In order to solve the problem of patient information security protection in medical images,whilst also taking into consideration the unchangeable particularity of medical images to the lesion area and the need for med...In order to solve the problem of patient information security protection in medical images,whilst also taking into consideration the unchangeable particularity of medical images to the lesion area and the need for medical images themselves to be protected,a novel robust watermarking algorithm for encrypted medical images based on dual-tree complex wavelet transform and discrete cosine transform(DTCWT-DCT)and chaotic map is proposed in this paper.First,DTCWT-DCT transformation was performed on medical images,and dot product was per-formed in relation to the transformation matrix and logistic map.Inverse transformation was undertaken to obtain encrypted medical images.Then,in the low-frequency part of the DTCWT-DCT transformation coefficient of the encrypted medical image,a set of 32 bits visual feature vectors that can effectively resist geometric attacks are found to be the feature vector of the encrypted medical image by using perceptual hashing.After that,different logistic initial values and growth parameters were set to encrypt the watermark,and zero-watermark technology was used to embed and extract the encrypted medical images by combining cryptography and third-party concepts.The proposed watermarking algorithm does not change the region of interest of medical images thus it does not affect the judgment of doctors.Additionally,the security of the algorithm is enhanced by using chaotic mapping,which is sensitive to the initial value in order to encrypt the medical image and the watermark.The simulation results show that the pro-posed algorithm has good homomorphism,which can not only protect the original medical image and the watermark information,but can also embed and extract the watermark directly in the encrypted image,eliminating the potential risk of decrypting the embedded watermark and extracting watermark.Compared with the recent related research,the proposed algorithm solves the contradiction between robustness and invisibility of the watermarking algorithm for encrypted medical images,and it has good results against both conventional attacks and geometric attacks.Under geometric attacks in particular,the proposed algorithm performs much better than existing algorithms.展开更多
Purpose:Given the information overload of scientific literature,there is an increasing need for computable biomedical knowledge buried in free text.This study aimed to develop a novel approach to extracting and measur...Purpose:Given the information overload of scientific literature,there is an increasing need for computable biomedical knowledge buried in free text.This study aimed to develop a novel approach to extracting and measuring uncertain biomedical knowledge from scientific statements.Design/methodology/approach:Taking cardiovascular research publications in China as a sample,we extracted subject-predicate-object triples(SPO triples)as knowledge units and unknown/hedging/conflicting uncertainties as the knowledge context.We introduced information entropy(IE)as potential metric to quantify the uncertainty of epistemic status of scientific knowledge represented at subject-object pairs(SO pairs)levels.Findings:The results indicated an extraordinary growth of cardiovascular publications in China while only a modest growth of the novel SPO triples.After evaluating the uncertainty of biomedical knowledge with IE,we identified the Top 10 SO pairs with highest IE,which implied the epistemic status pluralism.Visual presentation of the SO pairs overlaid with uncertainty provided a comprehensive overview of clusters of biomedical knowledge and contending topics in cardiovascular research.Research limitations:The current methods didn’t distinguish the specificity and probabilities of uncertainty cue words.The number of sentences surrounding a given triple may also influence the value of IE.Practical implications:Our approach identified major uncertain knowledge areas such as diagnostic biomarkers,genetic polymorphism and co-existing risk factors related to cardiovascular diseases in China.These areas are suggested to be prioritized;new hypotheses need to be verified,while disputes,conflicts,and contradictions need to be settled.Originality/value:We provided a novel approach by combining natural language processing and computational linguistics with informetric methods to extract and measure uncertain knowledge from scientific statements.展开更多
The field of healthcare is considered to be the most promising application of intelligent sensor networks.However,the security and privacy protection ofmedical images collected by intelligent sensor networks is a hot ...The field of healthcare is considered to be the most promising application of intelligent sensor networks.However,the security and privacy protection ofmedical images collected by intelligent sensor networks is a hot problem that has attracted more and more attention.Fortunately,digital watermarking provides an effective method to solve this problem.In order to improve the robustness of the medical image watermarking scheme,in this paper,we propose a novel zero-watermarking algorithm with the integer wavelet transform(IWT),Schur decomposition and image block energy.Specifically,we first use IWT to extract low-frequency information and divide them into non-overlapping blocks,then we decompose the sub-blocks by Schur decomposition.After that,the feature matrix is constructed according to the relationship between the image block energy and the whole image energy.At the same time,we encrypt watermarking with the logistic chaotic position scrambling.Finally,the zero-watermarking is obtained by XOR operation with the encrypted watermarking.Three indexes of peak signal-to-noise ratio,normalization coefficient(NC)and the bit error rate(BER)are used to evaluate the robustness of the algorithm.According to the experimental results,most of the NC values are around 0.9 under various attacks,while the BER values are very close to 0.These experimental results show that the proposed algorithm is more robust than the existing zero-watermarking methods,which indicates it is more suitable for medical image privacy and security protection.展开更多
Body temperature is an important clinical indicator of health and illness. Many studies of human body temperature have been conducted;however, there are no studies that compare the body temperatures of humans and othe...Body temperature is an important clinical indicator of health and illness. Many studies of human body temperature have been conducted;however, there are no studies that compare the body temperatures of humans and other homeothermic animals. Twenty-six homeothermic animal species, including humans, were selected and characteristics of their internal environment were studied based on previous reports. The studied species were divided into two groups based on habitat (eight aquatic and eighteen terrestrial species) and three groups based on diet (carnivores, herbivores, and omnivores). Body temperatures, erythrocyte counts, and lymphocyte percentages were compared between species and between groups. Our results showed that carnivores had lower body temperatures and erythrocyte numbers than herbivores, and lower lymphocyte ratios than both herbivores and omnivores. Aquatic mammals that experienced a second adaptation event during their evolutionary process had lower body temperatures and lymphocyte ratios than terrestrial animals. These results suggest that excessive adaptation induced by stress or a change in environment may result in relative hypothermia and lymphocytopenia, features that aquatic mammals with stressful evolutionary backgrounds share with human cancer patients. However, our study is based on analysis of previous observations and reports, and further research (e.g., larger-scale studies) is needed to support this hypothesis.展开更多
