Clear cell Renal Cell Carcinoma (ccRCC) is the most common malignant tumor in the urinary system and the predominant subtype of malignant renal tumors with high mortality. Biopsy is the main examination to determine ccRCC grade, but it can lead to unavoidable complications and sampling bias. Therefore, non-invasive technology  for ccRCC grading is attracting more and more attention. However, noise labels on CT images containing multiple grades but only one label make prediction difficult. However, noise labels exist in CT images, which contain multiple grades but only one label, making prediction difficult.We proposed a Transformer-based deep learning algorithm with CT images to improve the diagnostic accuracy of grading prediction and to improve the diagnostic accuracy of ccRCC grading.We integrate different training models to improve robustness and predict Fuhrman nuclear grade. Then, we conducted experiments on a collected ccRCC dataset containing 759 patients and used average classification accuracy, sensitivity, specificity, and AreaUnderCurve as indicators to evaluate the quality of research. In the comparative experiments, we further performed various current deep learning algorithms to show the advantages of the proposed method. We collected patients with pathologically proven ccRCC diagnosed from April 2010 to December 2018 as the training and internal test dataset, containing 759 patients. We propose a transformer-based network architecture that efficiently employs convolutional neural networks (CNNs) and self-attention mechanisms to extract a persuasive feature automatically. And then, a nonlinear classifier is applied to classify. We integrate different training models to improve the accuracy and robustness of the model. The average classification accuracy, sensitivity, specificity, and area under curve are used as indicators to evaluate the quality of a model.The mean accuracy, sensitivity, specificity, and Area Under Curve achieved by CNN were 82.3%, 89.4%, 83.2%, and 85.7%, respectively. In contrast, the proposed Transformer-based model obtains a mean accuracy of 87.1% with a sensitivity of 91.3%, a specificity of 85.3%, and an Area Under Curve (AUC) of 90.3%. The integrated model acquires a better performance (86.5% ACC and an AUC of 91.2%).A transformer-based network performs better than traditional deep learning algorithms in terms of the accuracy of ccRCC prediction. Meanwhile, the transformer has a certain advantage in dealing with noise labels existing in CT images of ccRCC. This method is promising to be applied to other medical tasks . Renal cell carcinoma (RCC) is the most common kidney tumor , 2. CleaThe preoperative biopsy is the gold standard for evaluating the grade of ccRCC. However, patients undergoing biopsy are at risk of complications, e.g., hematuria (with more than 80% incidence), perirenal hematoma (with about 60%–90% incidence), and infection . The proPreoperative noninvasive prediction of ccRCC is conducive to delivering an individualized treatment. Previous studies , 13, usiThe transformer network, a branch of deep learning, is considered a promising technology for analyzing medical images because it can capture global representations and establish long-distance relationships within the image , 19. TheAs a result, this research aims to investigate an efficient transformer classifier for predicting the Fuhrman grading of ccRCC based on three-phased CT images.Patient cohort: This diagnostic and observational study was approved by the institutional review board , and written informed consent was obtained from all patients. Consecutive patients were collected from April 2010 to December 2018 in one hospital. We cleaned the original data according to the following rules: (1) there is no obvious noise in the image of the patient; (2) the patient image has no apparent artifacts. Specifically, all of the pathologically proven ccRCC grades are reconfirmed by experienced radiologists. This work uses the Fuhrman nuclear grading system as the standard grading system. Finally, 759 patients were included in this work.CT images: All patients underwent a multi-slice CT scan with three phases, including unenhanced, arterial, and portal venous phases, using the following systems: LightSpeed VCT , Sensation 64 CT (Siemens), or Sensation 16 CT (Siemens). The PCP, CMP, and NP of the MDCT (multidetector CT) examination were acquired for each patient with the following protocol. By using a high-pressure injector at a rate of 3.5 ml/s, 70–100 ml of contrast agents were injected into the antecubital vein. The CMP is the corticomedullary phase contrast-enhanced scan starting 30 s after the contrast agent injection. The NP is the nephrographic phase contrast-enhanced scan starting 90 s after the contrast materials injection. Spiral scanning and thin-slice reconstruction were used for all three phases. The CT scanning parameters of the phases were as follows: the tube voltage was 120 KV; the reconstruction thickness was 1 mm to 5 mm, and the matrix was 512 × 512. All CT scans of patients are converted to color-scale images and reviewed by experienced radiologists in abdominal imaging. The ccRCC images collected from the CMP phase are used in experiments.CT Processing: Medical CT slices are diverse and complex. If CT slices are selected to be the input of the classification model, the results are imperfect and require further optimization. To make a more accurate diagnosis, we preprocess the original CT image by detecting the organ or lesion area from medical scanning. We utilize a tumor detection network to quickly and efficiently obtain the rectangular region of interest of the tumor in each phase image. As shown in Because medical data are scarce and difficult to label, large deep learning models rely on data augmentation to improve performance. To study the impact of data enhancement, we will consider several common enhancements here. There are two types of data augmentation in the vision computer domain. One is appearance transformation, such as sharpness, brightness, contrast, saturation, gray processing, Gaussian blur, and elasticity , anotherTo make the features extracted from the ccRCC dataset correspond to the label as much as possible, we introduced a transformer module into convolutional networks to improve performance. We illustrate the overall diagram in The network framework, termed TransResNet, is composed of convolution blocks, transformer blocks, and classifier (one FC layer). The convolution block adopts a pyramid structure, in which the resolution of feature mapping decreases with the increase in network depth while the number of channels increases. It consists of four phases: the first phase is a 3 × 3 convolution with stride 1 and padding 1, which is used to extract initial local features. In the last three stages, we refer to the first three layers of ResNet, in which the output channels of each layer are 16, 32, and 64, respectively. The Transformer block contains 12 repeated transformers. As shown in X∈ℛc×h×w with c channels and the feature shape of h × w. Self-attention estimates the relationship between one part of a feature map and other parts . Therefore, the feature map is divided into a sequence X={x1,x2,…,xn}  loss between prediction and ground truth, which can be written as:Referring to ViT , we takey is the real label and where the https://github.com/yangmeiyi/ccRCC_project.git.We constructed TransResNet with three residual blocks and 12 transformer blocks. The major hyper-parameters are as follows: the optimizer is Stochastic Gradient Descent (SGD) with an initial learning rate of 0.01, momentum of 0.9, and weight decay of 5e−4. The batch size is 100 per worker. For the epochs, the learning rate is scaled linearly from 0.01 to 0.00001, and then it is divided by 10 at epochs 50, 100, and 150. The proposed model is implemented using Pytorch 1.0.1. We ran the experiments on an Ubuntu 16.04.3 server with four NVIDIA GeForce GTX 1080 cards. Specific details of the code can be seen at: To a certain degree, ensemble learning can improve prediction accuracy. To achieve a strong classifier, we train multiple different classifiers. We selected the following five networks as sub-networks of the integration model:TransResNet: In medical, ResNet  is the pTransDenseNet: DenseNet  found thTransInception: Compared with the structure of ResNet, Inception  not onlyTransSENet: Different from the previously mentioned networks, improving the performance through spatial latitude, SENet  establisTransRegNet: RegNet  proposesWe combine them through the average method. These classifiers and TransResNet are constructed in the same way. The difference lies in the construction of convolution blocks, such as TransDenseNet uses a dense convolutional network and TransInception adopts an inception network.N trained different classifiers. Facing a test sample x (x ∈ test dataset), the prediction results of the integrated model are a N-dimensional vector {S1,S2,…,Sn} . The final score using averaging is formulated as follows:Suppose we have got This study included 759 patients, which comprised 477 low-grade [grade I  and II ] ccRCC patients and 282 high-grade [grade III  and IV ] ccRCC patients. Male and female patients are equally represented, accounting for 64% and 46%, respectively. The patient characteristics of the training and testing cohorts are shown in To validate this model, we choose Accuracy (ACC), Area Under Curve (AUC), Sensitivity (SE), and Specificity (SP) as evaluation criteria.Performance with data enhancement: To comprehend the role of enhancement strategy in detail, the individual and different combinations of them are discussed. With inconsistent images of the ccRCC, cropping as the basic data processing is applied. Performance on different architectures. This section shows the effect of the transformer on ResNet  , 37. TheThis retrospective study comprehensively investigates the pros and cons of different deep learning models based on two-phase CT images for discriminating low- and high-grade ccRCC. According to data scarcity and noise labels, a transformer-based deep learning model, TransResNet, is proposed. After a thorough comparison, the proposed discriminative model, TransResNet, achieved satisfactory performance. In addition, we find that this model can effectively alleviate the impact of noise labels.At present, the analysis of radiomics is a common paradigm to predict the ccRCC. Previous studies analyzed the texture of non-enhanced CT through machine learning to obtain universal features for higher accuracy. For example, Coy et al. have shown that high-grade ccRCC lesions are significantly larger and have more calcifications, necrosis, aggregation system infiltration, and unclear tumor margins than low-grade ccRCC lesions . In addiIn ccRCC, some high-grade lesions have low-grade features due to unavoidable sampling bias. It was also interesting to find that convolution-based networks focus on local pixel subsets, which results in a tendency to learn local patterns so that the relationships within images are ignored. As a result, in the complex samples, the convolution kernel cannot effectively make decisions, which leads to unsatisfactory grading accuracy. CNNs uniformly process CT images regardless of their importance. In contrast, the transformer uses a self-attention mechanism instead of a CNN, which can establish long-range dependencies in images. The Transformer has been widely used for medical tasks with high accuracy, e.g., prostatic segmentation , delineaAnalysis of noise labels section). As is known to all, different models have respective preferences for data, and the features learned from a single model are not necessarily reliable. This paper trains a set of heterogeneous models to improve robustness and accuracy by integrating their extracted features, with ACC increasing from 85.8% to 86.5% and AUC increasing from 90% to 91.2%. This study shows that our method can effectively reduce the difficulty of grading caused by noise labels. This method can be widely applied to other medical grading tasks, e.g., the grade of neurogliomas and meningiomas.Correctly grading complex samples is one of the significant indicators for evaluating the strength and stability of a deep learning model, which reflects the ability to extract general features. Our method shows some advantages when faced with noise samples and difficultly graded examples . Written informed consent for participation was not required for this study in accordance with the national legislation and the institutional requirements.MY designed the study and all laboratory studies, performed experiments, analyzed the data, and wrote the manuscript. ML, JD, and XC performed laboratory experiments. XH, YW, QL, and SW collected and analyzed the ccRCC data. LX and FZ provided help in data and statistical analysis. LW and XW participated in data analysis and interpretation. ML and BS oversaw the design of the study and all laboratory studies, data analysis, and interpretation, and wrote the manuscript. All authors contributed to the article and approved the submitted version.This work was supported by the Medico-Engineering Cooperation Funds from University of Electronic Science and Technology of China , the Municipal Government of Quzhou, as well as the Zhejiang Provincial Natural Science Foundation of China under Grant No. LGF22G010009.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.


