Novel automated non-invasive detection of ocular surface squamous neoplasia using artificial intelligence

Ocular surface squamous neoplasia(OSSN)is a common eye surface tumour,characterized by the growth of abnormal cells on the ocular surface.OSSN includes invasive squamous cell carcinoma(SCC),in which tumour cells penetrate the basement membrane and infiltrate the stroma,as well as non-invasive conjunctival intraepithelial neoplasia,dysplasia,and SCC in-situ thereby presenting a challenge in early detection and diagnosis.Early identification and precise demarcation of the OSSN border leads to straightforward and curative treatments,such as topical medicines,whereas advanced invasive lesions may need orbital exenteration,which carries a risk of death.Artificial intelligence(AI)has emerged as a promising tool in the field of eye care and holds potential for its application in OSSN management.AI algorithms trained on large datasets can analyze ocular surface images to identify suspicious lesions associated with OSSN,aiding ophthalmologists in early detection and diagnosis.AI can also track and monitor lesion progression over time,providing objective measurements to guide treatment decisions.Furthermore,AI can assist in treatment planning by offering personalized recommendations based on patient data and predicting the treatment response.This manuscript highlights the role of AI in OSSN,specifically focusing on its contributions in early detection and diagnosis,assessment of lesion progression,treatment planning,telemedicine and remote monitoring,and research and data analysis.

Progress in non-invasive detection of liver fibrosis

Liver fibrosis is an important pathological precondition for hepatocellular carcinoma.The degree of hepatic fibrosis is positively correlated with liver cancer.Liver fibrosis is a series of pathological and physiological process related to liver cell necrosis and degeneration after chronic liver injury,which finally leads to extracellular matrix and collagen deposition.The early detection and precise staging of fibrosis and cirrhosis are very important for early diagnosis and timely initiation of appropriate therapeutic regimens.The risk of severe liver fibrosis finally progressing to liver carcinoma is＆gt;50%.It is known that biopsy is the gold standard for the diagnosis and staging of liver fibrosis.However,this method has some limitations,such as the potential for pain,sampling variability,and low patient acceptance.Furthermore,the necessity of obtaining a tissue diagnosis of liver fibrosis still remains controversial.An increasing number of reliable non-invasive approaches are now available that are widely applied in clinical practice,mostly in cases of viral hepatitis,resulting in a significantly decreased need for liver biopsy.In fact,the noninvasive detection and evaluation of liver cirrhosis now has good accuracy due to current serum markers,ultrasound imaging,and magnetic resonance imaging quantification techniques.A prominent advantage of the non-invasive detection and assessment of liver fibrosis is that liver fibrosis can be monitored repeatedly and easily in the same patient.Serum biomarkers have the advantages of high applicability（〉95%）and good reproducibility.However,their results can be influenced by different patient conditions because none of these markers are liver-specific.The most promising techniques appear to be transient elastography and magnetic resonance elastography because they provide reliable results for the detection of fibrosis in the advanced stages,and future developments promise to increase the reliability and accuracy of the staging of hepatic fibrosis.This article aims to describe the recent progress in the development of non-invasive assessment methods for the staging of liver fibrosis,with a special emphasize on computer-aided quantitative and deep learning methods.

Functional near-infrared spectroscopy in non-invasive neuromodulation

Non-invasive cerebral neuromodulation technologies are essential for the reorganization of cerebral neural networks,which have been widely applied in the field of central neurological diseases,such as stroke,Parkinson’s disease,and mental disorders.Although significant advances have been made in neuromodulation technologies,the identification of optimal neurostimulation paramete rs including the co rtical target,duration,and inhibition or excitation pattern is still limited due to the lack of guidance for neural circuits.Moreove r,the neural mechanism unde rlying neuromodulation for improved behavioral performance remains poorly understood.Recently,advancements in neuroimaging have provided insight into neuromodulation techniques.Functional near-infrared spectroscopy,as a novel non-invasive optical brain imaging method,can detect brain activity by measuring cerebral hemodynamics with the advantages of portability,high motion tole rance,and anti-electromagnetic interference.Coupling functional near-infra red spectroscopy with neuromodulation technologies offe rs an opportunity to monitor the cortical response,provide realtime feedbac k,and establish a closed-loop strategy integrating evaluation,feedbac k,and intervention for neurostimulation,which provides a theoretical basis for development of individualized precise neuro rehabilitation.We aimed to summarize the advantages of functional near-infra red spectroscopy and provide an ove rview of the current research on functional near-infrared spectroscopy in transcranial magnetic stimulation,transcranial electrical stimulation,neurofeedback,and braincomputer interfaces.Furthermore,the future perspectives and directions for the application of functional near-infrared spectroscopy in neuromodulation are summarized.In conclusion,functional near-infrared spectroscopy combined with neuromodulation may promote the optimization of central pellral reorganization to achieve better functional recovery form central nervous system diseases.