Purpose:The aim of the study was to investigate the actual benefits of the electronic official document online submission and approval system and the satisfaction of hospital staff in a medical center in southern Taiw...Purpose:The aim of the study was to investigate the actual benefits of the electronic official document online submission and approval system and the satisfaction of hospital staff in a medical center in southern Taiwan,and to find out whether there are any differences between medical institutions and general government personnel.Methods:A cross-sectional study was conducted to investigate satisfactory outcome with questionnaires.The subjects were administrators,healthcare professionals and medical personnel of a medical center in the southern part of Taiwan who had signed electronic documents online.A total of 395 questionnaires were sent out,147 of which were valid,and the rate of collecting data survey was 37%.We analyzed with SPSS version 20.Results:The official document approval system was mainly used by administrative units and contractors,accounting for more than 50%of users.Besides,the frequency of use was at least more than once a week.As for the user’s perception of operating system,most people thought that it is easier to choose the format of official document and to set up the duty agent on leave,but in the part of the signing and approval process setting or modifying,it was considered more difficult,accounting for 38.1%.In terms of perceptual usefulness,the average value was 3.81,which showed that the user agreed that the system has met the needs of daily official documents.When some users of service area encountered problems with their use,the clerical staff were able to provide services immediately and have the professional ability to resolve problems,in order to agree to the majority,accounting for 53.7%.In addition,nearly 60%of users rated the official document system positively,with an average of 3.84 satisfaction,which was higher than the certified value of 3,conforming to the standards for satisfaction with the use of official documents.In addition,the role authorization,perceptual easy-to-use and service area were significant(p<0.05).The perceptual usefulness of Subordinate units was also significant(p=0.016).The frequency of use,perceptual easy-to-use,perceptual usefulness,service area and satisfaction were significantly different(p<0.05).The correlation coefficient between perceptual usefulness and user satisfaction was 0.833,indicating that there was a high correlation.The daily usage frequency of contractors was higher than supervisors.However,supervisors had the highest frequency of use every quarter(p=0.135).There was no significant difference between contractors and supervisors in the frequency of use.Conclusion:It is suggested that education and training on the operation of the electronic official document on-line submission and approval system should be conducted,which can enhance the education and training of supervisors and medical personnel.Continually,invite supervisors and medical personnel to provide advices on the official document system as a reference for future improvements of the system.展开更多
This study aimed to evaluate the correlation between nursing informatics(NI)competency and information literacy skills for evidencebased practice(EBP)among intensive care nurses.This cross-sectional study was conducte...This study aimed to evaluate the correlation between nursing informatics(NI)competency and information literacy skills for evidencebased practice(EBP)among intensive care nurses.This cross-sectional study was conducted on 184 nurses working in intensive care units(ICUs).The study data were collected through demographic information,Nursing Informatics Competency Assessment Tool(NICAT),and information literacy skills for EBP questionnaires.The intensive care nurses received competent and low-moderate levels for the total scores of NI competency and information literacy skills,respectively.They received a moderate score for the use of different information resources but a low score for information searching skills,different search features,and knowledge about search operators,and only 31.5%of the nurses selected the most appropriate statement.NI competency and related subscales had a significant direct bidirectional correlation with information literacy skills for EBP and its subscales(P<0.05).Nurses require a high level of NI competency and information literacy for EBP to obtain up-to-date information and provide better care and decision-making.Health planners and policymakers should develop interventions to enhance NI competency and information literacy skills among nurses and motivate them to use EBP in clinical settings.展开更多
Artificial intelligence(AI)is defined as the digital computer or computer-controlled robot's ability to mimic intelligent conduct and crucial thinking commonly associated with intelligent beings.The application of...Artificial intelligence(AI)is defined as the digital computer or computer-controlled robot's ability to mimic intelligent conduct and crucial thinking commonly associated with intelligent beings.The application of AI technology and machine learning in medicine have allowed medical practitioners to provide patients with better quality of services;and current advancements have led to a dramatic change in the healthcare system.However,many efficient applications are still in their initial stages,which need further evaluations to improve and develop these applications.Clinicians must recognize and acclimate themselves with the developments in AI technology to improve their delivery of healthcare services;but for this to be possible,a significant revision of medical education is needed to provide future leaders with the required competencies.This article reviews the potential and limitations of AI in healthcare,as well as the current medical application trends including healthcare administration,clinical decision assistance,patient health monitoring,healthcare resource allocation,medical research,and public health policy development.Also,future possibilities for further clinical and scientific practice were also summarized.展开更多
Patients with leukemia often suffer from the combined effects of cancer-related fatigue(CRF)and subthreshold depression,which mutually exacerbate each other in a vicious cycle.In this editorial,we comment on the artic...Patients with leukemia often suffer from the combined effects of cancer-related fatigue(CRF)and subthreshold depression,which mutually exacerbate each other in a vicious cycle.In this editorial,we comment on the article by Liu et al,published in the World Journal of Psychiatry.We further elucidate the profound impact of subthreshold depressive symptoms on the experience of CRF and complications in patients with leukemia.This editorial highlights the importance of early identification and treatment of subclinical depression,and advocates for a multidisciplinary and integrated treatment approach that includes social support,psychological interventions,and individualized treatment plans.Future research needs to explore the biological mechanisms underlying the interaction between the two to develop more effective prevention and treatment strategies.展开更多
BACKGROUND Acute appendicitis(AAp)is a prevalent medical condition characterized by inflammation of the appendix that frequently necessitates urgent surgical procedures.Approximately two-thirds of patients with AAp ex...BACKGROUND Acute appendicitis(AAp)is a prevalent medical condition characterized by inflammation of the appendix that frequently necessitates urgent surgical procedures.Approximately two-thirds of patients with AAp exhibit characteristic signs and symptoms;hence,negative AAp and complicated AAp are the primary concerns in research on AAp.In other terms,further investigations and algorithms are required for at least one third of patients to predict the clinical condition and distinguish them from uncomplicated patients with AAp.AIM To use a Stochastic Gradient Boosting(SGB)-based machine learning(ML)algorithm to tell the difference between AAp patients who are complicated and those who are not,and to find some important biomarkers for both types of AAp by using modeling to get variable importance values.METHODS This study analyzed an open access data set containing 140 people,including 41 healthy controls,65 individuals with uncomplicated AAp,and 34 individuals with complicated AAp.We analyzed some demographic data(age,sex)of the patients and the following biochemical blood parameters:White blood cell(WBC)count,neutrophils,lymphocytes,monocytes,platelet count,neutrophil-tolymphocyte ratio,lymphocyte-to-monocyte ratio,mean platelet volume,neutrophil-to-immature granulocyte ratio,ferritin,total bilirubin,immature granulocyte count,immature granulocyte percent,and neutrophil-to-immature granulocyte ratio.We tested the SGB model using n-fold cross-validation.It was implemented with an 80-20 training-test split.We used variable importance values to identify the variables that were most effective on the target.RESULTS The SGB model demonstrated excellent performance in distinguishing AAp from control patients with an accuracy of 96.3%,a micro aera under the curve(AUC)of 94.7%,a sensitivity of 94.7%,and a specificity of 100%.In distinguishing complicated AAp patients from uncomplicated ones,the model achieved an accuracy of 78.9%,a micro AUC of 79%,a sensitivity of 83.3%,and a specificity of 76.9%.The most useful biomarkers for confirming the AA diagnosis were WBC(100%),neutrophils(95.14%),and the lymphocyte-monocyte ratio(76.05%).On the other hand,the most useful biomarkers for accurate diagnosis of complicated AAp were total bilirubin(100%),WBC(96.90%),and the neutrophil-immature granulocytes ratio(64.05%).CONCLUSION The SGB model achieved high accuracy rates in identifying AAp patients while it showed moderate performance in distinguishing complicated AAp patients from uncomplicated AAp patients.Although the model's accuracy in the classification of complicated AAp is moderate,the high variable importance obtained is clinically significant.We need further prospective validation studies,but the integration of such ML algorithms into clinical practice may improve diagnostic processes.展开更多