We aimed to investigate factors related to the 90-day poor prognosis (mRS≥3) in patients with transient ischemic attack (TIA) or minor stroke, construct 90-day poor prognosis prediction models for patients with TIA or minor stroke, and compare the predictive performance of machine learning models and Logistic model.We selected TIA and minor stroke patients from a prospective registry study (CNSR-III). Demographic characteristics,smoking history, drinking history(≥20g/day), physiological data, medical history,secondary prevention treatment, in-hospital evaluation and education,laboratory data, neurological severity, mRS score and TOAST classification of patients were assessed. Univariate and multivariate logistic regression analyses were performed in the training set to identify predictors associated with poor outcome (mRS≥3). The predictors were used to establish machine learning models and the traditional Logistic model, which were randomly divided into the training set and test set according to the ratio of 70:30. The training set was used to construct the prediction model, and the test set was used to evaluate the effect of the model. The evaluation indicators of the model included the area under the curve (AUC) of the discrimination index and the Brier score  of the calibration index.P<0.05). All models, both those constructed by Logistic regression and those by machine learning had good calibration.A total of 10967 patients with TIA and minor stroke were enrolled in this study, with an average age of 61.77 ± 11.18 years, and women accounted for 30.68%. Factors associated with the poor prognosis in TIA and minor stroke patients included sex, age, stroke history, heart rate, D-dimer, creatinine, TOAST classification, admission mRS, discharge mRS, and discharge NIHSS score. All models, both those constructed by Logistic regression and those by machine learning, performed well in predicting the 90-day poor prognosis (AUC >0.800). The best performing AUC in the test set was the Catboost model (AUC=0.839), followed by the XGBoost, GBDT, random forest and Adaboost model . The performance of Catboost and XGBoost in predicting poor prognosis at 90-day was better than the Logistic model, and the difference was statistically significant(Machine learning algorithms were not inferior to the Logistic regression model in predicting the poor prognosis of patients with TIA and minor stroke at 90-day. Among them, the Catboost model had the best predictive performance. All models provided good discrimination.The online version contains supplementary material available at 10.1186/s12874-022-01672-z. Stroke was the second leading cause of death worldwide and the leading cause of mortality and disability in China , 2. In pAll anonymized data in this study could be shared by request from any qualified investigator.The CNSR-III database was a nationwide prospective clinical registry of ischemic stroke or TIA in China based on etiology, imaging, and biological markers. The detailed study design of the CNSR-III trial has been described elsewhere . BetweenIn this study, minor stroke was defined as an NIHSS score≤5. There were 15,166 patients in CNSR-III, and 4086 patients with an NIHSS score >5 were excluded.There were 11,080 patients with TIA and minor stroke (an NIHSS score≤5). 113 patients were excluded (Including patients whose mRS score was missing for 90-day in the follow-up data). A total of 10967 patients were eligible for the study. Supplementary Figure For baseline variables, we investigated demographic characteristics , smoking history, drinking history (≥20g/day), physiological data , medical history ,secondary prevention treatment, in-hospital evaluation and education , laboratory data, neurological severity (admission and discharge), mRS score (admission and discharge) and TOAST classification. Finally, a total of 44 variables were included as baseline variables for analysis  ≥ 3, and 0-2 was defined as a good prognosisWe used various machine learning techniques to predict poor prognosis at 90-day: CatBoost (CB) , XGBoostCB: CatBoost is an innovative ordered gradient boosting algorithm, which uses ordered target-based statistics for categorical features processing and permutation strategies to avoid prediction shift. Its base learner was an oblivious tree and each tree corresponds to a partition of the feature space. The model learns the feature space partition at each training iteration and finally obtains the aggregated data as a classification result .XGB: XGBoost was a Boosting library developed by Chen Tianqi of the University of Washington in 2016. It has both a linear scale solver and a tree learning algorithm. XGBoost was a second-order Taylor expansion of the loss function, and a regular term was added to the objective function to find the optimal solution as a whole, which was used to weigh the decline of the objective function and the complexity of the model, avoid overfitting, and improve the model The efficiency of the solution .GBTD: The decision tree used in GBDT was a regression tree. The goal of each training was to reduce the error of the last training and finally get the minimum error. The model uses the gradient descent method to reduce the error , 12.RF: RF was an integrated supervised learning method, which consists of multiple decision trees corresponding to different sub-data sets. Calculate the results for each tree, and get the average of the predicted results. This approach allows reducing variance in decision trees , 13.Ada: The basic idea of the AdaBoost algorithm was to classify a group of weak learners through weighted majority voting (or sum). It takes into account the mistakes of previous weak learners and repeatedly updates the data , 14, 15.Missing value processing: Continuous variables were filled using linear imputation, and categorical variables were filled using mode. Most of the 44 variables had no missing values, and 4 variables had missing values greater than 5%. We examined the distribution of the laboratory data after imputation of the variables used in the modeling and found no significant difference in the distribution of the data before and after imputation  and the poor prognosis group (mRS≥3) were randomly grouped according to a ratio of 70:30, divided into a training set and test set, repeated five times. The test set was only used for model testing. We use GridSearch CV for 5-fold cross-validation tuning in the training set. After tuning each model to the optimum in the training set, we performed model evaluation on a clean test set to test the predictive performance of the five models established in this study. Supplementary Table  and the A total of 15166 patients were registered to the cohort during the study period. After excluding 4086 patients with NIHSS score >5 and 113 patients with missing clinical outcomes(90-day mRS), 10967 patients were finally included . First, we performed univariate analysis in the training set and found that 28 variables (P<0.05) differed between patients with and without a poor functional outcome at 90-day (based on 44 baseline variables). They were demographics , smoking history, drinking history (≥20g/day), physiological data (systolic blood pressure and heart rate),medical history , secondary prevention treatment, in-hospital evaluation , laboratory data, neurological severity (admission and discharge), mRS score (admission and discharge) and TOAST classification. Table The mean age of the 10967 patients was 61.77±11.18 years and 30.68% were female . The above factors can be used as predictive indicators to build a predictive model. In TOAST classification, LAA was set as a control variable. In laboratory indicators, D-dimer and creatinine were risk factors for a poor prognosis of 90-day . Compared with males, females were risk factors for a poor prognosis of 90-day (OR= 1.226),other factors were showed in Table In the training set, the risk factors selected by the univariate analysis were included in the multivariate analysis. Multivariate Logistic regression results showed that sex, age, stroke history, heart rate, D-dimer, creatinine, TOAST classification, admission mRS, discharge mRS and discharge NIHSS score were related to the 90-day poor prognosis . Figure In the training set, We used the multivariate Logistic regression model as a feature selection method, and determined 10 variables as predictors to establish different prediction models. Five tree model classifiers, namely CB, XGB, GBDT, RF and Ada, were trained on the training data set repeated 5 times, and 5-fold cross-validation was performed to adjust the optimal parameters. Finally, the five optimal models adjusted training sets will be evaluated in test sets. The results of AUC, accuracy, PPV, NPV, F1-score, and Brier score in the test set were showed in Table Machine learning (ML) methods have gained increasing popularity in medical research. Machine learning-based algorithms may be used for screening, diagnostic, or prognostic purposes. ML methods have been tested in several medical conditions to predict a future health state in cardiovascular medicine. This study aimed two-fold: On the one hand, the training set of this study was used to discover the related factors of poor 90-day prognosis in patients with TIA and minor stroke, which provided reference for related research and clinical work. On the other hand, factors found in training sets were used as indicators for predicting poor patient prognosis at 90-day. 90-day poor prognosis prediction models for TIA and minor stroke patients were constructed, and the prediction performance of the ML algorithm and the Logistic model was compared.We performed univariate and multivariate analyses of patients with TIA and minor stroke in the training set and found that the 90-day prognosis prediction of patients with TIA and minor stroke was determined by many factors: sex, age, stroke history, heart rate, D-dimer, creatinine, TOAST classification, admission mRS, discharge mRS, and discharge NIHSS score.P<0.05), and CB model has the best prediction performance.The discrimination and the calibration of each model were good. The discrimination (AUC) between the CB model and the XGB model was better than the Logistic model  , 19, ageIn this study, we found that the risk of 90-day poor prognosis was higher for older TIA and minor patients, and the risk of 90-day poor prognosis was higher for females than for males. The human body's natural aging process was irreversible with age Therefore, age and sex were non-intervention factors. In