Detachable string magnetically controlled capsule endoscopy for the noninvasive diagnosis of esophageal diseases:A prospective,blind clinical study

BACKGROUND Traditional esophagogastroduodenoscopy(EGD),an invasive examination method,can cause discomfort and pain in patients.In contrast,magnetically controlled capsule endoscopy(MCE),a noninvasive method,is being applied for the detection of stomach and small intestinal diseases,but its application in treating esophageal diseases is not widespread.AIM To evaluate the safety and efficacy of detachable string MCE(ds-MCE)for the diagnosis of esophageal diseases.METHODS Fifty patients who had been diagnosed with esophageal diseases were pros-pectively recruited for this clinical study and underwent ds-MCE and conven-tional EGD.The primary endpoints included the sensitivity,specificity,positive predictive value,negative predictive value,and diagnostic accuracy of ds-MCE for patients with esophageal diseases.The secondary endpoints consisted of visualizing the esophageal and dentate lines,as well as the subjects'tolerance of the procedure.RESULTS Using EGD as the gold standard,the sensitivity,specificity,positive predictive value,negative predictive value,and diagnostic accuracy of ds-MCE for esophageal disease detection were 85.71%,86.21%,81.82%,89.29%,and 86%,respectively.ds-MCE was more comfortable and convenient than EGD was,with 80%of patients feeling that ds-MCE examination was very comfortable or comfortable and 50%of patients believing that detachable string v examination was very convenient.CONCLUSION This study revealed that ds-MCE has the same diagnostic effects as traditional EGD for esophageal diseases and is more comfortable and convenient than EGD,providing a novel noninvasive method for treating esophageal diseases.

Association of age at diagnosis of diabetes with subsequent risk of age-related ocular diseases and vision acuity

BACKGROUND The importance of age on the development of ocular conditions has been reported by numerous studies.Diabetes may have different associations with different stages of ocular conditions,and the duration of diabetes may affect the development of diabetic eye disease.While there is a dose-response relationship between the age at diagnosis of diabetes and the risk of cardiovascular disease and mortality,whether the age at diagnosis of diabetes is associated with incident ocular conditions remains to be explored.It is unclear which types of diabetes are more predictive of ocular conditions.AIM To examine associations between the age of diabetes diagnosis and the incidence of cataract,glaucoma,age-related macular degeneration(AMD),and vision acuity.METHODS Our analysis was using the UK Biobank.The cohort included 8709 diabetic participants and 17418 controls for ocular condition analysis,and 6689 diabetic participants and 13378 controls for vision analysis.Ocular diseases were identified using inpatient records until January 2021.Vision acuity was assessed using a chart.RESULTS During a median follow-up of 11.0 years,3874,665,and 616 new cases of cataract,glaucoma,and AMD,respectively,were identified.A stronger association between diabetes and incident ocular conditions was observed where diabetes was diagnosed at a younger age.Individuals with type 2 diabetes(T2D)diagnosed at<45 years[HR(95%CI):2.71(1.49-4.93)],45-49 years[2.57(1.17-5.65)],50-54 years[1.85(1.13-3.04)],or 50-59 years of age[1.53(1.00-2.34)]had a higher risk of AMD independent of glycated haemoglobin.T2D diagnosed<45 years[HR(95%CI):2.18(1.71-2.79)],45-49 years[1.54(1.19-2.01)],50-54 years[1.60(1.31-1.96)],or 55-59 years of age[1.21(1.02-1.43)]was associated with an increased cataract risk.T2D diagnosed<45 years of age only was associated with an increased risk of glaucoma[HR(95%CI):1.76(1.00-3.12)].HRs(95%CIs)for AMD,cataract,and glaucoma associated with type 1 diabetes(T1D)were 4.12(1.99-8.53),2.95(2.17-4.02),and 2.40(1.09-5.31),respectively.In multivariable-adjusted analysis,individuals with T2D diagnosed<45 years of age[β95%CI:0.025(0.009,0.040)]had a larger increase in LogMAR.Theβ(95%CI)for LogMAR associated with T1D was 0.044(0.014,0.073).CONCLUSION The younger age at the diagnosis of diabetes is associated with a larger relative risk of incident ocular diseases and greater vision loss.