Acute appendicitis(AAp)remains one of the most common abdominal emergencies,requiring rapid and accurate diagnosis to prevent complications and unnecessary surgeries.Conventional diagnostic methods,including medical h...Acute appendicitis(AAp)remains one of the most common abdominal emergencies,requiring rapid and accurate diagnosis to prevent complications and unnecessary surgeries.Conventional diagnostic methods,including medical history,clinical assessment,biochemical markers,and imaging techniques,often present limitations in sensitivity and specificity,especially in atypical cases.In recent years,artificial intelligence(AI)has demonstrated remarkable potential in enhancing diagnostic accuracy through machine learning(ML)and deep learning(DL)models.This review evaluates the current applications of AI in both adult and pediatric AAp,focusing on clinical data-based models,radiological imaging analysis,and AI-assisted clinical decision support systems.ML models such as random forest,support vector machines,logistic regression,and extreme gradient boosting have exhibited superior diagnostic performance compared to traditional scoring systems,achieving sensitivity and specificity rates exceeding 90%in multiple studies.Additionally,DL techniques,particularly convolutional neural networks,have been shown to outperform radiologists in interpreting ultrasound and computed tomography images,enhancing diagnostic confidence.This review synthesized findings from 65 studies,demonstrating that AI models integrating multimodal data including clinical,laboratory,and imaging parameters further improved diagnostic precision.Moreover,explainable AI approaches,such as SHapley Additive exPlanations and local interpretable model-agnostic explanations,have facilitated model transparency,fostering clinician trust in AI-driven decision-making.This review highlights the advancements in AI for AAp diagnosis,emphasizing that AI is used not only to establish the diagnosis of AAp but also to differentiate complicated from uncomplicated cases.While preliminary results are promising,further prospective,multicenter studies are required for large-scale clinical implementation,given that a great proportion of current evidence derives from retrospective designs,and existing prospective cohorts exhibit limited sample sizes or protocol variability.Future research should also focus on integrating AI-driven decision support tools into routine emergency care workflows.展开更多
Persistent left superior vena cava(PLSVC)is a congenital anomaly where the left-sided vena cava,which usually regresses during fetal development,persists.Double superior vena cava resulting from a PLSVC is indeed a ra...Persistent left superior vena cava(PLSVC)is a congenital anomaly where the left-sided vena cava,which usually regresses during fetal development,persists.Double superior vena cava resulting from a PLSVC is indeed a rare phenomenon.In the general population,the incidence of this condition is reported to be between 0.3%and 2.1%.[1]While this anatomical variation is often asymptomatic and discovered incidentally,it becomes relevant in certain clinical scenarios.Indeed,the presence of a PLSVC and double superior vena cava can pose challenges as incorrect positioning and result in failure.展开更多
Hepatocellular carcinoma(HCC)remains a leading cause of cancer-related mortality globally,necessitating advanced diagnostic tools to improve early detection and personalized targeted therapy.This review synthesizes ev...Hepatocellular carcinoma(HCC)remains a leading cause of cancer-related mortality globally,necessitating advanced diagnostic tools to improve early detection and personalized targeted therapy.This review synthesizes evidence on explainable ensemble learning approaches for HCC classification,emphasizing their integration with clinical workflows and multi-omics data.A systematic analysis[including datasets such as The Cancer Genome Atlas,Gene Expression Omnibus,and the Surveillance,Epidemiology,and End Results(SEER)datasets]revealed that explainable ensemble learning models achieve high diagnostic accuracy by combining clinical features,serum biomarkers such as alpha-fetoprotein,imaging features such as computed tomography and magnetic resonance imaging,and genomic data.For instance,SHapley Additive exPlanations(SHAP)-based random forests trained on NCBI GSE14520 microarray data(n=445)achieved 96.53%accuracy,while stacking ensembles applied to the SEER program data(n=1897)demonstrated an area under the receiver operating characteristic curve of 0.779 for mortality prediction.Despite promising results,challenges persist,including the computational costs of SHAP and local interpretable model-agnostic explanations analyses(e.g.,TreeSHAP requiring distributed computing for metabolomics datasets)and dataset biases(e.g.,SEER’s Western population dominance limiting generalizability).Future research must address inter-cohort heterogeneity,standardize explainability metrics,and prioritize lightweight surrogate models for resource-limited settings.This review presents the potential of explainable ensemble learning frameworks to bridge the gap between predictive accuracy and clinical interpretability,though rigorous validation in independent,multi-center cohorts is critical for real-world deployment.展开更多
BACKGROUND Endometriosis is a clinical condition characterized by the presence of endometrial glands outside the uterine cavity.While its incidence remains mostly uncertain,endometriosis impacts around 180 million wom...BACKGROUND Endometriosis is a clinical condition characterized by the presence of endometrial glands outside the uterine cavity.While its incidence remains mostly uncertain,endometriosis impacts around 180 million women worldwide.Despite the presentation of several epidemiological and clinical explanations,the precise mechanism underlying the disease remains ambiguous.In recent years,researchers have examined the hereditary dimension of the disease.Genetic research has aimed to discover the gene or genes responsible for the disease through association or linkage studies involving candidate genes or DNA mapping techniques.AIM To identify genetic biomarkers linked to endometriosis by the application of machine learning(ML)approaches.METHODS This case-control study accounted for the open-access transcriptomic data set of endometriosis and the control group.We included data from 22 controls and 16 endometriosis patients for this purpose.We used AdaBoost,XGBoost,Stochasting Gradient Boosting,Bagged Classification and Regression Trees(CART)for classification using five-fold cross validation.We evaluated the performance of the models using the performance measures of accuracy,balanced accuracy,sensitivity,specificity,positive predictive value,negative predictive value and F1 score.RESULTS Bagged CART gave the best classification metrics.The metrics obtained from this model are 85.7%,85.7%,100%,75%,75%,100%and 85.7%for accuracy,balanced accuracy,sensitivity,specificity,positive predictive value,negative predictive value and F1 score,respectively.Based on the variable importance of modeling,we can use the genes CUX2,CLMP,CEP131,EHD4,CDH24,ILRUN,LINC01709,HOTAIR,SLC30A2 and NKG7 and other transcripts with inaccessible gene names as potential biomarkers for endometriosis.CONCLUSION This study determined possible genomic biomarkers for endometriosis using transcriptomic data from patients with/without endometriosis.The applied ML model successfully classified endometriosis and created a highly accurate diagnostic prediction model.Future genomic studies could explain the underlying pathology of endometriosis,and a non-invasive diagnostic method could replace the invasive ones.展开更多
Combined hepatocellular-cholangiocarcinoma(cHCC-CCA)is a rare hetero-geneous primary malignant liver tumor containing both hepatocellular and cholangiocarcinoma features.The complex presentation of cHCC-CCA tends to b...Combined hepatocellular-cholangiocarcinoma(cHCC-CCA)is a rare hetero-geneous primary malignant liver tumor containing both hepatocellular and cholangiocarcinoma features.The complex presentation of cHCC-CCA tends to be poorly investigated,and the information derived from traditional diagnostic techniques(histopathology and radiological imaging)is often not optimal.Since cHCC-CCA is usually difficult to diagnose due to complex histopathological features(edge learning)as excessive photos,hence,achieves treatment delays and poor prognosis,the incorporation of advanced artificial intelligence like edge learning is able to improve the patient’s outcome.Using artificial intelligence,particularly deep learning,has recently opened new doorways for the impro-vement of diagnostic accuracy.If artificial intelligence models are deployed on local devices,edge learning exercises this type of learning,which provides real time processing,improved data privacy and reduced bandwidth usage.This narrative review investigates the conceptual formulation of edge learning together with its opportunities for clinical applications in the prediction and classification of cHCC-CCA,the technical solution strategies,the clinical benefits it offers,and associated challenges and future directions.展开更多