addition, the proportion of stroke history in the poor prognosis group was significantly higher than that in the good prognosis group, suggesting that stroke history was a predictor of the 90-day poor prognosis. Previous studies had shown that high heart rate was an independent risk factor for stroke and cardiovascular and cerebrovascular events. Patients with high a heart rate had a significantly higher incidence of stroke and adverse cardiovascular and cerebrovascular outcomes –28. ThisThis study found that a high level of creatinine at admission was associated with a poor prognosis at 90-day, and patients with a high creatinine level were at higher risk of a poor prognosis at 90-day. In recent years, studies found a specific correlation between creatinine and nerve damage, but there were few clinical correlations between creatinine and nerve damage and stroke prognosis . Unlike P<0.05). Conventionally, AUC values>0.70 were considered to represent moderate discrimination, values>0.80 good discrimination, and values>0.90 excellent discrimination. In terms of calibration, the Brier score of each model was better, and the CB, XGB and GBDT models have the best correction effect (Brier score=0.047). In addition, Supplementary Figure All six models showed good discrimination in this study (AUC>0.80), which was consistent with previous studies –39. AmonIn previous studies, when comparing ML models and the traditional Logistic model, ML models were superior to logistic model likely due to the features instead of the algorithm itself , 40–42, This study showed that compared with the Logistic model, the CB model showed the highest AUC. This result showed that the CatBoost algorithm can well predict patients with poor 90-day prognosis from TIA and minor stroke patients. CatBoost was a new integrated algorithm based on decision tree gradient lifting developed by researchers and engineers of Yandex in Russia. Previously, the two mainstream algorithms in the Boosting family were XGBoost and LightGBM, and according to official evaluation, the new CatBoost member model in the Boosting family had better performance than the above two algorithms. It was also consistent with the results of this study. CatBoost’s success might have been explained by its ability to process categorical features and model feature combinations. Additionally, CatBoost’s new capacity in undertaking feature combinations increased its nonlinear modeling abilities. In addition, although the LR algorithm performed well, it may also be because we used the stepwise regression method to screen the characteristic variables. Perhaps the CatBoost algorithm will be more convenient when there are more feature variables, a larger amount of data, or even multivariate heterogeneous data, and the performance difference between the two algorithms will be greater. As an emerging algorithm, CatBoost has unique advantages. It competes with any leading machine learning algorithm and handles categorical features automatically, requiring less hyperparameter tuning, enhancing model stability and reducing the possibility of overfitting.As our analysis showed, machine learning models, especially CatBoost algorithm, showed promising results in predicting poor outcome in patients with 90-day TIA and minor stroke. Due to the particularity of clinical research, it is extremely important to select variables based on prior knowledge, and to use stepwise regression with more interpretability for features selection. Although our study used only 10 predictors for modeling, the machine learning model, especially the CB model, showed better predictive performance without sacrificing accuracy. And due to the low cost of the number of features, it is more convenient to perform external verification and input into Clinical Decision Support System (CDSS) for clinical practical application prediction in the future. The knowledge-driven model of combining new algorithms in machine learning may complement the purely data-driven approach of previous research in the field of machine learning for disease. I believe that our research can be applied to electronic health records to provide services for doctors, health care workers.This study has several limitations. First, although the ML algorithm could have high accuracy and AUC performance, especially CB models, we still need more external validation to check the robustness of ML model. Second, it could be combined with a better selection algorithm to improve accurate prediction In the future. Third, imaging and omics data were not included in this study, which may limit the predictive performance to some extent. Fourth, this study performed a single imputation of missing values, which will inevitably lead to certain bias, but if the missing values are deleted, certain selection bias cannot be avoided; Since mehtods of directly testing missing at random is not available yet, we are not confident to state that variables with >5% missing values are missing at random . NeverthWe will further explore the adaptation conditions of different models for ischemic stroke and conduct comprehensive studies on the development of predictive models and predictive performance to provide a more comprehensive reference for establishing a perfect prognosis prediction of ischemic stroke.Additional file 1.Additional file 2.Additional file 3.Additional file 4.Additional file 5.Additional file 6.


Background and Objectives: Neonatal mortality is a global public health issue, disproportionately affecting low- and middle-income nations. Although Romania is a high-income nation, according to the European Union’s most recent demographic data, it had the second-highest infant death rate in 2019. Although significant progress has been made in the last three decades in lowering newborn mortality, more initiatives to accelerate progress are required to meet the 2030 Sustainable Development Goals (SDG) objective. Therefore, we aimed to develop an observational study to determine the influence of maternal factors on in-hospital neonatal intensive care unit admission and mortality in premature infants born in western Romania. While newborn mortality has decreased globally, the pace of decline is far less than what is desired. Materials and Methods: A retrospective study comprising 328 premature patients and 422 full-term newborns, was developed at a tertiary obstetrics and gynecology clinic in western Romania, comprising the period of the last 24 months before the COVID-19 pandemic and the first 24 months of the pandemic. Results: The following variables were identified as statistically significant risk factors for neonatal intensive care unit admission: age > 35 years, OR = 1.59; twin births, OR = 1.14; low gestational age, OR = 1.66; preeclampsia, OR = 2.33; and peripartum infection, OR = 2.25. The same risk factors, with the exception of twin births, were significantly associated with in-hospital neonatal mortality. Except for a longer duration of maternal hospitalization and neonatal therapy with surfactant, steroids, and antibiotics, the COVID-19 pandemic did not cause significant differences in the evolution and outcomes of preterm newborns. Conclusions: The major maternal risk factors for NICU admission were advanced age, twin pregnancy, low gestational age, preeclampsia, and peripartum infection. Additionally, these characteristics contributed to a high likelihood of death, despite adequate access to medical care and advanced life support for the neonates. Understanding the causes of morbidity and death in neonates admitted to the neonatal intensive care unit enables better prioritization and planning of health services, resource reallocation, and care quality improvement. Neonatal mortality is a public health issue that affects the world’s poorest and middle-income countries the most . Even thEven while neonatal mortality is decreasing globally, the rate of decline is far slower than that of post-neonatal death under five years old. The majority of newborn deaths in low- and middle-income nations occur without a known cause of death . BecausePreterm birth is often regarded as the leading risk factor for perinatal morbidity and death, as 15 million newborns are prematurely born every year . AlthougPreterm deliveries have been observed to cause a disproportionate number of newborn fatalities . As a reAn observational study with patients enrolled between 2018 and 2021 was conducted at the University Clinic of Obstetrics and Gynecology “Bega” associated with the “Victor Babes” University of Medicine and Pharmacy in Timisoara, Romania. The research population and relevant features were identified using a population-based administrative database of patients who appeared in the outpatient setting of the same clinic throughout the study period. Our centralized database included patient medical records protected by privacy laws and obtained with the patient’s agreement, including their demographic information, medical history, and in-hospital procedures. The baseline characteristics and procedures for all patients were recorded in the hospital database, as well as in paper patient records inspected by certified clinicians participating in the current study. We performed a computerized database search to ascertain the precise diagnosis as defined by the International Classification of Diseases (ICD-10) and the procedures defined by the Current Procedural Terminology (CPT).Neonates were included in the current study based on the World Health Organization (WHO) definition of prematurity . PretermThe Timis County Emergency Clinical Hospital “Pius Brinzeu” Local Commission of Ethics for Scientific Research operates in accordance with Article 167 of Law No. 95/2006, Art. 28, Chapter VIII of Order 904/2006; with EU GCP Directives 2005/28/EC, the International Conference on Harmonisation of Technical Requirements for Registration of Pharmaceuticals for Human Use (ICH); and with the Declaration of Helsinki—Recommendations Guiding Medical Practice. Approval number 23 was assigned to the present research on 20 January 2022.The variables considered for statistical analysis comprised maternal background data . A high obstetrical risk pregnancy was considered as any condition associated with a pregnancy where there is an actual or potential risk to the mother or fetus . Data wet-test and the Mann–Whitney U-test were employed. A Shapiro–Wilk test was performed to assess the normality of data. The proportions were analyzed statistically using the Chi2 and Fisher’s exact tests. A multivariate regression