Differential diagnosis of Crohn’s disease and intestinal tuberculosis based on ATR-FTIR spectroscopy combined with machine learning

BACKGROUND Crohn’s disease(CD)is often misdiagnosed as intestinal tuberculosis(ITB).However,the treatment and prognosis of these two diseases are dramatically different.Therefore,it is important to develop a method to identify CD and ITB with high accuracy,specificity,and speed.AIM To develop a method to identify CD and ITB with high accuracy,specificity,and speed.METHODS A total of 72 paraffin wax-embedded tissue sections were pathologically and clinically diagnosed as CD or ITB.Paraffin wax-embedded tissue sections were attached to a metal coating and measured using attenuated total reflectance fourier transform infrared spectroscopy at mid-infrared wavelengths combined with XGBoost for differential diagnosis.RESULTS The results showed that the paraffin wax-embedded specimens of CD and ITB were significantly different in their spectral signals at 1074 cm^(-1) and 1234 cm^(-1) bands,and the differential diagnosis model based on spectral characteristics combined with machine learning showed accuracy,specificity,and sensitivity of 91.84%,92.59%,and 90.90%,respectively,for the differential diagnosis of CD and ITB.CONCLUSION Information on the mid-infrared region can reveal the different histological components of CD and ITB at the molecular level,and spectral analysis combined with machine learning to establish a diagnostic model is expected to become a new method for the differential diagnosis of CD and ITB.

Artificial intelligence in individualized retinal disease management

Owing to the rapid development of modern computer technologies,artificial intelligence(AI)has emerged as an essential instrument for intelligent analysis across a range of fields.AI has been proven to be highly effective in ophthalmology,where it is frequently used for identifying,diagnosing,and typing retinal diseases.An increasing number of researchers have begun to comprehensively map patients’retinal diseases using AI,which has made individualized clinical prediction and treatment possible.These include prognostic improvement,risk prediction,progression assessment,and interventional therapies for retinal diseases.Researchers have used a range of input data methods to increase the accuracy and dependability of the results,including the use of tabular,textual,or image-based input data.They also combined the analyses of multiple types of input data.To give ophthalmologists access to precise,individualized,and high-quality treatment strategies that will further optimize treatment outcomes,this review summarizes the latest findings in AI research related to the prediction and guidance of clinical diagnosis and treatment of retinal diseases.

Retinal capillary plexus in Parkinson’s disease using optical coherence tomography angiography

AIM:To evaluate the alterations of the retinal microvasculature and foveal avascular zone in patients with Parkinson’s disease(PD)using optical coherence tomography angiography(OCT-A).METHODS:A retrospective study of PD patients examined in the Ophthalmology Department of the General Hospital of Athens,“Georgios Gennimatas”from March 2021 to March 2022 was conducted.Totally 44 patients with PD were included and 18 healthy controls were examined,hence a total of 124 eyes were enrolled in the study.The foveal and parafoveal superficial and deep capillary plexus vascular density(fSCP-VD,fDCP-VD,pSCP-VD,pDCP-CD)and foveal avascular zone(FAZ)were quantified with OCTA.Optical coherence tomography(OCT)was used to measure macular thickness.Our statistical analysis was conducted by using a mixed effect linear regression model.RESULTS:After adjustment for age and gender,the mean parafoveal superficial capillary plexus vascular density(pSCP-VD)and mean parafoveal deep capillary plexus vascular density(pDCP-VD)were significantly decreased in individuals with PD(P<0.001 in both)by-2.35(95%CI-3.3,-1.45)and-7.5(95%CI-10.4,-4.6)respectively.fSCP-VD and fDCP-VD didn’t approach statistical significance.The FAZ area and perimeter were significantly decreased(P<0.001 in both)by-0.1 mm^(2)(95%CI-0.13,-0.07)and-0.49 mm^(2)(95%CI-0.66,-0.32)respectively.Circularity didn’t approach statistical significance.Central retinal thickness(CRT)was significantly decreased in individuals with PD(P<0.001)by-23.1μm(95%CI-30.2,-16)and temporal retinal thickness(TRT)was decreased(P=0.025)by-11μm(95%CI-22,-1.5)while nasal retinal thickness(NRT)only approached statistical significance(P=0.066).CONCLUSION:The mean pSCP-VD,pDCP-VD,CRT and TRT are significantly decreased and FAZ is altered in individuals with PD.These findings can be potentially used as biomarkers for the diagnosis and evaluation of early PD.