Exploring non-invasive diagnostics for metabolic dysfunctionassociated fatty liver disease

The population with metabolic dysfunction-associated fatty liver disease(MAFLD)is increasingly common worldwide.Identification of people at risk of progression to advanced stages is necessary to timely offer interventions and appropriate care.Liver biopsy is currently considered the gold standard for the diagnosis and staging of MAFLD,but it has associated risks and limitations.This has spurred the exploration of non-invasive diagnostics for MAFLD,especially for steatohepatitis and fibrosis.These non-invasive approaches mostly include biomarkers and algorithms derived from anthropometric measurements,serum tests,imaging or stool metagenome profiling.However,they still need rigorous and widespread clinical validation for the diagnostic performance.

FibroScan-aspartate transaminase:A superior non-invasive model for diagnosing high-risk metabolic dysfunction-associated steatohepatitis

BACKGROUND Non-alcoholic fatty liver disease(NAFLD)with hepatic histological NAFLD activity score≥4 and fibrosis stage F≥2 is regarded as“at risk”non-alcoholic steatohepatitis(NASH).Based on an international consensus,NAFLD and NASH were renamed as metabolic dysfunction-associated steatotic liver disease(MASLD)and metabolic dysfunction-associated steatohepatitis(MASH),respectively;hence,we introduced the term“high-risk MASH”.Diagnostic values of seven non-invasive models,including FibroScan-aspartate transaminase(FAST),fibrosis-4(FIB-4),aspartate transaminase to platelet ratio index(APRI),etc.for high-risk MASH have rarely been studied and compared in MASLD.AIM To assess the clinical value of seven non-invasive models as alternatives to liver biopsy for diagnosing high-risk MASH.METHODS A retrospective analysis was conducted on 309 patients diagnosed with NAFLD via liver biopsy at Beijing Ditan Hospital,between January 2012 and December 2020.After screening for MASLD and the exclusion criteria,279 patients wereincluded and categorized into high-risk and non-high-risk MASH groups.Utilizing threshold values of each model,sensitivity,specificity,positive predictive value(PPV),and negative predictive values(NPV),were calculated.Receiver operating characteristic curves were constructed to evaluate their diagnostic efficacy based on the area under the curve(AUROC).RESULTS MASLD diagnostic criteria were met by 99.4%patients with NAFLD.The MASLD population was analyzed in two cohorts:Overall population(279 patients)and the subgroup(117 patients)who underwent liver transient elastography(FibroScan).In the overall population,FIB-4 showed better diagnostic efficacy and higher PPV,with sensitivity,specificity,PPV,NPV,and AUROC of 26.9%,95.2%,73.5%,72.2%,and 0.75.APRI,Forns index,and aspartate transaminase to alanine transaminase ratio(ARR)showed moderate diagnostic efficacy,whereas S index and gamma-glutamyl transpeptidase to platelet ratio(GPR)were relatively weaker.In the subgroup,FAST had the highest diagnostic efficacy,its sensitivity,specificity,PPV,NPV,and AUROC were 44.2%,92.3%,82.1%,67.4%,and 0.82.The FIB-4 AUROC was 0.76.S index and GPR exhibited almost no diagnostic value for high-risk MASH.CONCLUSION FAST and FIB-4 could replace liver biopsy as more effectively diagnostic methods for high-risk MASH compared to APRI,Forns index,ARR,S index,and GPR;FAST is superior to FIB-4.

