气管黏液表皮样癌误诊为气管异物1例

1临床资料患儿,男,5岁,因进食后呛咳、发现气管异物4 h于2022-06-17就诊于昆明市儿童医院。患儿家属代诉患儿进食鸡蛋时不慎误吸,随后出现频繁、剧烈呛咳,伴喘息。随即于当地医院就诊,行胸部CT示气管近隆突部2.2 cm处见0.9 cm×0.8 cm稍高密度影,考虑异物(图1A)。诊断为主气管异物梗阻,未行处理,由救护车送至我院ICU。查体:一般情况欠佳,双肺呼吸音粗,可闻及少许痰鸣音,未闻及哮鸣音。入院诊断:气管内异物、呼吸道感染。患儿入ICU后为防止烦躁加重气管异物位置变化,给予舒芬太尼、右美托咪定和丙泊酚持续镇静镇痛,给予头孢唑林钠、奥美拉唑以及地奈德和吸入用盐酸氨溴索雾化。

Stage at diagnosis of colorectal cancer through diagnostic route:Who should be screened?

BACKGROUND Colorectal cancer(CRC)is a global health concern,with advanced-stage diagnoses contributing to poor prognoses.The efficacy of CRC screening has been well-established;nevertheless,a significant proportion of patients remain unscreened,with>70%of cases diagnosed outside screening.Although identifying specific subgroups for whom CRC screening should be particularly recommended is crucial owing to limited resources,the association between the diagnostic routes and identification of these subgroups has been less appreciated.In the Japanese cancer registry,the diagnostic routes for groups discovered outside of screening are primarily categorized into those with comorbidities found during hospital visits and those with CRC-related symptoms.AIM To clarify the stage at CRC diagnosis based on diagnostic routes.METHODS We conducted a retrospective observational study using a cancer registry of patients with CRC between January 2016 and December 2019 at two hospitals.The diagnostic routes were primarily classified into three groups:Cancer screening,follow-up,and symptomatic.The early-stage was defined as Stages 0 or I.Multivariate and univariate logistic regressions were exploited to determine the odds of early-stage diagnosis in the symptomatic and cancer screening groups,referencing the follow-up group.The adjusted covariates were age,sex,and tumor location.RESULTS Of the 2083 patients,715(34.4%),1064(51.1%),and 304(14.6%)belonged to the follow-up,symptomatic,and cancer screening groups,respectively.Among the 2083 patients,CRCs diagnosed at an early stage were 57.3%(410 of 715),23.9%(254 of 1064),and 59.5%(181 of 304)in the follow-up,symptomatic,and cancer screening groups,respectively.The symptomatic group exhibited a lower likelihood of early-stage diagnosis than the follow-up group[P<0.001,adjusted odds ratio(aOR),0.23;95%confidence interval(95%CI):0.19-0.29].The likelihood of diagnosis at an early stage was similar between the follow-up and cancer screening groups(P=0.493,aOR for early-stage diagnosis in the cancer screening group vs follow-up group=1.11;95%CI=0.82-1.49).CONCLUSION CRCs detected during hospital visits for comorbidities were diagnosed earlier,similar to cancer screening.CRC screening should be recommended,particularly for patients without periodical hospital visits for comorbidities.

Transient elastography with controlled attenuation parameter for the diagnosis of colorectal polyps in patients with nonalcoholic fatty liver disease

BACKGROUND The severity of nonalcoholic fatty liver disease(NAFLD)and lipid metabolism are related to the occurrence of colorectal polyps.Liver-controlled attenuation parameters(liver-CAPs)have been established to predict the prognosis of hepatic steatosis patients.AIM To explore the risk factors associated with colorectal polyps in patients with NAFLD by analyzing liver-CAPs and establishing a diagnostic model.METHODS Patients who were diagnosed with colorectal polyps in the Department of Gastroenterology of our hospital between June 2021 and April 2022 composed the case group,and those with no important abnormalities composed the control group.The area under the receiver operating characteristic curve was used to predict the diagnostic efficiency.Differences were considered statistically significant when P<0.05.RESULTS The median triglyceride(TG)and liver-CAP in the case group were significantly greater than those in the control group(mmol/L,1.74 vs 1.05;dB/m,282 vs 254,P<0.05).TG and liver-CAP were found to be independent risk factors for colorectal polyps,with ORs of 2.338(95%CI:1.154–4.733)and 1.019(95%CI:1.006–1.033),respectively(P<0.05).And there was no difference in the diagnostic efficacy between liver-CAP and TG combined with liver-CAP(TG+CAP)(P>0.05).When the liver-CAP was greater than 291 dB/m,colorectal polyps were more likely to occur.CONCLUSION The levels of TG and liver-CAP in patients with colorectal polyps are significantly greater than those patients without polyps.Liver-CAP alone can be used to diagnose NAFLD with colorectal polyps.