analysis was used to evaluate independent risk variables for newborn death in-hospital after adjusting for confounding factors. A Kaplan–Meier probability curve was plotted for independent risk factors to estimate the probability of death for these categories. The criterion for significance was fixed at 0.05. IBM SPSS software version 26.0 was used for statistical analysis . Absolute and percentage values were used to represent categorical variables. For parametric and non-parametric variables, respectively, the Student’s p-value < 0.001). The duration of hospitalization was significantly longer in preterms, with a median of 5.0 days, compared with a median of 3.3 days in the full-term group . There was 81.4% of women at high obstetrical risk in the group of preterm births, compared with only 17.5% among full-term births . All maternal comorbidities included in the study were in higher proportions within the cases associated with prematurity, and they were all statistically significant .p-value < 0.001). Birth weight was significantly lower in the premature group, with an average of 1503 g, compared with 2597 g in the full-term group . Most preterm deliveries were C-sections . As a consequence of prematurity, the number of NICU admissions and resuscitation requirements were statistically significantly higher. Preterm newborns had a median of 16.9 days of hospitalization, compared with 3.7 days for those born full-term . Therefore, the mortality in the group of preterms was statistically significantly higher . Lastly, the neonates born before term required significantly more medication therapy with steroids and antibiotics, as well as a higher need for surfactant therapy .Among neonatal characteristics , an abnop-value = 0.003). Anemia was also significantly different between the four years that were analyzed, although without a variation associated with the pandemic, since the highest prevalence of maternal anemia was in 2018 . Among neonatal characteristics, we did not identify any significant differences, although neonatal therapy with surfactant, steroids, and antibiotics was statistically higher during the pandemic.The four-year comparison of preterm newborns between the pre-pandemic and pandemic periods is described in p-value < 0.001), while the goodness-of-fit measure for the linear regression model was also significant at the 95% significance threshold , as represented in The Pearson correlation coefficient between birth weight and the number of hospitalization days was strong and negative . The survival analysis presented in p-value = 0.040). The survival analysis in p-value = 0.836). The Kaplan–Meier survival prediction for premature infants presented in The current study identified that advanced maternal age, twin pregnancy, low gestational age, preeclampsia, and peripartum infections were the predominant maternal risk factors for NICU admission. Additionally, these characteristics increased the chance of mortality, despite the neonates’ access to competent medical care and enhanced life support. Our results are also consistent with previous research indicating that congenital abnormalities were an important cause of NICU admission and mortality among premature newborns, although several maternal risk factors were identified as having lower but significant odds for the same complications ,30. AlthThere is a considerable difference in mortality between infants delivered at 24 weeks and full-term, indicating the important influence of prematurity on neonatal survival. Increased preterm births result in an increase in newborn mortality. Congenital abnormalities, delivery trauma, birth asphyxia, and hospital-acquired infection are other reasons for newborn mortality that were previously reported but not evaluated in the current study . A recenAssessing the number and etiology of these significant occurrences, as well as their risk factors, begins with classifying and reporting neonatal fatalities appropriately. A regionalized and integrated perinatal network plan should be established to minimize perinatal morbidity and death and to enhance preterm delivery and other high-risk newborn survival. Mortality data should be accessible by geographic region, rural or urban, site of death, date, underlying cause, and other variables such as socioeconomic status . This maSimilar to our results, earlier research has shown that immaturity, as measured by gestational weeks at delivery, is a major predictor of newborn mortality . NeonataSeveral other background factors were not analyzed in the current study. It was previously observed that neonatal mortality rate did not differ significantly by mother’s age, income, or employment status; on the other hand, mothers with a high school education or less were associated with a higher rate of neonatal deaths, likely due to a lack of awareness about how and when to seek medical care, particularly in emergency situations ,40. SomeEven though the majority of neonatal fatalities are preventable with effective treatments such as access to emergency obstetric and neonatal care, certain severe risk factors predispose newborns to severe complications and death, despite access to critical healthcare services. Nonetheless, stakeholders must understand the socioeconomic and geographic patterns of newborn mortality in order to expand access to effective therapies with a particular emphasis on high-risk populations. This will guarantee that every pregnant woman and newborn has the same level of access to life-saving treatments.


This research aimed to identify independent risk factors for hyperuricemia (HUA) in diabetic kidney disease (DKD) patients and develop an HUA risk model based on a retrospective study in Ningbo, China.Six hundred and ten DKD patients attending the two hospitals between January 2019 and December 2020 were enrolled in this research and randomized to the training and validation cohorts based on the corresponding ratio (7:3). Independent risk factors associated with HUA were identified by multivariable logistic regression analysis. The characteristic variables of the HUA risk prediction model were screened out by the least absolute shrinkage and selection operator (LASSO) combined with 10-fold cross-validation, and the model was presented by nomogram. The C-index and receiver operating characteristic (ROC) curve, calibration curve and Hosmer–Lemeshow test, and decision curve analysis (DCA) were performed to evaluate the discriminatory power, degree of fitting, and clinical applicability of the risk model.P > 0.05), and the results of the DCA showed that the prediction model could be beneficial for patients when the threshold probability was 9–79%. Meanwhile, the risk model was also well validated in the validation cohort, where the C-index was 0.843 (95% CI: 0.780–0.906), the degree of fit was good, and the DCA risk threshold probability was 7–100%.Body mass index (BMI), HbA1c, estimated glomerular filtration rate (eGFR), and hyperlipidemia were identified as independent risk factors for HUA in the DKD population. The characteristic variables  were screened out by LASSO combined with 10-fold cross-validation and included as predictors in the HUA risk prediction model. In the training cohort, the HUA risk model showed good discriminatory power with a C-index of 0.761 (95% CI: 0.712–0.810) and excellent degree of fit (Hosmer–Lemeshow test, The development of risk models contributes to the early identification and prevention of HUA in the DKD population, which is vital for preventing and reducing adverse prognostic events in DKD. Diabetes mellitus (DM) is a metabolic disease caused by a combination of genetic, environmental factors and dietary habits and is characterized by chronic elevation of blood glucose and inadequate insulin secretion. With the prolonged course of DM and poor long-term glycemic control, the accumulation of abnormal substances in the metabolic process  can cause functional damage to multiple organs of the body, including the kidneys, retina, and heart and brain vessels. Among them, diabetic kidney disease (DKD) and cardiovascular disease are the leading causes of death and disability in diabetic patients, posing a significant threat to human physical and mental health.DKD is one of the most important microvascular complications of DM, with an increased urinary albumin excretion rate and reduced glomerular filtration rate as the main clinical manifestations . The maiThe public has increasingly recognized HUA as a risk factor for DKD –8, and U2 for more than 3 months; (3) renal biopsy consistent with DKD pathological changes. The study was approved by the ethics committee of the Affiliated Hospital of Medical School, Ningbo University (KY20171112), and written informed consent was obtained from all participants.From January 2019 to December 2020, questionnaires were administered to T2DM patients who were outpatients and inpatients in two hospitals in Ningbo, including the Affiliated Hospital of Medical School, Ningbo University, Yinzhou No. 3 Hospital. Relevant clinical data were obtained and recorded through questionnaires, physical examinations, and laboratory tests. To ensure the accuracy of the study, the completeness of each individual data was checked, those with more missing values  were removed, and those with fewer missing values  were filled with multiple imputation . After dInclusion criteria: T2DM; age ≥18 years; clearly diagnosed DKD. exclusion criteria: other renal diseases; severe life-threatening organ dysfunction of the heart, lungs, kidney and liver; tumors; hormone use within the past 6 months.The demographic and clinical data for this study were primarily information that was readily available, relatively complete, and comparable in clinical practice, which was collected through a questionnaire. All staffs involved in the questionnaire received standardized training. The questionnaire survey collected participants' general characteristics , biochemical indicators in the last 3 months , and chronic complications. Biochemical indicators were obtained from each hospital's electronic laboratory record system. Confirmation of chronic complications required a review of medical history and inquiry and was recorded only if there was a clear previous diagnosis.https://www.R-project.org). Comparison of the count data between the two groups was performed by chi-square test. All tests were two-tailed, and a P value of < 0.05 was considered statistically significant.Six-hundred ten patients with DKD were enrolled in this research, and data information