Association of autoimmune thyroid disease with type 1 diabetes mellitus and its ultrasonic diagnosis and management

As a common hyperglycemic disease,type 1 diabetes mellitus(T1DM)is a complicated disorder that requires a lifelong insulin supply due to the immunemediated destruction of pancreaticβcells.Although it is an organ-specific autoimmune disorder,T1DM is often associated with multiple other autoimmune disorders.The most prevalent concomitant autoimmune disorder occurring in T1DM is autoimmune thyroid disease(AITD),which mainly exhibits two extremes of phenotypes:hyperthyroidism[Graves'disease(GD)]and hypothyroidism[Hashimoto's thyroiditis,(HT)].However,the presence of comorbid AITD may negatively affect metabolic management in T1DM patients and thereby may increase the risk for potential diabetes-related complications.Thus,routine screening of thyroid function has been recommended when T1DM is diagnosed.Here,first,we summarize current knowledge regarding the etiology and pathogenesis mechanisms of both diseases.Subsequently,an updated review of the association between T1DM and AITD is offered.Finally,we provide a relatively detailed review focusing on the application of thyroid ultrasonography in diagnosing and managing HT and GD,suggesting its critical role in the timely and accurate diagnosis of AITD in T1DM.

Intestinal Behçet's disease: A review of clinical diagnosis and treatment

Behçet's disease(BD)is a chronic inflammatory disorder prone to frequent re-currences,with a high predilection for intestinal involvement.However,the ef-ficacy and long-term effects of surgical treatment for intestinal BD are unknown.In the current issue of World J Gastrointest Surg,Park et al conducted a retrospec-tive analysis of 31 patients with intestinal BD who received surgical treatment.They found that elevated C-reactive protein levels and emergency surgery were poor prognostic factors for postoperative recurrence,emphasizing the adverse impact of severe inflammation on the prognosis of patients with intestinal BD.This work has clinical significance for evaluating the postoperative condition of intestinal BD.The editorial attempts to summarize the clinical diagnosis and treatment of intestinal BD,focusing on the impact of adverse factors on surgical outcomes.We hope this review will facilitate more precise postoperative management of patients with intestinal BD by clinicians.

Stem cell therapy in retinal diseases

Alteration of the outer retina leads to various diseases such as age-related macular degeneration or retinitis pigmentosa characterized by decreased visual acuity and ultimately blindness.Despite intensive research in the field of retinal disorders,there is currently no curative treatment.Several therapeutic approaches such as cell-based replacement and gene therapies are currently in development.In the context of cell-based therapies,different cell sources such as embryonic stem cells,induced pluripotent stem cells,or multipotent stem cells can be used for transplantation.In the vast majority of human clinical trials,retinal pigment epithelial cells and photoreceptors are the cell types considered for replacement cell therapies.In this review,we summarize the progress made in stem cell therapies ranging from the pre-clinical studies to clinical trials for retinal disease.

Convolutional Neural Network-Based Classificationof Multiple Retinal Diseases Using Fundus Images

Use of deep learning algorithms for the investigation and analysis of medical images has emerged as a powerful technique.The increase in retinal dis-eases is alarming as it may lead to permanent blindness if left untreated.Automa-tion of the diagnosis process of retinal diseases not only assists ophthalmologists in correct decision-making but saves time also.Several researchers have worked on automated retinal disease classification but restricted either to hand-crafted fea-ture selection or binary classification.This paper presents a deep learning-based approach for the automated classification of multiple retinal diseases using fundus images.For this research,the data has been collected and combined from three distinct sources.The images are preprocessed for enhancing the details.Six layers of the convolutional neural network(CNN)are used for the automated feature extraction and classification of 20 retinal diseases.It is observed that the results are reliant on the number of classes.For binary classification(healthy vs.unhealthy),up to 100%accuracy has been achieved.When 16 classes are used(treating stages of a disease as a single class),93.3%accuracy,92%sensitivity and 93%specificity have been obtained respectively.For 20 classes(treating stages of the disease as separate classes),the accuracy,sensitivity and specificity have dropped to 92.4%,92%and 92%respectively.

Automated Classification of Inherited Retinal Diseases in Optical Coherence Tomography Images Using Few-shot Learning

Objective To develop a few-shot learning(FSL) approach for classifying optical coherence tomography(OCT) images in patients with inherited retinal disorders(IRDs).Methods In this study, an FSL model based on a student–teacher learning framework was designed to classify images. 2,317 images from 189 participants were included. Of these, 1,126 images revealed IRDs, 533 were normal samples, and 658 were control samples.Results The FSL model achieved a total accuracy of 0.974–0.983, total sensitivity of 0.934–0.957, total specificity of 0.984–0.990, and total F1 score of 0.935–0.957, which were superior to the total accuracy of the baseline model of 0.943–0.954, total sensitivity of 0.866–0.886, total specificity of 0.962–0.971,and total F1 score of 0.859–0.885. The performance of most subclassifications also exhibited advantages. Moreover, the FSL model had a higher area under curves(AUC) of the receiver operating characteristic(ROC) curves in most subclassifications.Conclusion This study demonstrates the effective use of the FSL model for the classification of OCT images from patients with IRDs, normal, and control participants with a smaller volume of data. The general principle and similar network architectures can also be applied to other retinal diseases with a low prevalence.