Optimized online filter stack spectrometer for ultrashort X-ray pulses

Currently,with the advent of high-repetition-rate laser-plasma experiments,the demand for online diagnosis for the X-ray spectrum is increasing because the laser-plasma-generated X-ray spectrum is very important for characterizing electron dynamics and applications.In this study,scintillators and silicon PIN(P-type–intrinsic-N-type semiconductor)diodes were used to construct a wideband online filter stack spectrometer.The X-ray sensor and filter arrangement was optimized using a genetic algorithm to minimize the condition number of the response matrix.Consequently,the unfolding error was significantly reduced based on numerical experiments.The detector responses were quantitatively calibrated by irradiating the scintillator and PIN diode with various nuclides and comparing the measuredγ-ray peaks.A prototype 15-channel spectrometer was developed by integrating an X-ray detector with front-and back-end electronics.The prototype spectrometer could record X-ray pulse signals at a repetition rate of 1 kHz.Furthermore,an optimized spectrometer was employed to record the real-time spectra of laser-driven bremsstrahlung sources.This optimized spectrometer offers a compact solution for spectrum diagnostics of ultrashort X-ray pulses,exhibiting improved accuracy in terms of spectrum measurements and repetition rates,and could be widely used in next-generation high-repetition-rate high-power laser facilities.

A synthetic diagnostics platform for microwave imaging diagnostics in tokamaks

Interpreting experimental diagnostics data in tokamaks,while considering non-ideal effects,is challenging due to the complexity of plasmas.To address this challenge,a general synthetic diagnostics(GSD)platform has been established that facilitates microwave imaging reflectometry and electron cyclotron emission imaging.This platform utilizes plasma profiles as input and incorporates the finite-difference time domain,ray tracing and the radiative transfer equation to calculate the propagation of plasma spontaneous radiation and the external electromagnetic field in plasmas.Benchmark tests for classical cases have been conducted to verify the accuracy of every core module in the GSD platform.Finally,2D imaging of a typical electron temperature distribution is reproduced by this platform and the results are consistent with the given real experimental data.This platform also has the potential to be extended to 3D electromagnetic field simulations and other microwave diagnostics such as cross-polarization scattering.

Diagnostic delay in inflammatory bowel diseases in a German population

BACKGROUND Early diagnosis is key to prevent bowel damage in inflammatory bowel disease(IBD).Risk factor analyses linked with delayed diagnosis in European IBD patients are scarce and no data in German IBD patients exists.AIM To identify risk factors leading to prolonged diagnostic time in a German IBD cohort.METHODS Between 2012 and 2022,430 IBD patients from four Berlin hospitals were enrolled in a prospective study and asked to complete a 16-item questionnaire to determine features of the path leading to IBD diagnosis.Total diagnostic time was defined as the time from symptom onset to consulting a physician(patient waiting time)and from first consultation to IBD diagnosis(physician diagnostic time).Univariate and multivariate analyses were performed to identify risk factors for each time period.RESULTS The total diagnostic time was significantly longer in Crohn’s disease(CD)compared to ulcerative colitis(UC)patients(12.0 vs 4.0 mo;P<0.001),mainly due to increased physician diagnostic time(5.5 vs 1.0 mo;P<0.001).In a multivariate analysis,the predominant symptoms diarrhea(P=0.012)and skin lesions(P=0.028)as well as performed gastroscopy(P=0.042)were associated with longer physician diagnostic time in CD patients.In UC,fever was correlated(P=0.020)with shorter physician diagnostic time,while fatigue(P=0.011)and positive family history(P=0.046)were correlated with longer physician diagnostic time.CONCLUSION We demonstrated that CD patients compared to UC are at risk of long diagnostic delay.Future efforts should focus on shortening the diagnostic delay for a better outcome in these patients.