Recent developments in non-invasive methods for assessing metabolic dysfunction-associated fatty liver disease

The prevalence of metabolic dysfunction-associated fatty liver disease(MAFLD)is increasing,affecting over one-third of the global population and contributing to significant morbidity and mortality.Diagnosing MAFLD,especially with advan-ced fibrosis,remains challenging due to the limitations of liver biopsy,the current gold standard.Non-invasive tests are crucial for early detection and management.Among these,the fibrosis-4 index(Fib-4)is widely recommended as a first-line test for screening for liver fibrosis.Advanced imaging techniques,including ultrasound-based elastography and magnetic resonance elastography,offer high accuracy but are limited by cost and availability.Combining biomarkers,such as in the enhanced liver fibrosis score and FibroScan-AST score,enhances diagnostic precision and is recommended to further stratify patients who are considered to be intermediate or high risk from the Fib-4 score.We believe that the future lies in the combined use of biomarkers to improve diagnostic accuracy.

Non-invasively differentiate non-alcoholic steatohepatitis by visualizing hepatic integrinαvβ3 expression with a targeted molecular imaging modality

BACKGROUND Non-invasive methods to diagnose non-alcoholic steatohepatitis(NASH),an inflammatory subtype of non-alcoholic fatty liver disease(NAFLD),are currently unavailable.AIM To develop an integrinαvβ3-targeted molecular imaging modality to differentiate NASH.METHODS Integrinαvβ3 expression was assessed in Human LO2 hepatocytes Scultured with palmitic and oleic acids(FFA).Hepatic integrinαvβ3 expression was analyzed in rabbits fed a high-fat diet(HFD)and in rats fed a high-fat,high-carbohydrate diet(HFCD).After synthesis,cyclic arginine-glycine-aspartic acid peptide(cRGD)was labeled with gadolinium(Gd)and used as a contrast agent in magnetic resonance imaging(MRI)performed on mice fed with HFCD.RESULTS Integrinαvβ3 was markedly expressed on FFA-cultured hepatocytes,unlike the control hepatocytes.Hepatic integrinαvβ3 expression significantly increased in both HFD-fed rabbits and HFCD-fed rats as simple fatty liver(FL)progressed to steatohepatitis.The distribution of integrinαvβ3 in the liver of NASH cases largely overlapped with albumin-positive staining areas.In comparison to mice with simple FL,the relative liver MRI-T1 signal value at 60 minutes post-injection of Gd-labeled cRGD was significantly increased in mice with steatohepatitis(P<0.05),showing a positive correlation with the NAFLD activity score(r=0.945;P<0.01).Hepatic integrinαvβ3 expression was significantly upregulated during NASH development,with hepatocytes being the primary cells expressing integrinαvβ3.CONCLUSION After using Gd-labeled cRGD as a tracer,NASH was successfully distinguished by visualizing hepatic integrinαvβ3 expression with MRI.

Advances in the Research Application of Ultrasound Non-Invasive Stress-Strain Loop Technology in Cardiovascular Diseases

The ultrasound pressure-strain loop (PSL) technique is a non-invasive method of examining myocardial work, which takes into account the effect of cardiac afterload on deformation and combines the overall longitudinal strain force of the left ventricle with the changes in the left ventricular pressure, allowing earlier detection of possible subclinical cardiac damage in patients, and a more accurate and non-invasive assessment of the patient’s myocardial work performance. In this article, we will discuss the progress of PSL applications in cardiovascular diseases.

Ensemble for evaluating diagnostic efficacy of non-invasive indices in predicting liver fibrosis in untreated hepatitis C virus population