The authors employed inappropriate search keywords and strategies in their published bibliometric papers within volume 29 of the World Journal of Gastroenterology.The comment highlights the identified issues,provides ...The authors employed inappropriate search keywords and strategies in their published bibliometric papers within volume 29 of the World Journal of Gastroenterology.The comment highlights the identified issues,provides evidence,and suggests improved study methodologies.Subsequent results with more appro-priate search strategies were presented to address the shortcomings.展开更多
Head injuries from vehicle collisions,falls,and sports are often the result of complex mechanisms involving both linear and angular forces.This study aims to quantitatively assess the effects of linear and angular for...Head injuries from vehicle collisions,falls,and sports are often the result of complex mechanisms involving both linear and angular forces.This study aims to quantitatively assess the effects of linear and angular force on the severity of traumatic brain injury in rats during collisions.An orthogonal experimental design was employed,facilitating the manipulation of linear velocity,rotational acceleration,and angle(light,medium,and heavy)across 54 rats.24 hours post-injury,magnetic resonance imaging T2-weighted imaging,and diffusion tensor imaging were utilized to detect abnormal brain signals,with the fractional anisotropy value of the corpus callosum serv-ing as the primary injury indicator.Anatomical analyses and immunohistological staining were conducted to measure the amyloid precursor protein(β-APP)accumulation,using integrated optical density as a secondary indicator.Entropy weighting was applied to derive index weights for the injury scoring system.Through analysis guided by analysis of variance and linear regression,it was determined that both linear and angular loadings significantly impacted brain injury severity.Increased rotational acceleration at constant linear velocities correlated with more severe injuries,whereas the rotation angle exhibited minimal effect.Linear velocity emerged as the primary determinant of injury severity,accounting for 91.5%of the variance,while rotational acceleration and rotation angle contributed 6.5%and 0.9%,respectively.These findings offer critical insights for developing protective measures against brain injuries in traffic accidents.展开更多
It is often challenging to diagnose acute myocardial infarction(AMI)in patients with elevated high-sensitivity cardiac troponin T(hs-cTnT)before observing a significant rise and/or fall in hs-cTnT.The current study ai...It is often challenging to diagnose acute myocardial infarction(AMI)in patients with elevated high-sensitivity cardiac troponin T(hs-cTnT)before observing a significant rise and/or fall in hs-cTnT.The current study aimed to identify an optimal cut-off to rule in AMI.A total of 76411 patients with elevated hs-cTnT were included.The predictive cut-off values for diagnosing ST-segment elevation myocardial infarction(STEMI)and non-STsegment elevation myocardial infarction(NSTEMI)were assessed using the area under the receiver operating characteristic curve(AUC).Among the patients,50466(66.0%)had non-cardiac diseases,25945(34.0%)had cardiac diseases,and 15502(20.3%)had AMI,including 816(1.1%)with STEMI and 14686(19.2%)with NSTEMI.The median hs-cTnT level was 3788.0 ng/L in STEMI patients and 67.2 ng/L in NSTEMI patients.The optimal cut-off for diagnosing STEMI was 251.9 ng/L,with a sensitivity of 90.7%,specificity of 86.5%,and an AUC of 0.942;the optimal cut-off for diagnosing NSTEMI was 130.5 ng/L,with a sensitivity of 40.9%,specificity of 83.8%,and an AUC of 0.638.Collectively,optimizing the cut-off values for diagnosing STEMI and NSTEMI to 251.9 ng/L and 130.5 ng/L,respectively,demonstrated high accuracy in a large cohort of Chinese patients with elevated hs-cTnT.展开更多
Objectives:This study aimed to develop and validate a stroke risk prediction model based on machine learning(ML)and regional healthcare big data,and determine whether it may improve the prediction performance compared...Objectives:This study aimed to develop and validate a stroke risk prediction model based on machine learning(ML)and regional healthcare big data,and determine whether it may improve the prediction performance compared with the conventional Logistic Regression(LR)model.Methods:This retrospective cohort study analyzed data from the CHinese Electronic health Records Research in Yinzhou(CHERRY)(2015–2021).We included adults aged 18–75 from the platform who had established records before 2015.Individuals with pre-existing stroke,key data absence,or excessive missingness(>30%)were excluded.Data on demographic,clinical measures,lifestyle factors,comorbidities,and family history of stroke were collected.Variable selection was performed in two stages:an initial screening via univariate analysis,followed by a prioritization of variables based on clinical relevance and actionability,with a focus on those that are modifiable.Stroke prediction models were developed using LR and four ML algorithms:Decision Tree(DT),Random Forest(RF),eXtreme Gradient Boosting(XGBoost),and Back Propagation Neural Network(BPNN).The dataset was split 7:3 for training and validation sets.Performance was assessed using receiver operating characteristic(ROC)curves,calibration,and confusion matrices,and the cutoff value was determined by Youden's index to classify risk groups.Results:The study cohort comprised 92,172 participants with 436 incident stroke cases(incidence rate:474/100,000 person-years).Ultimately,13 predictor variables were included.RF achieved the highest accuracy(0.935),precision(0.923),sensitivity(recall:0.947),and F1 score(0.935).Model evaluation demonstrated superior predictive performance of ML algorithms over conventional LR,with training/validation areaunderthe curve(AUC)sof0.777/0.779(LR),0.921/0.918(BPNN),0.988/0.980(RF),0.980/0.955(DT),and 0.962/0.958(XGBoost).Calibration analysis revealed a better fit for DT,LR and BPNN compared to RF and XGBoost model.Based on the optimal performance of the RF model,the ranking of factors in descending order of importance was:hypertension,age,diabetes,systolic blood pressure,waist,high-density lipoprotein Cholesterol,fasting blood glucose,physical activity,BMI,low-density lipoprotein cholesterol,total cholesterol,dietary habits,and family history of stroke.Using Youden's index as the optimal cutoff,the RF model stratified individuals into high-risk(>0.789)and low-risk(≤0.789)groups with robust discrimination.Conclusions:The ML-based prediction models demonstrated superior performance metrics compared to conventional LR and the RF is the optimal prediction model,providing an effective tool for risk stratifi cation in primary stroke prevention in community settings.展开更多
BACKGROUND Gastric cancer is a major global health concern,often diagnosed at advanced stages,leading to poor prognosis.Proximal and distal gastric cancers exhibit distinct clinicopathological features.AIM To investig...BACKGROUND Gastric cancer is a major global health concern,often diagnosed at advanced stages,leading to poor prognosis.Proximal and distal gastric cancers exhibit distinct clinicopathological features.AIM To investigate the diagnostic value of hematological and inflammatory markers in differentiating proximal and distal gastric cancers and to evaluate their association with clinical outcomes.METHODS A retrospective cohort study was conducted on 150 patients diagnosed with gastric adenocarcinoma through histopathological analysis.Patients were categorized into proximal gastric cancer and distal gastric cancer groups.Laboratory parameters were analyzed.RESULTS Of the 150 patients,84 had proximal gastric cancer and 66 had distal gastric cancer.Dysphagia was significantly more common in the proximal gastric cancer group,while anemia and higher platelet-to-lymphocyte ratio values were observed in the distal gastric cancer group(P=0.031).Tumor stage and neutrophil-to-lymphocyte ratio emerged as independent predictors of all-cause mortality.No significant differences were found in other laboratory or biochemical parameters between the groups.CONCLUSION Proximal and distal gastric cancers demonstrate distinct clinical and laboratory profiles.The platelet-to-lymphocyte ratio may serve as a valuable marker in differentiating cancer localization,while the neutrophil-to-lymphocyte ratio is a prognostic indicator for mortality.These findings highlight the potential of hematological markers in optimizing diagnosis and treatment strategies for gastric cancer.展开更多
基金supported by the Key Research Project of Hainan Province[ZDYF2018129]the Higher Education Research Project of Hainan Province(Hnky2019-73)+3 种基金the National Natural Science Foundation of China[61762033]the Natural Science Foundation of Hainan[617175]the Special Scientific Research Project of Philosophy and Social Sciences of Chongqing Medical University[201703]the Key Research Project of Haikou College of Economics[HJKZ18-01].