Accuracy improvement for classifying retinal OCT images by diseases using deep learning-based selective denoising approach

In ophthalmology,retinal optical coherence tomography(OCT)images with noticeable structural features help identify human eyes as healthy or diseased.The recently hot arti ficial intelligence(AI)realized this recognition process automatically.However,speckle noise in the original retinal OCT image reduces the accuracy of disease classi fication.This study presents a timesaving approach based on deep learning to improve classi fication accuracy by removing the noise from the original dataset.Firstly,four pre-trained convolutional neural networks(CNNs)from the ImageNet Large Scale Visual Recognition Challenge(ILSVRC)were trained to classify the original images into two categories:The noise reduction required(NRR)and the noise-free(NF)images.Among the CNNs,VGG19 BN performed best with 98%accuracy and 99%recall.Then,we used the block-matching and 3D filtering(BM3D)algorithm to denoise the NRR images.Those noise-removed NRR and the NF images form the processed dataset.The quality of images in the dataset is prominently ameliorated after denoising,which is valid to improve the models'performance.The original and processed datasets were tested on the four pre-trained CNNs to evaluate the effectiveness of our proposed approach.We have compared the CNNs,and the results show the performance of the CNNs trained with the processed dataset is improved by an average of 2.04%,5.19%,and 5.10%under overall accuracy(OA),Macro F1-score,and Micro F1-score,respectively.Especially for DenseNet161,the OA is improved to 98.14%.Our proposed method demonstrates its effectiveness in improving classi fication accuracy and opens a new solution to reduce denoising time-consuming for large datasets.

Hybrid Deep Learning Method for Diagnosis of Cucurbita Leaf Diseases

In agricultural engineering,the main challenge is on methodologies used for disease detection.The manual methods depend on the experience of the personal.Due to large variation in environmental condition,disease diagnosis and classification becomes a challenging task.Apart from the disease,the leaves are affected by climate changes which is hard for the image processing method to discriminate the disease from the other background.In Cucurbita gourd family,the disease severity examination of leaf samples through computer vision,and deep learning methodologies have gained popularity in recent years.In this paper,a hybrid method based on Convolutional Neural Network(CNN)is proposed for automatic pumpkin leaf image classification.The Proposed Denoising and deep Convolutional Neural Network(CNN)method enhances the Pumpkin Leaf Pre-processing and diagnosis.Real time data base was used for training and testing of the proposed work.Investigation on existing pre-trained network Alexnet and googlenet was investigated is done to evaluate the performance of the pro-posed method.The system and computer simulations were performed using Matlab tool.

Complications of Ziv-Aflibercept in Choroidal and Retinal Vascular Diseases

Introduction: The modern ophthalmology trends are changing rapidly every day with the introduction of much newer studies and research. Numerous anti-vascular endothelial growth factors (VEGF) are utilized as the mainstay in the treatment of intraocular vascular pathologies. The rationale of this study is to add to the literature regarding the safety and efficacy profile of the ziv-aflibercept as there is insubstantial data in patients with intraocular vascular pathologies being treated with this injection with prime focus on the complications of the injection. Materials and Methods: A prospective observational study was conducted at Opthalmology Department, Lahore General Hospital, Lahore between 14 August 2018 and 23 December 2019. Patients with choroidal and retinal vascular diseases like diabetic macular edema (DME), age-related macular degeneration (AMD) and retinal vein occlusion (RVO) who had no active infection of eye and had no history of myocardial infarction or cerebrovascular accident were added in this study. Results: Best-corrected visual acuity was significantly improved at 4, 8, and 12 weeks as compared to the baseline (p Conclusion: The use of ziv-aflibercept injection via intravitreal route under aseptic conditions for choroidal and retinal vascular diseases is effective as well as safe with mild and treatable ocular side effects.