for all variables was expressed as counts (%). Statistical analysis was performed with R software  , 18, whiThe risk predictive models were evaluated in terms of discriminatory ability [C-index and receiver operating characteristic (ROC) curve], calibration ability , and clinical applicability [decision curve analysis (DCA)] .2, and hyperlipidemia compared with the control group. BMI (P = 0.025), SBP (P = 0.004), PBG (P = 0.017), HbA1c (P < 0.001), UA (P < 0.001), eGFR (P < 0.001) and hypertension (P < 0.001) were found to be significantly different between the two groups by univariate analysis , an overwhelming majority of age >60 years (73.5 vs. 75.8%), and a predominance of T2DM duration of 15–20 years (34.7 vs. 26.3%). In the DKD population, the HUA group had a higher proportion of smoking history, drinking history, obese patients, FBG >7 mmol/L, HbA1c >8%, hypertension and eGFR ≤ 120 mL/min/1.73manalysis . A totalanalysis .P < 0.1). BMI, HbA1c, eGFR, and hyperlipidemia were identified as independent risk factors for HUA in the DKD population  were used to assess the discriminatory ability of the risk model. In the training cohort, the C-index was 0.761 (95% CI: 0.712–0.810), and the AUC was 0.761, while in the validation cohort, the values were 0.843 (95% CI: 0.780–0.906) and 0.843 .P > 0.05)  .DCA is a method that has been used to evaluate the clinical applicability of risk models. DKD seriously affects the quality of life of T2DM patients and threatens their lives, while an increasing number of scholars have started to pay attention to and study the relationship between UA and DKD –26. ThroThe proportion of HUA among DKD patients was found to be 32.4% in the study, higher than the 13% in Zhengzhou, China , which mThe construction of predictive models is important for the early diagnosis and prevention of diseases. Various HUA risk prediction models have been established in recent years based on normal populations in different regions –13, 39, Compared to previous studies, we have the following advantages. First, we identified risk factors associated with HUA based on the DKD population, which has rarely been reported. HUA is well known as a risk factor for DKD, while the identification of HUA risk factors could help delay the progression of DKD and reduce the occurrence of adverse prognostic events. Second, we may be the first to develop an HUA risk prediction model based on a DKD population, which has important implications for the early diagnosis and prevention of the disease. Certainly, there are some limitations in our study. First, the diagnosis of DKD is predominantly clinical, so the presence of nondiabetic kidney disease cannot be completely ruled out. Second, as a cross-sectional study, there is no escape from the fact that our sample size was limited. Third, the HUA risk prediction model is only validated by internal datasets, while the validation of external datasets is necessary. Furthermore, we will expand the sample size to improve the stability of the model; meanwhile, we will cooperate with multiple centers to obtain external datasets to validate the model.Briefly, based on a multicenter study in Ningbo, China, we identified independent risk factors  associated with HUA and constructed an HUA risk prediction model in the DKD population. The establishment of risk prediction helps us to identify individuals at high risk of HUA early in the DKD population, which is important for the prevention and reduction of adverse prognostic events in DKD.The original contributions presented in this study are included in the article, further inquiries can be directed to the corresponding author/s.The study was reviewed and approved by the Ethics Committee of the Affiliated Hospital of Medical School, Ningbo University, Ningbo, China. The patients/participants provided their written informed consent to participate in this study.GH and ML conceived and designed the research, drafted the manuscript, and took part in the discussion. GH performed the statistical analysis. YM and YL revised the manuscript. All authors contributed to the article and approved the submitted version.This research was supported by grants from Natural Science Fund of Ningbo, China (No. 2018A610248), NINGBO Medical & Health Leading Academic Discipline Project (No. 2022-F24), Zhejiang Medicine and Health Technology Project, China (No. 2018ZH029 and 2020KY871), the Major Project for Science and Technology Innovation 2025 in Ningbo, China (No. 2019B10035), Ningbo Social Development, China (No. 2019C50080), and Ningbo Social Welfare Research (No. 2022S047).The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.


Bio-based poly(lactic acid) (PLA) composite films were produced using unmodified soda micro- or nano-lignin as a green filler at four different contents, between 0.5 wt% and 5 wt%. The PLA–lignin composite polymers were synthesized by solvent casting to prepare a masterbatch, followed by melt mixing. The composites were then converted into films, to evaluate the effect of lignin content and size on their physicochemical and mechanical properties. Differential scanning calorimetry (DSC), supported by polarized light microscopy (PLM), infrared spectroscopy (FTIR-ATR), X-ray diffraction (XRD), and transmission electron microscopy (TEM) were employed to investigate the PLA crystallization and the interactions with Lignin (L) and Nanolignin (NL). The presence of both fillers (L and NL) had a negligible effect on the glass transition temperature (chain diffusion). However, it resulted in suppression of the corresponding change in heat capacity. This was indicative of a partial immobilization of the PLA chains on the lignin entities, due to interfacial interactions, which was slightly stronger in the case of NL. Lignin was also found to facilitate crystallization, in terms of nucleation; whereas, this was not clear in the crystalline fraction. The addition of L and NL led to systematically larger crystallites compared with neat PLA, which, combined with the higher melting temperature, provided indications of a denser crystal structure in the composites. The mechanical, optical, antioxidant, and surface properties of the composite films were also investigated. The tensile strength and Young’s modulus were improved by the addition of L and especially NL. The UV-blocking and antioxidant properties of the composite films were also enhanced, especially at higher filler contents. Importantly, the PLA–NL composite films constantly outperformed their PLA–L counterparts, due to the finer dispersion of NL in the PLA matrix, as verified by the TEM micrographs. These results suggest that bio-based and biodegradable PLA films filled with L, and particularly NL, can be employed as competitive and green alternatives in the food packaging industry. The significant environmental impact of petrochemicals, as well as the depletion of petroleum resources, calls for the rapid replacement of fossil-based materials with biodegradable ones manufactured from renewable resources. One of the most promising bio-based polymers is polylactic acid (PLA), an aliphatic polyester that can be obtained from renewable resources rich in starch or sugars, such as corn, potatoes, and sugarcane. Starch can be enzymatically hydrolyzed to produce sugars, and sugars can be fermented to produce lactic acid. ,2. PLA iNevertheless, PLA presents some drawbacks such as inherent brittleness , low crystallization rate, quite slow biodegradation in the soil, moderate gas barrier properties, and low thermal stability . To overLignin, in particular, is a biopolymer derived from agro-industrial waste residues. The source and pre-treatment process of lignin determine its biological, morphological, and physicochemical characteristics . Lignin ® lignin at 7 and 15 wt% loading by compounding at 175 °C. On the one hand, the synthesized PLA–L composites showed a gradual increase in thermal stability, Young’s modulus, and impact strength with increasing lignin content. On the other hand, the elongation at break and tensile strength were significantly reduced. Fereira da Silva et al. ) or/and the presence of NL and L. The extreme cases are recorded in Tg (57–58 °C) with the addition of lignin. In addition, none of our composites contained severely short chains, namely, below the molar mass threshold for entanglements  of the matrix, the Δcp,n dropped with the presence of lignin. Moreover, there was a stronger effect of NL compared to L. Since the polymers are amorphous, we could employ a widely adopted model to rationalize this systematic effect of lignin. According to Schick and coworkers . This indicates that, for similar mobilities and lengths of the polymer chains, the presence of L and NL clearly facilitated nucleation. In In α′ crystal forms. Within the complex melting region, the melting of low-quality or metastable crystals takes place, along with partial recrystallizations, and, finally, overall melting , the Tm2 was recorded as elevated in the presence of lignin. This effect could suggest a better crystal quality, e.g., more dense crystals/lamellae packing.Finally, it should be noted that the melting was recorded within all samples as complex peaks at relatively low temperatures. This is the expected situation for cold crystallized PLA in combination with relatively low [η] ,65,66 an melting ,51,69. TXRD patterns of all composite films are shown in The morphology of the PLA crystallites during the crystallization from the glass  was investigated using PLM. All the samples were heated up to 185 °C and then quenched to room temperature. The quenched materials were then heated with a heating rate of 5 °C/min at 110 °C, a temperature at which all samples exhibited the phenomenon of cold crystallization . The micTm2 for the composites. Unfortunately, the optical resolution does not allow further distinguishing the effects arising from the different types (size) of fillers.The results of PLM revealed tremendous changes with respect to the semicrystalline morphology. By the latter term, we refer to the numbers, sizes, and distribution of polymer crystals throughout the sample volume. The addition of L and NL systematically led to larger crystallites, appearing as brighter spots in the PLM, when compared with neat PLA. This suggests a denser crystal structure (thicker lamellae packing) in the composites. This effect correlates well with the increase mentioned above in The PLA-L/NL distribution was also investigated using TEM. From the TEM micrographs , the L aThe mechanical properties of the composites