Polarimeter–interferometer diagnostic using terahertz solid-state sources for fluctuation measurements on Keda Torus e Xperiment(KTX)

A multi-channel polarimeter-interferometer has been developed on the Keda Torus eXperiment(KTX)for the study of equilibrium dynamics and internal magnetic fluctuations.A three-wave technique based on terahertz solid-state sources(-650 GHz)is applied for simultaneous measurements of electron density and Faraday rotation angle.The output power of the microwave source is 2 mW.Faraday rotation effect using a rotating wave plate is tested with phase noise less than 0.8°,and the density phase noise is less than 0.9°.Measurement of Faraday rotation angle and density for discharges on KTX have demonstrated high sensitivity to internal MHD activities.

Development of D_α band symmetrical visible optical diagnostic for boundary reconstruction on EAST tokamak

To investigate the potential of utilizing visible spectral imaging for controlling the plasma boundary shape during stable operation of plasma in future tokamak, a D_α band symmetric visible light diagnostic system was designed and implemented on the Experimental Advanced Superconducting Tokamak(EAST). This system leverages two symmetric optics for joint plasma imaging. The optical system exhibits a spatial resolution less than 2 mm at the poloidal cross-section, distortion within the field of view below 10%, and relative illumination of 91%.The high-quality images obtained enable clear observation of both the plasma boundary position and the characteristics of components within the vacuum vessel. Following system calibration and coordinate transformation, the image coordinate boundary features are mapped to the tokamak coordinate system. Utilizing this system, the plasma boundary was reconstructed, and the resulting representation showed alignment with the EFIT(Equilibrium Fitting) results. This underscores the system's superior performance in boundary reconstruction applications and provides a diagnostic foundation for boundary shape control based on visible spectral imaging.

Reconstruction of poloidal magnetic field profiles in field-reversed configurations with machine learning in laser-driven ion-beam trace probe

The diagnostic of poloidal magnetic field(B_(p))in field-reversed configuration(FRC),promising for achieving efficient plasma confinement due to its highβ,is a huge challenge because B_(p)is small and reverses around the core region.The laser-driven ion-beam trace probe(LITP)has been proven to diagnose the B_(p)profile in FRCs recently,whereas the existing iterative reconstruction approach cannot handle the measurement errors well.In this work,the machine learning approach,a fast-growing and powerful technology in automation and control,is applied to B_(p)reconstruction in FRCs based on LITP principles and it has a better performance than the previous approach.The machine learning approach achieves a more accurate reconstruction of B_(p)profile when 20%detector errors are considered,15%B_(p)fluctuation is introduced and the size of the detector is remarkably reduced.Therefore,machine learning could be a powerful support for LITP diagnosis of the magnetic field in magnetic confinement fusion devices.

Evaluation of urea breath test as a diagnostic tool for Helicobacter pylori infection in adult dyspeptic patients

In this editorial,we discuss the article in the World Journal of Gastroenterology.The article conducts a meta-analysis of the diagnostic accuracy of the urea breath test(UBT),a non-invasive method for detecting Helicobacter pylori(H.pylori)infection in humans.It is based on radionuclide-labeled urea.Various methods,both invasive and non-invasive,are available for diagnosing H.pylori infection,inclu-ding endoscopy with biopsy,serology for immunoglobulin titers,stool antigen analysis,and UBT.Several guidelines recommend UBTs as the primary choice for diagnosing H.pylori infection and for reexamining after eradication therapy.It is used to be the first choice non-invasive test due to their high accuracy,specificity,rapid results,and simplicity.Moreover,its performance remains unaffected by the distribution of H.pylori in the stomach,allowing a high flow of patients to be tested.Despite its widespread use,the performance characteristics of UBT have been inconsistently described and remain incompletely defined.There are two UBTs available with Food and Drug Administration approval:The 13C and 14C tests.Both tests are affordable and can provide real-time results.Physicians may prefer the 13C test because it is non-radioactive,compared to 14C which uses a radioactive isotope,especially in young children and pregnant women.Although there was heterogeneity among the studies regarding the diagnostic accuracy of both UBTs,13C-UBT consistently outperforms the 14C-UBT.This makes the 13C-UBT the preferred diagnostic approach.Furthermore,the provided findings of the meta-analysis emphasize the significance of precise considerations when choosing urea dosage,assessment timing,and measurement techniques for both the 13C-UBT and 14C-UBT,to enhance diagnostic precision.