BACKGROUND Hepatitis C virus(HCV)infection progresses through various phases,starting with inflammation and ending with hepatocellular carcinoma.There are several invasive and non-invasive methods to diagnose chronic HCV infection.The invasive methods have their benefits but are linked to morbidity and complications.Thus,it is important to analyze the potential of non-invasive methods as an alternative.Shear wave elastography(SWE)is a non-invasive imaging tool widely validated in clinical and research studies as a surrogate marker of liver fibrosis.Liver fibrosis determination by invasive liver biopsy and non-invasive SWE agree closely in clinical studies and therefore both are gold standards.AIM To analyzed the diagnostic efficacy of non-invasive indices[serum fibronectin,aspartate aminotransferase to platelet ratio index(APRI),alanine aminotransferase ratio(AAR),and fibrosis-4(FIB-4)]in relation to SWE.We have used an Artificial Intelligence method to predict the severity of liver fibrosis and uncover the complex relationship between non-invasive indices and fibrosis severity.METHODS We have conducted a hospital-based study considering 100 untreated patients detected as HCV positive using a quantitative Real-Time Polymerase Chain Reaction assay.We performed statistical and probabilistic analyses to determine the relationship between non-invasive indices and the severity of fibrosis.We also used standard diagnostic methods to measure the diagnostic accuracy for all the subjects.RESULTS The results of our study showed that fibronectin is a highly accurate diagnostic tool for predicting fibrosis stages(mild,moderate,and severe).This was based on its sensitivity(100%,92.2%,96.2%),specificity(96%,100%,98.6%),Youden’s index(0.960,0.922,0.948),area under receiver operating characteristic curve(0.999,0.993,0.922),and Likelihood test(LR+>10 and LR-<0.1).Additionally,our Bayesian Network analysis revealed that fibronectin(>200),AAR(>1),APRI(>3),and FIB-4(>4)were all strongly associated with patients who had severe fibrosis,with a 100% probability.CONCLUSION We have found a strong correlation between fibronectin and liver fibrosis progression in HCV patients.Additionally,we observed that the severity of liver fibrosis increases with an increase in the non-invasive indices that we investigated.

A Study on Near-Infrared Non-Invasive Blood Glucose Concentration Regression Prediction Based on PSO-MKL-SVR

To improve the accuracy of predicting non-invasive blood glucose concentration in the near-infrared spectrum, we utilized the Particle Swarm Optimization (PSO) algorithm to optimize hyperparameters for the Multi-Kernel Learning Support Vector Machine (MKL-SVR). With these optimized hyperparameters, we established a non-invasive blood glucose regression model, referred to as the PSO-MKL-SVR model. Subsequently, we conducted a comparative analysis between the PSO-MKL-SVR model and the PSO-SVR model. In a dataset comprising ten volunteers, the PSO-MKL-SVR model exhibited significant precision improvements, including a 16.03% reduction in Mean Square Error and a 0.29% increase in the Squared Correlation Coefficient. Moreover, there was a 0.14% higher probability of the Clark’s Error Grid Analysis falling within Zone A. Additionally, the PSO-MKL-SVR model demonstrated a faster operational speed compared to the PSO-SVR model.

Observation on the Effect of Non-Invasive Ventilator Combined with Conventional Therapy in the Treatment of Chronic Obstructive Pulmonary Disease Complicated with Respiratory Failure

Objective:To explore the clinical effect of a non-invasive ventilator combined with conventional therapy in the treatment of patients with chronic obstructive pulmonary disease(COPD)combined with respiratory failure.Methods:68 patients with COPD combined with respiratory failure treated in our hospital from September 2021 to October 2023 were selected as the research subjects.Using the random number table method,they were divided into a control group and an experimental group of 34 cases each.The control group received conventional symptomatic treatment,and the experimental group received non-invasive ventilator treatment based on the control group.The clinical effects,blood gas indicators(partial pressure of carbon dioxide(PaCO_(2)),partial pressure of oxygen(PaO_(2)),arterial oxygen saturation(SaO_(2))),lung function(forced expiratory volume in 1 second(FEV1),forced vital capacity(FVC),6 min walking distance),complications,and inflammatory factor levels(c-reactive protein(CRP),interleukin-6(IL-6),neutrophil-to-lymphocyte ratio(NLR))of the two groups of patients were observed.Results:(1)The clinical efficacy of the patients in the experimental group(33/97.06%)was more significant as compared with the control group(25/73.53%)(P<0.05);(2)After treatment,the clinical efficacy of the two groups of patients in terms of FEV1,FEV1/FVC,6-minute walking distance,PaO_(2)and SaO_(2)all increased in the experimental group as compared to that of the control group(P<0.05);(3)After treatment,the PaCO_(2),CRP,IL-6,and NLR of the two groups of patients decreased,and the decrease in the experimental group was higher than that of the control group(P<0.05);(4)The patients’complication rate in the experimental group(2/5.88%)was lower as compared to that of the control group(9/26.46%)(P<0.05).Conclusion:Non-invasive ventilators combined with conventional therapy achieved good clinical results in treating patients with COPD and respiratory failure.

