vitro fertilization-embryo transfer (IVF-ET) is a commonly used assisted reproductive technology. Its success depends on many factors, including endometrial receptivity. Endometrial receptivity can be evaluated by ultrasound, endometrial biopsy, and magnetic resonance imaging. Compared with the latter two methods, ultrasound has the advantages of wide availability, non-invasiveness, and low cost. Three-dimensional (3D) ultrasound imaging examines endometrial thickness, morphology, and blood vessels, which are associated with the success of embryo implantation. However, there are no reports of endometrial receptivity assessment by 3D ultrasound. Therefore, we aimed to evaluate endometrial receptivity using 3D ultrasound and construct a predictive model for first-trimester pregnancy inception following IVF-ET.MethodsWe performed a prospective observational study on infertile women who underwent IVF-ET between December 2019 and February 2021. These women had 3D ultrasound evaluations, measuring endometrial thickness, volume, pattern, morphology, peristalsis, uterine artery blood flow index, sub-endometrial blood flow index, and distribution pattern. We recorded the occurrence of first-trimester pregnancies in these women. Using Akaike information criterion (AIC) and backward stepwise regression, a first-trimester pregnancy prediction model was constructed based on the minimum AIC value and validated internally and externally.Results111 women were enrolled, with 103 included in the analysis. Univariate and multiple logistic regression analyses showed that endometrial thickness and vascularization flow index (VFI) were independent factors associated with the occurrence of a pregnancy. The final prediction model corresponding to the minimum AIC value (65.166) was Y = − 6.131–0.182endometrial thickness + 0.542endometrial volume + 4.374VFI + 0.132age. In the test set, modeling cohort, and external validation cohort, the model showed satisfactory differentiation, with C index of 0.841 (95%CI 0.699–0.817), 0.727 (95%CI 0.619–0.815), and 0.745 (95%CI 0.671–0.840), respectively. The Hosmer–Lemeshow goodness of fit tests reported P = 0.865, 0.139, and 0.070, respectively, indicating a high agreement with the actual IVF-ET outcome. This model reached the highest diagnostic efficiency (sensitivity 88.9%, specificity 75%, Youden index 0.639) at a diagnostic cut-off value of ≥ 0.360.ConclusionsThe predictive model based on endometrial receptivity evaluations by 3D ultrasound had high diagnostic efficiency and could be a simple and effective tool to predict first-trimester pregnancy inception after IVF-ET.

Interpretable clinical visualization model for prediction of prognosis in osteosarcoma: a large cohort data study.

BackgroundCurrently, the clinical prediction model for patients with osteosarcoma was almost developed from single-center data, lacking external validation. Due to their low reliability and low predictive power, there were few clinical applications. Our study aimed to set up a clinical prediction model with stronger predictive ability, credibility, and clinical application value for osteosarcoma.MethodsClinical information related to osteosarcoma patients from 2010 to 2016 was collected in the SEER database and four different Chinese medical centers. Factors were screened using three models (full subset regression, univariate Cox, and LASSO) via minimum AIC and maximum AUC values in the SEER database. The model was selected by the strongest predictive power and visualized by three statistical methods: nomogram, web calculator, and decision tree. The model was further externally validated and evaluated for its clinical utility in data from four medical centers.ResultsEight predicting factors, namely, age, grade, laterality, stage M, surgery, bone metastases, lung metastases, and tumor size, were selected from the model based on the minimum AIC and maximum AUC value. The internal and external validation results showed that the model possessed good consistency. ROC curves revealed good predictive ability (AUC > 0.8 in both internal and external validation). The DCA results demonstrated that the model had an excellent clinical predicted utility in 3 years and 5 years for North American and Chinese patients.ConclusionsThe clinical prediction model was built and visualized in this study, including a nomogram and a web calculator (https://dr-lee.shinyapps.io/osteosarcoma/), which indicated very good consistency, predictive power, and clinical application value.

The Efficacy of the Systemic Immune-Inflammation Index and Prognosis Nutritional Index for the Diagnosis of Venous Thromboembolism in Gastrointestinal Cancers.

PurposeThis study aimed to analyze the association between venous thromboembolism (VTE) and inflammatory markers like systemic immune-inflammation index (SII) and prognosis nutritional index (PNI), and to evaluate their efficacy for the diagnosis of VTE in patients with gastrointestinal malignancies.Patients and MethodsA total of 1326 patients with the initial diagnosis of gastrointestinal cancer in the First Affiliated Hospital of Anhui Medical University (AHMU) were enrolled in the training cohort. Univariate and multivariate analysis was used to pinpoint independent predictors of VTE, which were eventually visualized as the nomogram models. The Akaike Information Criterion (AIC) was used to screen the best model. The receiver operating characteristic curve (ROC) and the clinical decision curve analysis (DCA) were utilized to evaluate the models’ predictive performance in the training queue and another external sample of 250 patients at the Second Affiliated Hospital of AHMU.ResultsA total of 476 patients were complicated with VTE in the training cohort. Multifactorial analysis of clinical characteristics and inflammatory markers showed that PNI, SII, age, tumor location, and therapy were independent risk factors of VTE, visualized as model A. Another model B was constructed by adding coagulation markers to the previous analysis. Model B was the best prediction model with the minimum AIC value, followed by model A with an AUC of 0.806 (95% CI 0.782~0.830) which was similar to model B’s 0.832 (95% CI 0.810~0.855) but significantly higher than the currently widely used Khorana score’s 0.592 (95% CI 0.562~0.621) and the CATS score’s 0.682 (95% CI 0.653~0.712). The external verification yielded similar findings, with the AUC being 0.792 (95% CI 0.734~0.851), 0.834 (95% CI 0.778~0.890), 0.655 (95% CI 0.582~0.729), and 0.774 (95% CI 0.699~0.849) respectively. The DCA curves demonstrated that new models had excellent usefulness in screening patients with a high VTE risk.ConclusionThe SII and PNI were simple and viable inflammatory markers associated with VTE, and the nomogram based on them and clinical features had a meaningful clinical utility for VTE in patients with gastrointestinal malignancies.