Urea breath test for Helicobacter pylori infection in adult dyspeptic patients: A meta-analysis of diagnostic test accuracy

BACKGROUND Helicobacter pylori(H.pylori)infection has been well-established as a significant risk factor for several gastrointestinal disorders.The urea breath test(UBT)has emerged as a leading non-invasive method for detecting H.pylori.Despite numerous studies confirming its substantial accuracy,the reliability of UBT results is often compromised by inherent limitations.These findings underscore the need for a rigorous statistical synthesis to clarify and reconcile the diagnostic accuracy of the UBT for the diagnosis of H.pylori infection.AIM To determine and compare the diagnostic accuracy of 13C-UBT and 14C-UBT for H.pylori infection in adult patients with dyspepsia.METHODS We conducted an independent search of the PubMed/MEDLINE,EMBASE,and Cochrane Central databases until April 2022.Our search included diagnostic accuracy studies that evaluated at least one of the index tests(^(13)C-UBT or ^(14)C-UBT)against a reference standard.We used the QUADAS-2 tool to assess the methodo-logical quality of the studies.We utilized the bivariate random-effects model to calculate sensitivity,specificity,positive and negative test likelihood ratios(LR+and LR-),as well as the diagnostic odds ratio(DOR),and their 95%confidence intervals.We conducted subgroup analyses based on urea dosing,time after urea administration,and assessment technique.To investigate a possible threshold effect,we conducted Spearman correlation analysis,and we generated summary receiver operating characteristic(SROC)curves to assess heterogeneity.Finally,we visually inspected a funnel plot and used Egger’s test to evaluate publication bias.endorsing both as reliable diagnostic tools in clinical practice.CONCLUSION In summary,our study has demonstrated that ^(13)C-UBT has been found to outperform the ^(14)C-UBT,making it the preferred diagnostic approach.Additionally,our results emphasize the significance of carefully considering urea dosage,assessment timing,and measurement techniques for both tests to enhance diagnostic precision.Nevertheless,it is crucial for researchers and clinicians to evaluate the strengths and limitations of our findings before implementing them in practice.

A Review of the Application of Artificial Intelligence in Orthopedic Diseases

In recent years,Artificial Intelligence(AI)has revolutionized people’s lives.AI has long made breakthrough progress in the field of surgery.However,the research on the application of AI in orthopedics is still in the exploratory stage.The paper first introduces the background of AI and orthopedic diseases,addresses the shortcomings of traditional methods in the detection of fractures and orthopedic diseases,draws out the advantages of deep learning and machine learning in image detection,and reviews the latest results of deep learning and machine learning applied to orthopedic image detection in recent years,describing the contributions,strengths and weaknesses,and the direction of the future improvements that can be made in each study.Next,the paper also introduces the difficulties of traditional orthopedic surgery and the roles played by AI in preoperative,intraoperative,and postoperative orthopedic surgery,scientifically discussing the advantages and prospects of AI in orthopedic surgery.Finally,the article discusses the limitations of current research and technology in clinical applications,proposes solutions to the problems,and summarizes and outlines possible future research directions.The main objective of this review is to inform future research and development of AI in orthopedics.