文摘In order to solve the problem of patient information security protection in medical images,whilst also taking into consideration the unchangeable particularity of medical images to the lesion area and the need for medical images themselves to be protected,a novel robust watermarking algorithm for encrypted medical images based on dual-tree complex wavelet transform and discrete cosine transform(DTCWT-DCT)and chaotic map is proposed in this paper.First,DTCWT-DCT transformation was performed on medical images,and dot product was per-formed in relation to the transformation matrix and logistic map.Inverse transformation was undertaken to obtain encrypted medical images.Then,in the low-frequency part of the DTCWT-DCT transformation coefficient of the encrypted medical image,a set of 32 bits visual feature vectors that can effectively resist geometric attacks are found to be the feature vector of the encrypted medical image by using perceptual hashing.After that,different logistic initial values and growth parameters were set to encrypt the watermark,and zero-watermark technology was used to embed and extract the encrypted medical images by combining cryptography and third-party concepts.The proposed watermarking algorithm does not change the region of interest of medical images thus it does not affect the judgment of doctors.Additionally,the security of the algorithm is enhanced by using chaotic mapping,which is sensitive to the initial value in order to encrypt the medical image and the watermark.The simulation results show that the pro-posed algorithm has good homomorphism,which can not only protect the original medical image and the watermark information,but can also embed and extract the watermark directly in the encrypted image,eliminating the potential risk of decrypting the embedded watermark and extracting watermark.Compared with the recent related research,the proposed algorithm solves the contradiction between robustness and invisibility of the watermarking algorithm for encrypted medical images,and it has good results against both conventional attacks and geometric attacks.Under geometric attacks in particular,the proposed algorithm performs much better than existing algorithms.
基金funded by the National Natural Science Foundation of China(nos.71603280,72074006,and 82070235)the Beijing Municipal Natural Science Foundation(7191013)+1 种基金Research Unit of Medical Science Research Management/Basic and Clinical Research of Metabolic Cardiovascular Diseases,Chinese Academy of Medical Sciences(2021RU003)Peking University Health Science Center and the Young Elite Scientists Sponsorship Program by China Association for Science and Technology(2017QNRC001).
文摘Purpose:Given the information overload of scientific literature,there is an increasing need for computable biomedical knowledge buried in free text.This study aimed to develop a novel approach to extracting and measuring uncertain biomedical knowledge from scientific statements.Design/methodology/approach:Taking cardiovascular research publications in China as a sample,we extracted subject-predicate-object triples(SPO triples)as knowledge units and unknown/hedging/conflicting uncertainties as the knowledge context.We introduced information entropy(IE)as potential metric to quantify the uncertainty of epistemic status of scientific knowledge represented at subject-object pairs(SO pairs)levels.Findings:The results indicated an extraordinary growth of cardiovascular publications in China while only a modest growth of the novel SPO triples.After evaluating the uncertainty of biomedical knowledge with IE,we identified the Top 10 SO pairs with highest IE,which implied the epistemic status pluralism.Visual presentation of the SO pairs overlaid with uncertainty provided a comprehensive overview of clusters of biomedical knowledge and contending topics in cardiovascular research.Research limitations:The current methods didn’t distinguish the specificity and probabilities of uncertainty cue words.The number of sentences surrounding a given triple may also influence the value of IE.Practical implications:Our approach identified major uncertain knowledge areas such as diagnostic biomarkers,genetic polymorphism and co-existing risk factors related to cardiovascular diseases in China.These areas are suggested to be prioritized;new hypotheses need to be verified,while disputes,conflicts,and contradictions need to be settled.Originality/value:We provided a novel approach by combining natural language processing and computational linguistics with informetric methods to extract and measure uncertain knowledge from scientific statements.
基金supported in part by the Hainan Provincial Natural Science Foundation of China (No.620MS067)the Intelligent Medical Project of Chongqing Medical University (ZHYXQNRC202101)the Student Scientific Research and Innovation Experiment Project of the Medical Information College of Chongqing Medical University (No.2020C006).
文摘The field of healthcare is considered to be the most promising application of intelligent sensor networks.However,the security and privacy protection ofmedical images collected by intelligent sensor networks is a hot problem that has attracted more and more attention.Fortunately,digital watermarking provides an effective method to solve this problem.In order to improve the robustness of the medical image watermarking scheme,in this paper,we propose a novel zero-watermarking algorithm with the integer wavelet transform(IWT),Schur decomposition and image block energy.Specifically,we first use IWT to extract low-frequency information and divide them into non-overlapping blocks,then we decompose the sub-blocks by Schur decomposition.After that,the feature matrix is constructed according to the relationship between the image block energy and the whole image energy.At the same time,we encrypt watermarking with the logistic chaotic position scrambling.Finally,the zero-watermarking is obtained by XOR operation with the encrypted watermarking.Three indexes of peak signal-to-noise ratio,normalization coefficient(NC)and the bit error rate(BER)are used to evaluate the robustness of the algorithm.According to the experimental results,most of the NC values are around 0.9 under various attacks,while the BER values are very close to 0.These experimental results show that the proposed algorithm is more robust than the existing zero-watermarking methods,which indicates it is more suitable for medical image privacy and security protection.
文摘Body temperature is an important clinical indicator of health and illness. Many studies of human body temperature have been conducted;however, there are no studies that compare the body temperatures of humans and other homeothermic animals. Twenty-six homeothermic animal species, including humans, were selected and characteristics of their internal environment were studied based on previous reports. The studied species were divided into two groups based on habitat (eight aquatic and eighteen terrestrial species) and three groups based on diet (carnivores, herbivores, and omnivores). Body temperatures, erythrocyte counts, and lymphocyte percentages were compared between species and between groups. Our results showed that carnivores had lower body temperatures and erythrocyte numbers than herbivores, and lower lymphocyte ratios than both herbivores and omnivores. Aquatic mammals that experienced a second adaptation event during their evolutionary process had lower body temperatures and lymphocyte ratios than terrestrial animals. These results suggest that excessive adaptation induced by stress or a change in environment may result in relative hypothermia and lymphocytopenia, features that aquatic mammals with stressful evolutionary backgrounds share with human cancer patients. However, our study is based on analysis of previous observations and reports, and further research (e.g., larger-scale studies) is needed to support this hypothesis.