Analysis of the Effect of Non-Invasive Positive Pressure Ventilation in Emergency Treatment of Severe Bronchial Asthma with Respiratory Failure

Objective:This study aims to evaluate the clinical efficacy of non-invasive positive pressure ventilation(NIPPV)in patients with severe bronchial asthma combined with respiratory failure.Methods:90 patients with severe bronchial asthma combined with respiratory failure between September 2022 and December 2023 were selected for the study and randomly divided into the experimental group(NIPPV-assisted treatment)and the control group.The differences between the two groups were compared in terms of total effective rate of treatment,days of clinical symptom disappearance,days of hospitalization,lung function indexes,incidence of adverse reactions,and quality of life.Results:Patients in the experimental group had a significantly higher total effective rate of treatment(97.78%)than the control group(75.56%).In terms of pulmonary function indexes,patients in the experimental group showed significant improvement after treatment,especially the increase in forced expiratory volume and forced vital capacity,while these improvements were not as obvious in the control group.In addition,the incidence of adverse reactions was significantly lower in the experimental group than in the control group,suggesting that the application of NIPPV is relatively safe.Quality of life assessment also showed that patients in the experimental group had significantly better quality of life than the control group after treatment.Conclusion:This study demonstrated the effectiveness of NIPPV as an adjunctive treatment for severe bronchial asthma combined with respiratory failure.NIPPV can improve lung function,reduce the incidence of adverse effects,increase the overall effectiveness of the treatment,and contribute to the improvement of patients'quality of life.Therefore,NIPPV should be regarded as an effective and safe treatment in clinical management,especially in patients with severe bronchial asthma combined with respiratory failure,where its application has potential clinical significance.

Relationship Between Gene-Phenotype and Clinical Manifestations of Chromosomal Copy Number Variations Indicated by Non-Invasive Prenatal Testing

Objective:To analyze the clinical value of non-invasive prenatal testing(NIPT)in detecting chromosomal copy number variations(CNVs)and to explore the relationship between gene expression and clinical manifestations of chromosomal copy number variations.Methods:3551 naturally conceived singleton pregnant women who underwent NIPT were included in this study.The NIPT revealed abnormalities other than sex chromosome abnormalities and trisomy 13,18,and 21.Pregnant women with chromosome copy number variations underwent genetic counseling and prenatal ultrasound examination.Interventional prenatal diagnosis and chromosome microarray analysis(CMA)were performed.The clinical phenotypes and pregnancy outcomes of different prenatal diagnoses were analyzed.Additionally,a follow-up was conducted by telephone to track fetal development after birth,at six months,and one year post-birth.Results:A total of 53 cases among 3551 cases showed chromosomal copy number variation.Interventional prenatal diagnosis was performed in 36 cases:27 cases were negative and 8 were consistent with the NIPT test results.This indicates that NIPT’s positive predictive value(PPV)in CNVs is 22.22%.Conclusion:NIPT has certain clinical significance in screening chromosome copy number variations and is expected to become a routine screening for chromosomal microdeletions and microduplications.However,further interventional prenatal diagnosis is still needed to identify fetal CNVs.

基于改进Detection Transformer的棉花幼苗与杂草检测模型研究

基于深度学习的目标检测技术在棉花幼苗与杂草检测领域已取得一定进展。论文提出了基于改进Detection Transformer的棉花幼苗与杂草检测模型,以提高杂草目标检测的准确率和效率。首先,引入了可变形注意力模块替代原始模型中的Transformer注意力模块,提高模型对特征图目标形变的处理能力。提出新的降噪训练机制,解决了二分图匹配不稳定问题。提出混合查询选择策略,提高解码器对目标类别和位置信息的利用效率。使用Swin Transformer作为网络主干,提高模型特征提取能力。通过对比原网络,论文提出的模型方法在训练过程中表现出更快的收敛速度,并且在准确率方面提高了6.7%。