Clinical manifestations,diagnosis and long-term prognosis of adult autoimmune enteropathy:Experience from Peking Union Medical College Hospital

BACKGROUND Autoimmune enteropathy(AIE)is a rare disease whose diagnosis and long-term prognosis remain challenging,especially for adult AIE patients.AIM To improve overall understanding of this disease’s diagnosis and prognosis.METHODS We retrospectively analyzed the clinical,endoscopic and histopathological characteristics and prognoses of 16 adult AIE patients in our tertiary medical center between 2011 and 2023,whose diagnosis was based on the 2007 diagnostic criteria.RESULTS Diarrhea in AIE patients was characterized by secretory diarrhea.The common endoscopic manifestations were edema,villous blunting and mucosal hyperemia in the duodenum and ileum.Villous blunting(100%),deep crypt lymphocytic infiltration(67%),apoptotic bodies(50%),and mild intraepithelial lymphocytosis(69%)were observed in the duodenal biopsies.Moreover,there were other remarkable abnormalities,including reduced or absent goblet cells(duodenum 94%,ileum 62%),reduced or absent Paneth cells(duodenum 94%,ileum 69%)and neutrophil infiltration(duodenum 100%,ileum 69%).Our patients also fulfilled the 2018 diagnostic criteria but did not match the 2022 diagnostic criteria due to undetectable anti-enterocyte antibodies.All patients received glucocorticoid therapy as the initial medication,of which 14/16 patients achieved a clinical response in 5(IQR:3-20)days.Immunosuppressants were administered to 9 patients with indications of steroid dependence(6/9),steroid refractory status(2/9),or intensified maintenance medication(1/9).During the median of 20.5 months of followup,2 patients died from multiple organ failure,and 1 was diagnosed with non-Hodgkin’s lymphoma.The cumulative relapse-free survival rates were 62.5%,55.6%and 37.0%at 6 months,12 months and 48 months,respectively.CONCLUSION Certain histopathological findings,including a decrease or disappearance of goblet and Paneth cells in intestinal biopsies,might be potential diagnostic criteria for adult AIE.The long-term prognosis is still unsatisfactory despite corticosteroid and immunosuppressant medications,which highlights the need for early diagnosis and novel medications.

Development of a diagnostic nomogram for alpha-fetoproteinnegative hepatocellular carcinoma based on serological biomarkers

BACKGROUND Hepatocellular carcinoma(HCC)is the third leading cause of cancer-related death worldwide.Serum biomarkers play an important role in the early diagnosis and prognosis of HCC.Because a certain percentage of HCC patients are negative for alpha-fetoprotein(AFP),the diagnosis of AFP-negative HCC is essential to improve the detection rate of HCC.AIM To establish an effective model for diagnosing AFP-negative HCC based on serum tumour biomarkers.METHODS A total of 180 HCC patients were enrolled in this study.The expression levels of GP73,des-γ-carboxyprothrombin(DCP),CK18-M65,and CK18-M30 were detected by a fully automated chemiluminescence analyser.The variables were selected by logistic regression analysis.Several models were constructed using stepwise backward logistic regression.The performance of the models was compared using the C statistic,integrated discrimination improvement,net reclassification improvement,and calibration curves.The clinical utility of the nomogram was assessed using decision curve analysis(DCA).RESULTS The results showed that the expression levels of GP73,DCP,CK18-M65,and CK18-M30 were significantly greater in AFP-negative HCC patients than in healthy controls(P<0.001).Multivariate logistic regression analysis revealed that GP73,DCP,and CK18-M65 were independent factors for diagnosing AFP-negative HCC.By comparing the diagnostic performance of multiple models,we included GP73 and CK18-M65 as the model variables,and the model had good discrimination ability(area under the curve=0.946)and good goodness of fit.The DCA curves indicated the good clinical utility of the nomogram.CONCLUSION Our study identified GP73 and CK18-M65 as serum biomarkers with certain application value in the diagnosis of AFP-negative HCC.The diagnostic nomogram based on CK18-M65 combined with GP73 demonstrated good performance and effectively identified high-risk groups of patients with HCC.