文摘Purpose:The aim of the study was to investigate the actual benefits of the electronic official document online submission and approval system and the satisfaction of hospital staff in a medical center in southern Taiwan,and to find out whether there are any differences between medical institutions and general government personnel.Methods:A cross-sectional study was conducted to investigate satisfactory outcome with questionnaires.The subjects were administrators,healthcare professionals and medical personnel of a medical center in the southern part of Taiwan who had signed electronic documents online.A total of 395 questionnaires were sent out,147 of which were valid,and the rate of collecting data survey was 37%.We analyzed with SPSS version 20.Results:The official document approval system was mainly used by administrative units and contractors,accounting for more than 50%of users.Besides,the frequency of use was at least more than once a week.As for the user’s perception of operating system,most people thought that it is easier to choose the format of official document and to set up the duty agent on leave,but in the part of the signing and approval process setting or modifying,it was considered more difficult,accounting for 38.1%.In terms of perceptual usefulness,the average value was 3.81,which showed that the user agreed that the system has met the needs of daily official documents.When some users of service area encountered problems with their use,the clerical staff were able to provide services immediately and have the professional ability to resolve problems,in order to agree to the majority,accounting for 53.7%.In addition,nearly 60%of users rated the official document system positively,with an average of 3.84 satisfaction,which was higher than the certified value of 3,conforming to the standards for satisfaction with the use of official documents.In addition,the role authorization,perceptual easy-to-use and service area were significant(p<0.05).The perceptual usefulness of Subordinate units was also significant(p=0.016).The frequency of use,perceptual easy-to-use,perceptual usefulness,service area and satisfaction were significantly different(p<0.05).The correlation coefficient between perceptual usefulness and user satisfaction was 0.833,indicating that there was a high correlation.The daily usage frequency of contractors was higher than supervisors.However,supervisors had the highest frequency of use every quarter(p=0.135).There was no significant difference between contractors and supervisors in the frequency of use.Conclusion:It is suggested that education and training on the operation of the electronic official document on-line submission and approval system should be conducted,which can enhance the education and training of supervisors and medical personnel.Continually,invite supervisors and medical personnel to provide advices on the official document system as a reference for future improvements of the system.
文摘This study aimed to evaluate the correlation between nursing informatics(NI)competency and information literacy skills for evidencebased practice(EBP)among intensive care nurses.This cross-sectional study was conducted on 184 nurses working in intensive care units(ICUs).The study data were collected through demographic information,Nursing Informatics Competency Assessment Tool(NICAT),and information literacy skills for EBP questionnaires.The intensive care nurses received competent and low-moderate levels for the total scores of NI competency and information literacy skills,respectively.They received a moderate score for the use of different information resources but a low score for information searching skills,different search features,and knowledge about search operators,and only 31.5%of the nurses selected the most appropriate statement.NI competency and related subscales had a significant direct bidirectional correlation with information literacy skills for EBP and its subscales(P<0.05).Nurses require a high level of NI competency and information literacy for EBP to obtain up-to-date information and provide better care and decision-making.Health planners and policymakers should develop interventions to enhance NI competency and information literacy skills among nurses and motivate them to use EBP in clinical settings.
文摘Artificial intelligence(AI)is defined as the digital computer or computer-controlled robot's ability to mimic intelligent conduct and crucial thinking commonly associated with intelligent beings.The application of AI technology and machine learning in medicine have allowed medical practitioners to provide patients with better quality of services;and current advancements have led to a dramatic change in the healthcare system.However,many efficient applications are still in their initial stages,which need further evaluations to improve and develop these applications.Clinicians must recognize and acclimate themselves with the developments in AI technology to improve their delivery of healthcare services;but for this to be possible,a significant revision of medical education is needed to provide future leaders with the required competencies.This article reviews the potential and limitations of AI in healthcare,as well as the current medical application trends including healthcare administration,clinical decision assistance,patient health monitoring,healthcare resource allocation,medical research,and public health policy development.Also,future possibilities for further clinical and scientific practice were also summarized.
文摘Patients with leukemia often suffer from the combined effects of cancer-related fatigue(CRF)and subthreshold depression,which mutually exacerbate each other in a vicious cycle.In this editorial,we comment on the article by Liu et al,published in the World Journal of Psychiatry.We further elucidate the profound impact of subthreshold depressive symptoms on the experience of CRF and complications in patients with leukemia.This editorial highlights the importance of early identification and treatment of subclinical depression,and advocates for a multidisciplinary and integrated treatment approach that includes social support,psychological interventions,and individualized treatment plans.Future research needs to explore the biological mechanisms underlying the interaction between the two to develop more effective prevention and treatment strategies.
文摘BACKGROUND Acute appendicitis(AAp)is a prevalent medical condition characterized by inflammation of the appendix that frequently necessitates urgent surgical procedures.Approximately two-thirds of patients with AAp exhibit characteristic signs and symptoms;hence,negative AAp and complicated AAp are the primary concerns in research on AAp.In other terms,further investigations and algorithms are required for at least one third of patients to predict the clinical condition and distinguish them from uncomplicated patients with AAp.AIM To use a Stochastic Gradient Boosting(SGB)-based machine learning(ML)algorithm to tell the difference between AAp patients who are complicated and those who are not,and to find some important biomarkers for both types of AAp by using modeling to get variable importance values.METHODS This study analyzed an open access data set containing 140 people,including 41 healthy controls,65 individuals with uncomplicated AAp,and 34 individuals with complicated AAp.We analyzed some demographic data(age,sex)of the patients and the following biochemical blood parameters:White blood cell(WBC)count,neutrophils,lymphocytes,monocytes,platelet count,neutrophil-tolymphocyte ratio,lymphocyte-to-monocyte ratio,mean platelet volume,neutrophil-to-immature granulocyte ratio,ferritin,total bilirubin,immature granulocyte count,immature granulocyte percent,and neutrophil-to-immature granulocyte ratio.We tested the SGB model using n-fold cross-validation.It was implemented with an 80-20 training-test split.We used variable importance values to identify the variables that were most effective on the target.RESULTS The SGB model demonstrated excellent performance in distinguishing AAp from control patients with an accuracy of 96.3%,a micro aera under the curve(AUC)of 94.7%,a sensitivity of 94.7%,and a specificity of 100%.In distinguishing complicated AAp patients from uncomplicated ones,the model achieved an accuracy of 78.9%,a micro AUC of 79%,a sensitivity of 83.3%,and a specificity of 76.9%.The most useful biomarkers for confirming the AA diagnosis were WBC(100%),neutrophils(95.14%),and the lymphocyte-monocyte ratio(76.05%).On the other hand,the most useful biomarkers for accurate diagnosis of complicated AAp were total bilirubin(100%),WBC(96.90%),and the neutrophil-immature granulocytes ratio(64.05%).CONCLUSION The SGB model achieved high accuracy rates in identifying AAp patients while it showed moderate performance in distinguishing complicated AAp patients from uncomplicated AAp patients.Although the model's accuracy in the classification of complicated AAp is moderate,the high variable importance obtained is clinically significant.We need further prospective validation studies,but the integration of such ML algorithms into clinical practice may improve diagnostic processes.