are essential for understanding their ability to maintain their structure under mechanical stress, related to various applications . In thisThe Young’s modulus showed a two-fold increase with NL addition, even at very low loadings of 0.5 wt%, compared to neat PLA, within experimental error. Specifically, the Young’s modulus increased from 638 ± 50 MPa for neat PLA to approximately 1300 ± 90 MPa for all PLA–NL composites. Similar values were observed for the PLA–L composites, albeit at L loadings greater than 1 wt%. The maximum values of 1380 MPa and 1373 MPa were obtained for 5 wt% L and 2.5 wt% NL, respectively. Sun et al.  suggesteFinally, the elongation at break was initially reduced with an increasing filler content, and all composites became more brittle compared to neat PLA. Notably, the elongation at break did not follow a specific trend with increasing filler content, and therefore the effect of L/NL addition could not be clearly identified. The reduction of the elongation at break was attributed to the brittle nature and the low molecular weight of the PLA, as well as to the rigidity of the lignin particles ,26,47. The antioxidant ability of lignin stems from the presence of phenolic hydroxyl groups . In thisThe UV-Vis barrier properties of the PLA–L/NL composites synthesized herein were also investigated. UV-Vis barrier properties are crucial for PLA-based films, especially for food packaging applications, since UV radiation can cause degradation of plastic composites. The surface functional groups of lignin such as phenols, ketones, and chromophores can improve the UV absorbance of lignin-containing composites . Figure Contact angle measurements of the PLA and PLA–L/NL composites were performed, to investigate the change in the hydrophobicity of the prepared films. In this study, unmodified lignin in micro- and nano-scale was used for the preparation of PLA biocomposites via combining solvent casting and melt mixing. Initially, the effect of fillers on the crystallization and semicrystalline morphology of the composites was investigated. The glass transition temperature of the composites was found to be unaffected; however, the corresponding heat capacity was reduced. When the change in molar mass in the composites was considered, the fillers were proven to play a facilitating role in the nucleation and resulted in a denser crystal structure in the composites. The FTIR and XRD results indicated non-negligible interactions between the lignin filler and the PLA matrix, especially in the case of NL. This was depicted in the tensile strength of the synthesized composites, which was significantly increased compared to neat PLA. Moreover, the tensile strength was consistently higher for the PLA–NL nanocomposites compared to their PLA–L counterparts at the same filler contents. Notably, the PLA–NL nanocomposite with 2.5 wt% NL showed a three-fold enhancement of tensile strength compared to the neat PLA. The Young’s modulus of the PLA–L/NL biocomposites was also increased, reaching maximum values of 1380 MPa and 1373 MPa for 5 wt% L and 2.5 wt% NL, respectively, compared to 638 ± 50 MPa for neat PLA. By contrast, the elongation at break of the composites decreased with the addition of both fillers. As revealed in the TEM micrographs, NL was dispersed in the PLA matrix in nanoscale range, while the L was mainly in μm sizes. The multifunctional effect of the L/NL integration with PLA was also demonstrated by evaluating the antioxidant capacity of the composites with a DPPH assay. The addition of L/NL in PLA resulted in an increase of the antioxidant capacity with increasing filler content. As above, the PLA–NL composites constantly outperformed their PLA–L counterparts at the same filler contents. For instance, the residual DDPH content for PLA-5 wt% L and PLA-5 wt% NL composites was 50% and 30% after 24 h, respectively. Finally, the chromophoric nature of lignin provided a UV blocking ability to the PLA-based films, which was proportional to the filler content. Overall, it was observed that the addition of lignin, and especially NL, which showed a better performance in all characterization studies, to PLA could be an alternative method for producing greener multifunctional PLA-based nanocomposites with added value for food packaging and other applications.


This study was to explore the relationship of older adults' demographic information, physiological indices, and stages of frailty with their risk of falling.  In the cross-sectional study, a total of 221 older adults with the mean age 74.9 (SD = 6.8) years old were surveyed by senior fitness test. t = 5.05, p < .000), right-hand grip strength , and total grip strength , time up and go test , and 30-sec chair stand test  with the risk of falling. According to the logistic regression analysis results, long-term medication  and right-hand grip strength  are the main predictors of older adults' risk of falling.  Results were observed in terms of participants' physical fitness, with significant differences being observed in the correlations of left-hand grip strength ( Older females with low education, history of falls, weaker grip strengths; taking longer to finish the TUG test; and standing fewer times during the 30-second chair stand test were at risk of fall. In prediction, older people using long-term medication were at lower risk of falling, and the greater the hand grip strength was, the lower the fall risk was. According to the research results, nursing personnel must develop care programs and improve older adults' risk of falls. Population aging is a worldwide crisis deserving attention. According to the World Health Organization, an “aging society” comprises more than 7% people aged 65 years or older; for an “aged society”, the corresponding ratio exceeds14%; and for a “hyper-aged society”, the ratio exceeds 20%.The number of people aged 65 years or older worldwide is estimated to rapidly rise from 900 million to 3200 million from 2016 to 2100. According to statistics from the Ministry of the Interior, R.O.C. (Taiwan), the ratio of adults aged 65 years or older in Taiwan reached10.6% by the end of 2009. This was estimated to rise to 14.7% in the next 10years and further to 37.5% in 2056 [Research on frailty indicated the prevalence of frailty among older adults in the United States was 9.6%, and the prevalence of prefrailty was 47% . In the According to research reports, the prevalence of falls among community-dwelling older adults was approximately 30% to 40%, and half of the older adults suffered from recurrent falls . AccordiTherefore, this study conducted investigations in communities to examine community-dwelling older adults' basic attributes, physical fitness, and frailty stages in relation to fall risk predictability. The results showed that it was possible to discover fall risk factors early enough to prevent or postpone the future occurrence of adverse health incidents among older adults, subsequently alleviating family care load and improving quality of life for older adults.This study first analyzed the basic attributes, physical fitness, frailty stage, and fall risk among community-dwelling older adults, and this was followed by an analysis of differences in basic attributes, physical fitness, frailty stage, and fall risk. Last, the study explored participants' basic attributes, physical fitness, and frailty stages in relation to fall risk predictability.Frailty, a continuum of malfunction in physiological systems, is a complex and dynamic condition in which elements related to the body, mind, and society interact. It is related to age but preventable and predictable by certain factors; therefore, aging does not necessarily cause frailty. Meanwhile, frailty is not related to any specific disease but increases comorbidities . AccordiFried et al.  distinguThe criteria for frailty of Study of Osteoporotic Fracture (SOF) Index proposed by Ensrud et al.  compriseThe aforementioned literature showed that CHS index and SOF index were equally effective methods for assessing the risk predictability of negative care outcomes in community-dwelling older adults, such as recurrent falls, fractures, disability, hospitalization, and mortality. Both were applicable to community-dwelling older adults and amply discriminating. However, the CHS index was less applicable to the community context because it comprised more questions and required more complex tests  than the SOF index did .Osteoporp = 0.018). Furthermore, results from the observation by Cigolle et al. [Research indicated that frail older adults are less resistant to pressure when experiencing stressful conditions such as injury, infection, anesthesia, surgery, and medication and that they experience recurring adverse health conditions, including illness, falls, disability, hospitalization, stays in long-term care institutions, and mortality . Lu et ae et al.  on 210 ie et al.  on 331 iAccording to the aforementioned literature, older adults' physical fitness for muscle strength, balance, and walking ability, for example, was closely related to the frequency of falls. Literature on falling was mostly set in institutions and outpatient departments of hospitals. Few research in Taiwan on prefrail community-dwelling older adults was based on objective measurements that involve investigating older adults' muscle strength, physical fitness, balance, and walking ability and how these factors influenced the incidence of falls. Therefore, this study explored, through community investigation, prefrail community-dwelling older adults' basic attributes, physical fitness, and frailty stages in relation to fall risk predictability.This study applied cross-sectional research design, purposive sampling, and structured questionnaires to collect and investigate basic attributes, physical fitness, and frailty stages in relation to fall risk predictability for community-dwelling adults aged 65 years or older.This study collected data on prefrail community-dwelling adults aged 65 years or older in northern Taiwan. Older adults fulfilling the following criteria were included in the study: (1) an age of 65 years or older without being banned from doing exercise, (2) clear consciousness and ability to communicate in Mandarin or Taiwanese, (3) prefrail status established through SOF Index screening, and (4) willingness to participate in the study and ability to complete questions independently or with assistance from researchers on site. The following criteria were used for exclusion:(1) inability to participate in the study due to serious visual and audio impairments, (2) balance problems and inability to participate in the tests , and (3) with wrist fractures.