Bunch-length measurement at a bunch-by-bunch rate based on time–frequency-domain joint analysis techniques and its application

This paper presents a new technique for measuring the bunch length of a high-energy electron beam at a bunch-by-bunch rate in storage rings.This technique uses the time–frequency-domain joint analysis of the bunch signal to obtain bunch-by-bunch and turn-by-turn longitudinal parameters,such as bunch length and synchronous phase.The bunch signal is obtained using a button electrode with a bandwidth of several gigahertz.The data acquisition device was a high-speed digital oscilloscope with a sampling rate of more than 10 GS/s,and the single-shot sampling data buffer covered thousands of turns.The bunch-length and synchronous phase information were extracted via offline calculations using Python scripts.The calibration coefficient of the system was determined using a commercial streak camera.Moreover,this technique was tested on two different storage rings and successfully captured various longitudinal transient processes during the harmonic cavity debugging process at the Shanghai Synchrotron Radiation Facility(SSRF),and longitudinal instabilities were observed during the single-bunch accumulation process at Hefei Light Source(HLS).For Gaussian-distribution bunches,the uncertainty of the bunch phase obtained using this technique was better than 0.2 ps,and the bunch-length uncertainty was better than 1 ps.The dynamic range exceeded 10 ms.This technology is a powerful and versatile beam diagnostic tool that can be conveniently deployed in high-energy electron storage rings.

Diagnostic Accuracy of Computerized Bowel Sound Analysis with Non-Invasive Devices for Irritable Bowel Syndrome:A Systematic Review and Meta-Analysis

Objective To assess the diagnostic accuracy of bowel sound analysis for irritable bowel syndrome(IBS)with a systematic review and meta-analysis.Methods We searched MEDLINE,Embase,the Cochrane Library,Web of Science,and IEEE Xplore databases until September 2023.Cross-sectional and case-control studies on diagnostic accuracy of bowel sound analysis for IBS were identified.We estimated the pooled sensitivity,specificity,positive likelihood ratio,negative likeli-hood ratio,and diagnostic odds ratio with a 95% confidence interval(CI),and plotted a summary receiver operat-ing characteristic curve and evaluated the area under the curve.Results Four studies were included.The pooled diagnostic sensitivity,specificity,positive likelihood ratio,nega-tive likelihood ratio,and diagnostic odds ratio were 0.94(95%CI,0.87‒0.97),0.89(95%CI,0.81‒0.94),8.43(95%CI,4.81‒14.78),0.07(95%CI,0.03‒0.15),and 118.86(95%CI,44.18‒319.75),respectively,with an area under the curve of 0.97(95%CI,0.95‒0.98).Conclusions Computerized bowel sound analysis is a promising tool for IBS.However,limited high-quality data make the results'validity and applicability questionable.There is a need for more diagnostic test accuracy studies and better wearable devices for monitoring and analysis of IBS.

Integrated wellbore-reservoir-geomechanics modeling for enhanced interpretation of distributed fiber-optic strain sensing data in hydraulicfracture analysis

Fiber-optic distributed strain sensing(FO-DSS)has been successful in monitoring strain changes along horizontal wellbores in hydraulically fractured reservoirs.However,the mechanism driving the various FO-DSS responses associated with near-wellbore hydraulic fracture properties is still unclear.To address this knowledge gap,we use coupled wellbore-reservoir-geomechanics simulations to study measured strain-change behavior and infer hydraulic fracture characteristics.The crossflow among fractures is captured through explicit modeling of the transient wellbore flow.In addition,local grid refinement is applied to accurately capture strain changes along the fiber.A Base Case model was designed with four fractures of varying properties,simulating strain change signals when the production well is shut-in for 10 d after 240 d of production and reopened for 2 d.Strain-pressure plots for different fracture clusters were used to gain insights into inferring fracture properties using DSS data.When comparing the model with and without the wellbore,distinct strain change signals were observed,emphasizing the importance of incorporating the wellbore in FO-DSS modeling.The effects of fracture spacing and matrix permeability on strain change signals were thoroughly investigated.The results of our numerical study can improve the understanding of the relation between DSS signals and fracture hydraulic properties,thus maximizing the value of the dataset for fracture diagnostics and characterization.