Emerging role of non-invasive and liquid biopsy biomarkers in pancreatic cancer

A global increase in the incidence of pancreatic cancer(PanCa)presents a major concern and health burden.The traditional tissue-based diagnostic techniques provided a major way forward for molecular diagnostics;however,they face limitations based on diagnosis-associated difficulties and concerns surrounding tissue availability in the clinical setting.Late disease development with asymptomatic behavior is a drawback in the case of existing diagnostic procedures.The capability of cell free markers in discriminating PanCa from autoimmune pancreatitis and chronic pancreatitis along with other precancerous lesions can be a boon to clinicians.Early-stage diagnosis of PanCa can be achieved only if these biomarkers specifically discriminate the non-carcinogenic disease stage from malignancy with respect to tumor stages.In this review,we comprehensively described the non-invasive disease detection approaches and why these approaches are gaining popularity for their early-stage diagnostic capability and associated clinical feasibility.

Non-invasive model for predicting high-risk esophageal varices based on liver and spleen stiffness

BACKGROUND Acute bleeding due to esophageal varices(EVs)is a life-threatening complication in patients with cirrhosis.The diagnosis of EVs is mainly through upper gastrointestinal endoscopy,but the discomfort,contraindications and complications of gastrointestinal endoscopic screening reduce patient compliance.According to the bleeding risk of EVs,the Baveno VI consensus divides varices into high bleeding risk EVs(HEVs)and low bleeding risk EVs(LEVs).We sought to identify a non-invasive prediction model based on spleen stiffness measurement(SSM)and liver stiffness measurement(LSM)as an alternative to EVs screening.AIM To develop a safe,simple and non-invasive model to predict HEVs in patients with viral cirrhosis and identify patients who can be exempted from upper gastrointestinal endoscopy.METHODS Data from 200 patients with viral cirrhosis were included in this study,with 140 patients as the modelling group and 60 patients as the external validation group,and the EVs types of patients were determined by upper gastrointestinal endoscopy and the Baveno Ⅵ consensus.Those patients were divided into the HEVs group(66 patients)and the LEVs group(74 patients).The effect of each parameter on HEVs was analyzed by univariate and multivariate analyses,and a noninvasive prediction model was established.Finally,the discrimination ability,calibration ability and clinical efficacy of the new model were verified in the modelling group and the external validation group.RESULTS Univariate and multivariate analyses showed that SSM and LSM were associated with the occurrence of HEVs in patients with viral cirrhosis.On this basis,logistic regression analysis was used to construct a prediction model:Ln[P/(1-P)]=-8.184-0.228×SSM+0.642×LSM.The area under the curve of the new model was 0.965.When the cut-off value was 0.27,the sensitivity,specificity,positive predictive value and negative predictive value of the model for predicting HEVs were 100.00%,82.43%,83.52%,and 100%,respectively.Compared with the four prediction models of liver stiffness-spleen diameter to platelet ratio score,variceal risk index,aspartate aminotransferase to alanine aminotransferase ratio,and Baveno VI,the established model can better predict HEVs in patients with viral cirrhosis.CONCLUSION Based on the SSM and LSM measured by transient elastography,we established a non-invasive prediction model for HEVs.The new model is reliable in predicting HEVs and can be used as an alternative to routine upper gastrointestinal endoscopy screening,which is helpful for clinical decision making.

Non-invasive detection of pancreatic cancer by measuring DNA methylation of Basonuclin 1 and Septin 9 in plasma

Pancreatic cancer (PC), a deadly malignancy with an overall 5-year survival rate of 5% to 15%, is ranked as the seventh leading cause of cancer death in the world in spite of its low occurrence rate.[1,2] Early detection appears to be the most effective approach to improve the overall survival of patients with PC. However, the difficulty in early detection of PC is a lack of specific symptoms and reliable biomarkers. Currently, carbohydrate antigen 19-9 (CA 19-9) is a serum biomarker that is widely used in PC detection. However, 10% of patients with PC cannot produce CA 19-9 and serum CA 19-9 is frequently absent in patients with early-stage cancer. Furthermore, CA 19-9 is often found to be elevated in benign conditions or in other cancers, making its utility limited. Therefore, it is important to identify new diagnostic biomarkers to improve PC detection.