α was set as 0.05, and Power was set as 0.9 with 95% of the confidence level and 5% of the confidence interval. Sample size was calculated; the total sample size was at least 221. The sex ratio of male to female in the assessable population is 1 : 2. Therefore, for 221 sampling participants, 75 males and 146 females are expected to be collected.This study used G-Power 3.1software: The characteristic included age, gender, marital status, living arrangement, education level, alcohol use history, history of falls, medical history, long-term medication, exercise status, and insomnia status.2). The scores proposed by the WHO were used. Chronic diseases such as cardiovascular diseases, diabetes, and cancer were caused by overweight and obesity [Overweight and obesity mean being abnormally or excessively fat and may lead to health risks. The main measurement is body mass index (BMI), which is obtained by the body weight(kg) divided by the square of the body height  in testing community-dwelling older adults [According to Shumway-Cook et al. , when thr adults , 28.The test uses an armless chair with a fixed height (43–46 cm).The test participant sits in the middle of the chair without leaning on the chair back. During the test, the participant places each of their hands on the opposite shoulders, crossed at the wrists, while keeping the feet flat on the floor. The participants rise to a full standing position upon hearing “go” and then sit down again, thus completing the cycle. The number of times the participants are able to complete in 30 seconds were recorded. The tools include a stopwatch and an armless chair. The test takers are given one chance, and the number of times is used as the measurement unit .R < 0.92, p < .05), and the number of times participants were able to stand decreased with age ; a correlation was observed between the number of times older adults were able to stand and leg muscle strength adjusted by weight . This indicated that older adults' leg muscle strength and endurance were significantly correlated to their activity levels as well as to future falls and hospitalization, which was also highly discriminating.Jones et al.  divided The SOF Index of Ensrud et al.  indicateThis study used the BBS for measuring a person's dynamic balance ability, which takes only15 to 20 minutes. It includes tests on 14 daily tasks, with the score for each ranging from 0 to 4. The total score is 56; test participants scoring45 or more are deemed to possess good balance ability and the ability to walk independently, whereas those scoring under 45 are deemed to be inferior in terms of physical balance and to be at risk of falls. According to Berg et al.  and ChouThe researchers first determined the principle investigator and submitted the project to the Research Ethics Committee of the National Taiwan University for review  prior to execution.n (%) and mean ± SD. Differences of the basic attributes, physical fitness, and fall risk were analyzed using chi-square test (χ2) and independent sample t-test. Risk predictability of fall risk on basic attributes and physical fitness was used by linear regression analysis.This study used SPSS Statistics 21.0 software for Windows to organize and analyze the data. Status of basic attributes, physical fitness, and fall risk were expressed as n = 113; 28.5%). The majority of participants exercised “more than three days per week” . The majority (59.7%) of participants were not insomniacs (The mean age of the study participants was 74.9 (SD = 6.8) years old. The majority of the older adults enrolled were between 65 and 75years of age (52.9%), were women , were married , and lived with family . The highest education level of the majority of participants was graduation from elementary school , and the next most common maximum education level was illiteracy . The majority of participants never drank alcohol . The number of those who had fallen was 49 (22.2%), with the majority having fallen only once. The majority of participants were not using long-term medication , and the average number of medicines used on a long-term basis by these adults was 2.11, with hypertension representing the major disease being treated . The mean left-handgrip strength was 25.22 kg (SD = 8.11), and the mean right-hand grip strength was 26.31 kg (SD = 8.66). The mean overall grip strength reached 51.54 kg (SD = 16.41). The mean for the TUG test was 8.12 seconds (SD = 3.04). In the 30-second chair stand test, the mean was 16.41 times (SD = 5.02) (The mean BMI of the study participants was 25.18 kg/m = 5.02) .t = −7.42, p < .000), gender , education level , fall history , the use of long-term medication , and the number of long-term medicines used , and the number of at risk of falls and among the older adults not at risk were significantly different(p < .05). Regarding the physical fitness, significant differences (p < .05) were observed between the left hand grip strength , right handgrip strength, overall grip strength, the TUG test result , the TUG test result  of study participants at risk of falls compared with for study participants not at risk of falls. In terms of frailty stage, a significant difference (p < .05) was observed between the older adults at risk of falls and those not at risk  and right handgrip strength  for older adults at risk of falls compared with for older adults no at risk. A further analysis demonstrated that older adults not using long-term medication were at lower risk of falling than those using long-term medication were. Greater right handgrip strength was associated with lower risk of falling , the BMI of reference for older adults must be normal between 18.5 and 25. In this study, the BMI groupings showed the majority to be close to overweight, and the respective result was close to that of Lee  on factoPrefrail older adults accounted for 34.4% of our study's participants, a proportion close to those in prefrailty in related studies. Investigations on the five frailty indexes and adverse health outcomes in community-dwelling older adults by domestic scholars found the prefrailty prevalence to be 32.3%, a result close to that of this study . The invThis study indicated significant differences regarding basic attributes, age, age grouping, gender, education, fall history, use or nonuse of long-term medication, the number of medicines used on a long-term basis, and fall risk, demonstrating a significant correlation of the seven basic attributes with fall risk. Further analysis demonstrated that community-dwelling older adults with high fall risk were advanced in age (aged 75 or older), women who had achieved a maximum education level of elementary school graduation, had a history of falls, used medication on a long-term basis, and used a greater number of medicines on a long-term basis. The results of this study in relation to age and age grouping were close to those of Lin and Wang  on prevep = 0.018), which reflected this study's results. A study by one domestic scholar [This study demonstrated that the left and right handgrip strengths, the overall grip strength, and results of the TUG test and the 30-second chair stand test significantly affected fall risk, exhibiting a significant correlation of the five physical fitness with fall risk. Further analysis indicated that individuals subject to such risks had weaker left hand, right hand, and overall grip strength; took longer to finish the TUG test; and succeeded in standing fewer times during the 30-second chair stand test. Lin et al.  tested c scholar  demonstr scholar  found th scholar , 59, 60. scholar , indicat scholar  demonstr scholar  indicati scholar  recommenp < 0.0001). These results reflected this study's.Significant differences were observed in this study for fall risk among older adults at different frailty stages. Further analysis indicated that 30% of the nonfrail older adults were at risk of falls, whereas 50% of the prefrail older adults were at risk, demonstrating a higher proportion of risk in prefrail older adults than in nonfrail ones, a result corresponding with several other ‘studies'. For example, Lu et al.  used FriThis study indicated that community-dwelling older adults with long-term medication exhibited lower fall risks. Regarding long-term medication in risk predictability, the study demonstrated that such adults with long-term medication had a lower fall risk. These results were different to those of several other studies. Hung and Lee , Lin et This study demonstrated that greater grip strength among community-dwelling older adults was associated with lower fall risk. The result resembled that of Wang et al.  on the cThis study had several limitations. First, in relation to the method, the results through cross-sectional research design could represent the physical fitness of community-dwelling older adults during a short period. This indicated only the correlation between the variables and falls and neglected to investigate how factors such as physical fitness and frailty stages in these adults affected falls at various times and in various periods. This limited the inferential levels of the research results. Second, the applied purposive sampling meant that the research results would apply to only a limited range of individuals. Despite the aforementioned limitations, this study had the advantage of being the first comparative study to address basic attributes, physical fitness, and frailty stages in relation to fall risk in community-dwelling older adults in Taiwan. Therefore, the results permit the assessment of frailty and fall risk in the risk group and the development of appropriate care interventions for preventing future falls.This study investigated the basic attributes, physical fitness, and frailty stages in relation to fall risk in community-dwelling older adults. Older females with low education, history of falls, weaker overall grip strength; taking longer to finish the TUG test; and standing fewer times during the 30-second chair stand test were at risk of fall. According to the research results, nursing personnel must develop care programs and improve older adults' risk of falls. In the prediction, older people using long-term medication were at lower risk of falling, and the greater the hand grip strength was, the lower the fall risk was. Therefore, comprehensive care plans including multicomponent physical activity programs were necessary.