文摘Acute appendicitis(AAp)remains one of the most common abdominal emergencies,requiring rapid and accurate diagnosis to prevent complications and unnecessary surgeries.Conventional diagnostic methods,including medical history,clinical assessment,biochemical markers,and imaging techniques,often present limitations in sensitivity and specificity,especially in atypical cases.In recent years,artificial intelligence(AI)has demonstrated remarkable potential in enhancing diagnostic accuracy through machine learning(ML)and deep learning(DL)models.This review evaluates the current applications of AI in both adult and pediatric AAp,focusing on clinical data-based models,radiological imaging analysis,and AI-assisted clinical decision support systems.ML models such as random forest,support vector machines,logistic regression,and extreme gradient boosting have exhibited superior diagnostic performance compared to traditional scoring systems,achieving sensitivity and specificity rates exceeding 90%in multiple studies.Additionally,DL techniques,particularly convolutional neural networks,have been shown to outperform radiologists in interpreting ultrasound and computed tomography images,enhancing diagnostic confidence.This review synthesized findings from 65 studies,demonstrating that AI models integrating multimodal data including clinical,laboratory,and imaging parameters further improved diagnostic precision.Moreover,explainable AI approaches,such as SHapley Additive exPlanations and local interpretable model-agnostic explanations,have facilitated model transparency,fostering clinician trust in AI-driven decision-making.This review highlights the advancements in AI for AAp diagnosis,emphasizing that AI is used not only to establish the diagnosis of AAp but also to differentiate complicated from uncomplicated cases.While preliminary results are promising,further prospective,multicenter studies are required for large-scale clinical implementation,given that a great proportion of current evidence derives from retrospective designs,and existing prospective cohorts exhibit limited sample sizes or protocol variability.Future research should also focus on integrating AI-driven decision support tools into routine emergency care workflows.
文摘Persistent left superior vena cava(PLSVC)is a congenital anomaly where the left-sided vena cava,which usually regresses during fetal development,persists.Double superior vena cava resulting from a PLSVC is indeed a rare phenomenon.In the general population,the incidence of this condition is reported to be between 0.3%and 2.1%.[1]While this anatomical variation is often asymptomatic and discovered incidentally,it becomes relevant in certain clinical scenarios.Indeed,the presence of a PLSVC and double superior vena cava can pose challenges as incorrect positioning and result in failure.
文摘Hepatocellular carcinoma(HCC)remains a leading cause of cancer-related mortality globally,necessitating advanced diagnostic tools to improve early detection and personalized targeted therapy.This review synthesizes evidence on explainable ensemble learning approaches for HCC classification,emphasizing their integration with clinical workflows and multi-omics data.A systematic analysis[including datasets such as The Cancer Genome Atlas,Gene Expression Omnibus,and the Surveillance,Epidemiology,and End Results(SEER)datasets]revealed that explainable ensemble learning models achieve high diagnostic accuracy by combining clinical features,serum biomarkers such as alpha-fetoprotein,imaging features such as computed tomography and magnetic resonance imaging,and genomic data.For instance,SHapley Additive exPlanations(SHAP)-based random forests trained on NCBI GSE14520 microarray data(n=445)achieved 96.53%accuracy,while stacking ensembles applied to the SEER program data(n=1897)demonstrated an area under the receiver operating characteristic curve of 0.779 for mortality prediction.Despite promising results,challenges persist,including the computational costs of SHAP and local interpretable model-agnostic explanations analyses(e.g.,TreeSHAP requiring distributed computing for metabolomics datasets)and dataset biases(e.g.,SEER’s Western population dominance limiting generalizability).Future research must address inter-cohort heterogeneity,standardize explainability metrics,and prioritize lightweight surrogate models for resource-limited settings.This review presents the potential of explainable ensemble learning frameworks to bridge the gap between predictive accuracy and clinical interpretability,though rigorous validation in independent,multi-center cohorts is critical for real-world deployment.
基金approved by the Inonu University institutional review board for noninterventional studies(Approval No:2022/3842).
文摘BACKGROUND Endometriosis is a clinical condition characterized by the presence of endometrial glands outside the uterine cavity.While its incidence remains mostly uncertain,endometriosis impacts around 180 million women worldwide.Despite the presentation of several epidemiological and clinical explanations,the precise mechanism underlying the disease remains ambiguous.In recent years,researchers have examined the hereditary dimension of the disease.Genetic research has aimed to discover the gene or genes responsible for the disease through association or linkage studies involving candidate genes or DNA mapping techniques.AIM To identify genetic biomarkers linked to endometriosis by the application of machine learning(ML)approaches.METHODS This case-control study accounted for the open-access transcriptomic data set of endometriosis and the control group.We included data from 22 controls and 16 endometriosis patients for this purpose.We used AdaBoost,XGBoost,Stochasting Gradient Boosting,Bagged Classification and Regression Trees(CART)for classification using five-fold cross validation.We evaluated the performance of the models using the performance measures of accuracy,balanced accuracy,sensitivity,specificity,positive predictive value,negative predictive value and F1 score.RESULTS Bagged CART gave the best classification metrics.The metrics obtained from this model are 85.7%,85.7%,100%,75%,75%,100%and 85.7%for accuracy,balanced accuracy,sensitivity,specificity,positive predictive value,negative predictive value and F1 score,respectively.Based on the variable importance of modeling,we can use the genes CUX2,CLMP,CEP131,EHD4,CDH24,ILRUN,LINC01709,HOTAIR,SLC30A2 and NKG7 and other transcripts with inaccessible gene names as potential biomarkers for endometriosis.CONCLUSION This study determined possible genomic biomarkers for endometriosis using transcriptomic data from patients with/without endometriosis.The applied ML model successfully classified endometriosis and created a highly accurate diagnostic prediction model.Future genomic studies could explain the underlying pathology of endometriosis,and a non-invasive diagnostic method could replace the invasive ones.
文摘Combined hepatocellular-cholangiocarcinoma(cHCC-CCA)is a rare hetero-geneous primary malignant liver tumor containing both hepatocellular and cholangiocarcinoma features.The complex presentation of cHCC-CCA tends to be poorly investigated,and the information derived from traditional diagnostic techniques(histopathology and radiological imaging)is often not optimal.Since cHCC-CCA is usually difficult to diagnose due to complex histopathological features(edge learning)as excessive photos,hence,achieves treatment delays and poor prognosis,the incorporation of advanced artificial intelligence like edge learning is able to improve the patient’s outcome.Using artificial intelligence,particularly deep learning,has recently opened new doorways for the impro-vement of diagnostic accuracy.If artificial intelligence models are deployed on local devices,edge learning exercises this type of learning,which provides real time processing,improved data privacy and reduced bandwidth usage.This narrative review investigates the conceptual formulation of edge learning together with its opportunities for clinical applications in the prediction and classification of cHCC-CCA,the technical solution strategies,the clinical benefits it offers,and associated challenges and future directions.
文摘The authors employed inappropriate search keywords and strategies in their published bibliometric papers within volume 29 of the World Journal of Gastroenterology.The comment highlights the identified issues,provides evidence,and suggests improved study methodologies.Subsequent results with more appro-priate search strategies were presented to address the shortcomings.
基金supported by the National Nature Science Foundation of China(Grant No.32171305)Chongqing Technology Innova-tion and Application Development Project(Grant No.CSTB2023YSZX-JSX0003)Chongqing Municipal“Doctoral Express”Research Project(Grant No.CSTB2022BSXM-JCX0013).