A Hybrid Intrusion Detection Method Based on Convolutional Neural Network and AdaBoost

To solve the problem of poor detection and limited application range of current intrusion detection methods,this paper attempts to use deep learning neural network technology to study a new type of intrusion detection method.Hence,we proposed an intrusion detection algorithm based on convolutional neural network(CNN)and AdaBoost algorithm.This algorithm uses CNN to extract the characteristics of network traffic data,which is particularly suitable for the analysis of continuous and classified attack data.The AdaBoost algorithm is used to classify network attack data that improved the detection effect of unbalanced data classification.We adopt the UNSW-NB15 dataset to test of this algorithm in the PyCharm environment.The results show that the detection rate of algorithm is99.27%and the false positive rate is lower than 0.98%.Comparative analysis shows that this algorithm has advantages over existing methods in terms of detection rate and false positive rate for small proportion of attack data.

Biomarkers for detecting colorectal cancer non-invasively: DNA,RNA or proteins?

Colorectal cancer(CRC)is a global problem affecting millions of people worldwide.This disease is unique because of its slow progress that makes it preventable and often curable.CRC symptoms usually emerge only at advanced stages of the disease,consequently its early detection can be achieved only through active population screening,which markedly reduces mortality due to this cancer.CRC screening tests that employ non-invasively detectable biomarkers are currently being actively developed and,in most cases,samples of either stool or blood are used.However,alternative biological substances that can be collected non-invasively(colorectal mucus,urine,saliva,exhaled air)have now emerged as new sources of diagnostic biomarkers.The main categories of currently explored CRC biomarkers are:(1)Proteins(comprising widely used haemoglobin);(2)DNA(including mutations and methylation markers);(3)RNA(in particular microRNAs);(4)Low molecular weight metabolites(comprising volatile organic compounds)detectable by metabolomic techniques;and(5)Shifts in gut microbiome composition.Numerous tests for early CRC detection employing such non-invasive biomarkers have been proposed and clinically studied.While some of these studies generated promising early results,very few of the proposed tests have been transformed into clinically validated diagnostic/screening techniques.Such DNA-based tests as Food and Drug Administration-approved multitarget stool test(marketed as Cologuard®)or blood test for methylated septin 9(marketed as Epi proColon®2.0 CE)show good diagnostic performance but remain too expensive and technically complex to become effective CRC screening tools.It can be concluded that,despite its deficiencies,the protein(haemoglobin)detection-based faecal immunochemical test(FIT)today presents the most cost-effective option for non-invasive CRC screening.The combination of non-invasive FIT and confirmatory invasive colonoscopy is the current strategy of choice for CRC screening.However,continuing intense research in the area promises the emergence of new superior non-invasive CRC screening tests that will allow the development of improved disease prevention strategies.

IDS-INT:Intrusion detection system using transformer-based transfer learning for imbalanced network traffic

A network intrusion detection system is critical for cyber security against llegitimate attacks.In terms of feature perspectives,network traffic may include a variety of elements such as attack reference,attack type,a subcategory of attack,host information,malicious scripts,etc.In terms of network perspectives,network traffic may contain an imbalanced number of harmful attacks when compared to normal traffic.It is challenging to identify a specific attack due to complex features and data imbalance issues.To address these issues,this paper proposes an Intrusion Detection System using transformer-based transfer learning for Imbalanced Network Traffic(IDS-INT).IDS-INT uses transformer-based transfer learning to learn feature interactions in both network feature representation and imbalanced data.First,detailed information about each type of attack is gathered from network interaction descriptions,which include network nodes,attack type,reference,host information,etc.Second,the transformer-based transfer learning approach is developed to learn detailed feature representation using their semantic anchors.Third,the Synthetic Minority Oversampling Technique(SMOTE)is implemented to balance abnormal traffic and detect minority attacks.Fourth,the Convolution Neural Network(CNN)model is designed to extract deep features from the balanced network traffic.Finally,the hybrid approach of the CNN-Long Short-Term Memory(CNN-LSTM)model is developed to detect different types of attacks from the deep features.Detailed experiments are conducted to test the proposed approach using three standard datasets,i.e.,UNsWNB15,CIC-IDS2017,and NSL-KDD.An explainable AI approach is implemented to interpret the proposed method and develop a trustable model.