Metabolic syndrome (MetS) encompasses several clinical presentations that include truncal obesity and insulin resistance at its core. MetS afflicts 23% of the adult US population, increasing their risk of diabetes and cardiovascular disease. Many studies have indicated the importance of a vegetarian diet in improving overall health and more specifically MetS components. Unfortunately, these findings have been inconsistent and cannot be extended to examine effects on MetS incidence in the younger adult population.N = 694; M = 92.82 cm; OR 0.999; p = 0.893; 95% CI ]; hypertension ; hyperlipidemia .This study aimed to conduct a retrospective analysis of a vegetarian vs. non-vegetarian dietary status in young adults (age 18–24) based on MetS components in later adulthood (age 20–30). This study focuses on elucidating any relationship between a vegetarian diet and MetS components of central obesity, hypertension, and hyperlipidemia. Methods: Waves 3 and 4 data were acquired from AddHealth. One-to-one propensity score matched vegetarians to non-vegetarians in a cohort of 535 women and 159 men. Logistical regression assessed the relationship between vegetarian status and MetS components, including truncal obesity (cm), hypertension , and hyperlipidemia (high and low). Results MetS components from ages 20 to 30 are not associated with vegetarian dietary status. Truncal obesity [Current study results were consistent with previous findings suggesting that consumption of a vegetarian diet cannot be directly linked to MetS outcomes. However, further investigation should be completed as MetS is a risk factor for several chronic diseases. Metabolic syndrome (MetS) is defined as “a cluster of metabolic disorders” by the American Heart Association . ApproxiMetabolic syndrome risk mitigation includes weight loss and increased physical activity. Consuming more fruits, grains, fish, and vegetables has also been shown to decrease the risk of MetS, and are key components of a vegetarian diet, based on a review on vegetarian diets published through the American Dietetic Association . Other sLong-term vegetarianism also had positive outcomes for diabetics. In a study among 652 patients with diabetes, 39% of patients treated with insulin and 71% of patients treated with oral hypoglycemic agents were able to discontinue medication use after following a low fat 10% energy) vegetarian diet. The low-fat vegetarian diet also resulted in a decrease in fasting blood glucose by 24%, serum cholesterol by more than 20%, and triacylglycerol by more than 30% % energy .Although many studies exist on the long-term health effects of a vegetarian diet, there are no studies that retrospectively assessed central obesity, hypertension, and hyperlipidemia in relation to vegetarian status in young adults. Retrospectively assessing their dietary status based on metabolic health outcomes in later years would allow us to understand the effects of a vegetarian diet on health outcomes in younger adults .A 2015 study by Chiu et al. focused on cross-sectional and longitudinal comparisons of metabolic profiles between vegetarians and non-vegetarians . While tThe aim of this study was to retrospectively examine the dietary status in young adults  and its relation to MetS components, namely hypertension, central obesity, and hyperlipidemia in 24–32-year-old participants (Wave IV). The secondary longitudinal data from the AddHealth study were employed to retrospectively compare vegetarian status based on MetS components, adjusting for race/ethnicity, socioeconomic status (SES), and gender as covariates. The AddHealth Study is an ongoing study that began in 1994 (Wave I), with the original goal of expanding the understanding of adolescent health and behavior. Since the study only gathered limited data (namely height and weight) in its first two waves, our study looked at the Wave III data in which participants were 18–24 years old. This age also typically coincides with completing high school and the newfound independence that follows . We hypoThe National Longitudinal Study of Adolescent Health  was a longitudinal study based on a clustered sampling design of a sample of grades 7–12 in the United States from 1994 to 1995. Systematic sampling and implicit stratification were used to make sure that the selected 80 high schools were representative of U.S. schools in regard to ethnicity, region of country, urbanicity, type, and size. Also, it was ensured that selection probability was proportional to the school size. The individuals in the study were followed with four in-home interviews through adolescence and transition to adulthood. These interviews were divided into Waves  . This stThe Add Health study did not use any existing measures, but instead, constructed questions to meet the goals of the study . As prevThis was measured by asking the following question: “Do you consider yourself a vegetarian? (0 = no and 1 = yes).Interviewers measured weight, height, and waist circumference based on standardized protocols. Waist circumference was measured at the superior margin of the iliac crest for all participants that could stand unassisted, including pregnant women, to the nearest 0.5 cm. Body mass index (BMI) was also measured and classified. Detailed information can be found elsewhere .Interviewers asked participants if they had ever been told that they have or had high blood cholesterol or triglycerides or lipids (0 = no and 1 = yes). Due to the nature of the restricted data, we were unable to access data for the Blood Spot Assay.Certified interviewers measured participants' seated, resting systolic, and diastolic blood pressures (mmHg) and pulse rate (beats/minute) according to a standardized protocol. Three measurements were performed at 30-s intervals. Blood pressure classification was based on guidelines from the Seventh Report of the Joint National Committee on Prevention Detection, Evaluation, and Treatment of High Blood Pressure. Detailed information can be found elsewhere .Covariates included race/ethnicity, SES, and gender. SES was measured by asking participants what the total household income was before taxes and deductions in 2006, 2007, and 2008. Participants were asked to include all sources of income, including non-legal sources. Values ranged from 1 = < $5,000 to 12= $50,000 and above.N = 694 matched participants .This study compared MetS components  between vegetarians and non-vegetarians. Data were used from Waves III and IV. Logistic regressions were employed to assess the association of our independent variables: hyperlipidemia, hypertension, and central obesity, along with SES, and race/ethnicity on our dichotomized dependent variable (vegetarian vs. non-vegetarian). The significance level was set at 0.05. Additionally, one-to-one propensity score matching was used to match characteristics between vegetarians and non-vegetarians with There was a total of 694 participants in this study. As we used one-to-one matching with propensity scoring, exactly half of the participants (347 participants) were vegetarian, and half were non-vegetarian. Of the 694 participants, 93.5% of the participants did not have hyperlipidemia, 42.1% of participants had a BMI between 18.5 and 25, 44.4% of participants had blood pressure within normal limits (systolic <120 mmHg and diastolic <80 mmHg), and 25.5% had an income between $50,000 and 74,999. A total of 72.9% of the participants self-reported as white, and 77.1% of the participants identified themselves as women. These values represented the mode of each variable. The mean waist size of men was 96 cm and women was a mean of 92 cm.p-values > 0.05, indicating insignificance. Additionally, our odds ratios suggest that the presence of a vegetarian diet status was not statistically significant in its association with the covariates. Detailed values are displayed in To determine whether a vegetarian diet was associated with the covariates , appropriate logistic regression analysis with one-to-one propensity scoring was performed. All the covariates displayed The sample results of the current study did not find a significant association between vegetarian diet consumption in young adulthood and measures of central obesity, hypertension, and hyperlipidemia in later years. This evidence indicates a lack of direct correlation between adherence to a vegetarian diet and MetS components.Results from previous studies indicate that the association between a vegetarian diet and traits of MetS are inconclusive, with variability seen among types of vegetarian diets. A Taiwanese cross-sectional analysis found that compared to non-vegetarians, vegetarians had significantly decreased abnormalities in BMI, systolic blood pressure, diastolic blood pressure, waist circumference, fasting blood glucose, total cholesterol, LDL, and Total cholesterol (TC): HDL ratio (OR 0.37–0.9) . The samOn the other hand, the Taiwanese study in agreement with the current study did not find a significant difference in overall MetS risk between vegetarians and non-vegetarians but did find that a lacto-ovo vegetarian diet showed a significant difference in MetS prevalence , under the modified ATP III criteria for Asians . This idMetabolic syndrome encompasses a variety of factors including dyslipidemia, glucose intolerance, hypertension, and obesity, which increase the risk for cardiovascular disease and type 2 diabetes mellitus . These iR2 = 0.173, B = 0.385, p = 0.001), a perceived safe neighborhood , and social cohesion  promoted physical activity. However, none of the neighborhood factors  were found to be significantly associated with the presence of MetS, supporting physical activity as an independent risk factor for MetS, in agreement with prior studies . Fixed variables including age, age-squared, and sex, accounted for 13% of the variation in MetS. Variance components for individuals with a shared environment in early life  and shared environments in later life  were also considered. Two of the four CpG sites (one on the CPT1A gene and one on the ABCG1 gene) were highly associated with MetS (p = 0.00007) and another site (on the SOCS3 gene) was associated with MetS (p = 0.07) to a lesser extent, in the model without shared-environment related variance. Factoring in the effects of a shared environment in early life resulted in a non-significant MetS heritability estimate . Yet, factoring in late-life shared environments resulted in increased heritability . The researchers concluded that while random effects in shared environments in early or late life did not drastically influence heritability, at times, the effect was significant enough to influence CpG heritability and the development of MetS. This study, like others preceding it, highlights the complex interworking of genetics and environment in the development of MetS  and its longitudinal association with disease outcomes are of particular interest.https://addhealth.cpc.unc.edu/).Publicly available datasets were analyzed in this study. This data can be found here: AddHealth (RP, RA, and JC contributed to writing of the manuscript. SM conducted the statistical analysis and contributed to writing of the manuscript. ZT, HA, MY, and SH provided edits to finalize the manuscript. All authors contributed to the article and approved the submitted version.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. 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