Intelligent diagnosis of retinal vein occlusion based on color fundus photographs

AIM:To develop an artificial intelligence(AI)diagnosis model based on deep learning(DL)algorithm to diagnose different types of retinal vein occlusion(RVO)by recognizing color fundus photographs(CFPs).METHODS:Totally 914 CFPs of healthy people and patients with RVO were collected as experimental data sets,and used to train,verify and test the diagnostic model of RVO.All the images were divided into four categories[normal,central retinal vein occlusion(CRVO),branch retinal vein occlusion(BRVO),and macular retinal vein occlusion(MRVO)]by three fundus disease experts.Swin Transformer was used to build the RVO diagnosis model,and different types of RVO diagnosis experiments were conducted.The model’s performance was compared to that of the experts.RESULTS:The accuracy of the model in the diagnosis of normal,CRVO,BRVO,and MRVO reached 1.000,0.978,0.957,and 0.978;the specificity reached 1.000,0.986,0.982,and 0.976;the sensitivity reached 1.000,0.955,0.917,and 1.000;the F1-Sore reached 1.000,0.9550.943,and 0.887 respectively.In addition,the area under curve of normal,CRVO,BRVO,and MRVO diagnosed by the diagnostic model were 1.000,0.900,0.959 and 0.970,respectively.The diagnostic results were highly consistent with those of fundus disease experts,and the diagnostic performance was superior.CONCLUSION:The diagnostic model developed in this study can well diagnose different types of RVO,effectively relieve the work pressure of clinicians,and provide help for the follow-up clinical diagnosis and treatment of RVO patients.

Salivary metabolites are promising noninvasive biomarkers of druginduced liver injury

BACKGROUND Drug-induced liver injury(DILI)is one of the most common adverse events of medication use,and its incidence is increasing.However,early detection of DILI is a crucial challenge due to a lack of biomarkers and noninvasive tests.AIM To identify salivary metabolic biomarkers of DILI for the future development of noninvasive diagnostic tools.METHODS Saliva samples from 31 DILI patients and 35 healthy controls(HCs)were subjected to untargeted metabolomics using ultrahigh-pressure liquid chromatography coupled with tandem mass spectrometry.Subsequent analyses,including partial least squares-discriminant analysis modeling,t tests and weighted metabolite coexpression network analysis(WMCNA),were conducted to identify key differentially expressed metabolites(DEMs)and metabolite sets.Furthermore we utilized least absolute shrinkage and selection operato and random fores analyses for biomarker prediction.The use of each metabolite and metabolite set to detect DILI was evaluated with area under the receiver operating characteristic curves.RESULTS We found 247 differentially expressed salivary metabolites between the DILI group and the HC group.Using WMCNA,we identified a set of 8 DEMs closely related to liver injury for further prediction testing.Interestingly,the distinct separation of DILI patients and HCs was achieved with five metabolites,namely,12-hydroxydodecanoic acid,3-hydroxydecanoic acid,tetradecanedioic acid,hypoxanthine,and inosine(area under the curve:0.733-1).CONCLUSION Salivary metabolomics revealed previously unreported metabolic alterations and diagnostic biomarkers in the saliva of DILI patients.Our study may provide a potentially feasible and noninvasive diagnostic method for DILI,but further validation is needed.

Explainable Conformer Network for Detection of COVID-19 Pneumonia from Chest CT Scan: From Concepts toward Clinical Explainability

The early implementation of treatment therapies necessitates the swift and precise identification of COVID-19 pneumonia by the analysis of chest CT scans.This study aims to investigate the indispensable need for precise and interpretable diagnostic tools for improving clinical decision-making for COVID-19 diagnosis.This paper proposes a novel deep learning approach,called Conformer Network,for explainable discrimination of viral pneumonia depending on the lung Region of Infections(ROI)within a single modality radiographic CT scan.Firstly,an efficient U-shaped transformer network is integrated for lung image segmentation.Then,a robust transfer learning technique is introduced to design a robust feature extractor based on pre-trained lightweight Big Transfer(BiT-L)and finetuned on medical data to effectively learn the patterns of infection in the input image.Secondly,this work presents a visual explanation method to guarantee clinical explainability for decisions made by Conformer Network.Experimental evaluation of real-world CT data demonstrated that the diagnostic accuracy of ourmodel outperforms cutting-edge studies with statistical significance.The Conformer Network achieves 97.40% of detection accuracy under cross-validation settings.Our model not only achieves high sensitivity and specificity but also affords visualizations of salient features contributing to each classification decision,enhancing the overall transparency and trustworthiness of our model.The findings provide obvious implications for the ability of our model to empower clinical staff by generating transparent intuitions about the features driving diagnostic decisions.