文摘Head injuries from vehicle collisions,falls,and sports are often the result of complex mechanisms involving both linear and angular forces.This study aims to quantitatively assess the effects of linear and angular force on the severity of traumatic brain injury in rats during collisions.An orthogonal experimental design was employed,facilitating the manipulation of linear velocity,rotational acceleration,and angle(light,medium,and heavy)across 54 rats.24 hours post-injury,magnetic resonance imaging T2-weighted imaging,and diffusion tensor imaging were utilized to detect abnormal brain signals,with the fractional anisotropy value of the corpus callosum serv-ing as the primary injury indicator.Anatomical analyses and immunohistological staining were conducted to measure the amyloid precursor protein(β-APP)accumulation,using integrated optical density as a secondary indicator.Entropy weighting was applied to derive index weights for the injury scoring system.Through analysis guided by analysis of variance and linear regression,it was determined that both linear and angular loadings significantly impacted brain injury severity.Increased rotational acceleration at constant linear velocities correlated with more severe injuries,whereas the rotation angle exhibited minimal effect.Linear velocity emerged as the primary determinant of injury severity,accounting for 91.5%of the variance,while rotational acceleration and rotation angle contributed 6.5%and 0.9%,respectively.These findings offer critical insights for developing protective measures against brain injuries in traffic accidents.
基金funded in part by the National Key R&D Program of China(Grant No.2022YFC2402404)the National Natural Science Foundation of China(Grant Nos.82170351 and 82370342)+1 种基金the Natural Science Foundation of Jiangsu Province(Grant Nos.BK20222002 and BK20231145)the Clinical Capacity Enhancement Project of Jiangsu Province Hospital(the First Affiliated Hospital of Nanjing Medical University)(Grant No.J SPH-MA-2022-3)。
文摘It is often challenging to diagnose acute myocardial infarction(AMI)in patients with elevated high-sensitivity cardiac troponin T(hs-cTnT)before observing a significant rise and/or fall in hs-cTnT.The current study aimed to identify an optimal cut-off to rule in AMI.A total of 76411 patients with elevated hs-cTnT were included.The predictive cut-off values for diagnosing ST-segment elevation myocardial infarction(STEMI)and non-STsegment elevation myocardial infarction(NSTEMI)were assessed using the area under the receiver operating characteristic curve(AUC).Among the patients,50466(66.0%)had non-cardiac diseases,25945(34.0%)had cardiac diseases,and 15502(20.3%)had AMI,including 816(1.1%)with STEMI and 14686(19.2%)with NSTEMI.The median hs-cTnT level was 3788.0 ng/L in STEMI patients and 67.2 ng/L in NSTEMI patients.The optimal cut-off for diagnosing STEMI was 251.9 ng/L,with a sensitivity of 90.7%,specificity of 86.5%,and an AUC of 0.942;the optimal cut-off for diagnosing NSTEMI was 130.5 ng/L,with a sensitivity of 40.9%,specificity of 83.8%,and an AUC of 0.638.Collectively,optimizing the cut-off values for diagnosing STEMI and NSTEMI to 251.9 ng/L and 130.5 ng/L,respectively,demonstrated high accuracy in a large cohort of Chinese patients with elevated hs-cTnT.
基金funded by Beijing Natural Science Foundation-Haidian Original Innovation Joint Fund(Grant No.L222103)the National Natural Science Foundation of China(Grant No.72174012)。
文摘Objectives:This study aimed to develop and validate a stroke risk prediction model based on machine learning(ML)and regional healthcare big data,and determine whether it may improve the prediction performance compared with the conventional Logistic Regression(LR)model.Methods:This retrospective cohort study analyzed data from the CHinese Electronic health Records Research in Yinzhou(CHERRY)(2015–2021).We included adults aged 18–75 from the platform who had established records before 2015.Individuals with pre-existing stroke,key data absence,or excessive missingness(>30%)were excluded.Data on demographic,clinical measures,lifestyle factors,comorbidities,and family history of stroke were collected.Variable selection was performed in two stages:an initial screening via univariate analysis,followed by a prioritization of variables based on clinical relevance and actionability,with a focus on those that are modifiable.Stroke prediction models were developed using LR and four ML algorithms:Decision Tree(DT),Random Forest(RF),eXtreme Gradient Boosting(XGBoost),and Back Propagation Neural Network(BPNN).The dataset was split 7:3 for training and validation sets.Performance was assessed using receiver operating characteristic(ROC)curves,calibration,and confusion matrices,and the cutoff value was determined by Youden's index to classify risk groups.Results:The study cohort comprised 92,172 participants with 436 incident stroke cases(incidence rate:474/100,000 person-years).Ultimately,13 predictor variables were included.RF achieved the highest accuracy(0.935),precision(0.923),sensitivity(recall:0.947),and F1 score(0.935).Model evaluation demonstrated superior predictive performance of ML algorithms over conventional LR,with training/validation areaunderthe curve(AUC)sof0.777/0.779(LR),0.921/0.918(BPNN),0.988/0.980(RF),0.980/0.955(DT),and 0.962/0.958(XGBoost).Calibration analysis revealed a better fit for DT,LR and BPNN compared to RF and XGBoost model.Based on the optimal performance of the RF model,the ranking of factors in descending order of importance was:hypertension,age,diabetes,systolic blood pressure,waist,high-density lipoprotein Cholesterol,fasting blood glucose,physical activity,BMI,low-density lipoprotein cholesterol,total cholesterol,dietary habits,and family history of stroke.Using Youden's index as the optimal cutoff,the RF model stratified individuals into high-risk(>0.789)and low-risk(≤0.789)groups with robust discrimination.Conclusions:The ML-based prediction models demonstrated superior performance metrics compared to conventional LR and the RF is the optimal prediction model,providing an effective tool for risk stratifi cation in primary stroke prevention in community settings.
基金This study was approved by the Agrı Training and Research Hospital Scientific Research Ethics Committee(No.E-95531838-050.99-86900)conducted in accordance with the Declaration of Helsinki.
文摘BACKGROUND Gastric cancer is a major global health concern,often diagnosed at advanced stages,leading to poor prognosis.Proximal and distal gastric cancers exhibit distinct clinicopathological features.AIM To investigate the diagnostic value of hematological and inflammatory markers in differentiating proximal and distal gastric cancers and to evaluate their association with clinical outcomes.METHODS A retrospective cohort study was conducted on 150 patients diagnosed with gastric adenocarcinoma through histopathological analysis.Patients were categorized into proximal gastric cancer and distal gastric cancer groups.Laboratory parameters were analyzed.RESULTS Of the 150 patients,84 had proximal gastric cancer and 66 had distal gastric cancer.Dysphagia was significantly more common in the proximal gastric cancer group,while anemia and higher platelet-to-lymphocyte ratio values were observed in the distal gastric cancer group(P=0.031).Tumor stage and neutrophil-to-lymphocyte ratio emerged as independent predictors of all-cause mortality.No significant differences were found in other laboratory or biochemical parameters between the groups.CONCLUSION Proximal and distal gastric cancers demonstrate distinct clinical and laboratory profiles.The platelet-to-lymphocyte ratio may serve as a valuable marker in differentiating cancer localization,while the neutrophil-to-lymphocyte ratio is a prognostic indicator for mortality.These findings highlight the potential of hematological markers in optimizing diagnosis and treatment strategies for gastric cancer.