Intelligent Deep Learning Based Multi-Retinal Disease Diagnosis and Classification Framework

In past decades,retinal diseases have become more common and affect people of all age grounds over the globe.For examining retinal eye disease,an artificial intelligence(AI)based multilabel classification model is needed for automated diagnosis.To analyze the retinal malady,the system proposes a multiclass and multi-label arrangement method.Therefore,the classification frameworks based on features are explicitly described by ophthalmologists under the application of domain knowledge,which tends to be time-consuming,vulnerable generalization ability,and unfeasible in massive datasets.Therefore,the automated diagnosis of multi-retinal diseases becomes essential,which can be solved by the deep learning(DL)models.With this motivation,this paper presents an intelligent deep learningbased multi-retinal disease diagnosis(IDL-MRDD)framework using fundus images.The proposed model aims to classify the color fundus images into different classes namely AMD,DR,Glaucoma,Hypertensive Retinopathy,Normal,Others,and Pathological Myopia.Besides,the artificial flora algorithm with Shannon’s function(AFA-SF)basedmulti-level thresholding technique is employed for image segmentation and thereby the infected regions can be properly detected.In addition,SqueezeNet based feature extractor is employed to generate a collection of feature vectors.Finally,the stacked sparse Autoencoder(SSAE)model is applied as a classifier to distinguish the input images into distinct retinal diseases.The efficacy of the IDL-MRDD technique is carried out on a benchmark multi-retinal disease dataset,comprising data instances from different classes.The experimental values pointed out the superior outcome over the existing techniques with the maximum accuracy of 0.963.

Emerging roles of exosomes in oral diseases progression

Oral diseases, such as periodontitis, salivary gland diseases, and oral cancers, significantly challenge health conditions due to their detrimental effects on patient's digestive functions, pronunciation, and esthetic demands. Delayed diagnosis and non-targeted treatment profoundly influence patients' prognosis and quality of life. The exploration of innovative approaches for early detection and precise treatment represents a promising frontier in oral medicine.

Pyrroloquinoline quinone:a potential neuroprotective compound for neurodegenerative diseases targeting metabolism

Pyrroloquinoline quinone is a quinone described as a cofactor for many bacterial dehydrogenases and is reported to exert an effect on metabolism in mammalian cells/tissues.Pyrroloquinoline quinone is present in the diet being available in foodstuffs,conferring the potential of this compound to be supplemented by dietary administration.Pyrroloquinoline quinone’s nutritional role in mammalian health is supported by the extensive deficits in reproduction,growth,and immunity resulting from the dietary absence of pyrroloquinoline quinone,and as such,pyrroloquinoline quinone has been considered as a“new vitamin.”Although the classification of pyrroloquinoline quinone as a vitamin needs to be properly established,the wide range of benefits for health provided has been reported in many studies.In this respect,pyrroloquinoline quinone seems to be particularly involved in regulating cell signaling pathways that promote metabolic and mitochondrial processes in many experimental contexts,thus dictating the rationale to consider pyrroloquinoline quinone as a vital compound for mammalian life.Through the regulation of different metabolic mechanisms,pyrroloquinoline quinone may improve clinical deficits where dysfunctional metabolism and mitochondrial activity contribute to induce cell damage and death.Pyrroloquinoline quinone has been demonstrated to have neuroprotective properties in different experimental models of neurodegeneration,although the link between pyrroloquinoline quinone-promoted metabolism and improved neuronal viability in some of such contexts is still to be fully elucidated.Here,we review the general properties of pyrroloquinoline quinone and its capacity to modulate metabolic and mitochondrial mechanisms in physiological contexts.In addition,we analyze the neuroprotective properties of pyrroloquinoline quinone in different neurodegenerative conditions and consider future perspectives for pyrroloquinoline quinone’s potential in health and disease.

Beyond dentistry:could prevention and screening for neurodegenerative diseases start in the dental office?

The differential diagnosis of neurodegenerative diseases is complex and relies on clinical assessment,biomarker levels in cerebrospinal fluid,neuroimaging and neuropsychological assessment.The efforts of the scientific community are focused on two aspects:a)the discovery of minimally invasive biomarkers;b)the discovery of early biomarkers that can predict the progression to clinical disease in the presymptomatic stage of a disease.Considering the impact of the number of patients affected by chronic neurodegenerative diseases on public health expenditures,early diagnosis seems to be a primary need of our society.

Prognostic Nutritional Index Associates with the Severity of Silicosis:A Study from a Tertiary Class A Prevention and Treatment Institute for Occupational Diseases in China

Pneumoconiosis is a group of heterogeneous fibrotic lung diseases caused by inorganic mineral dust and includes coal workers’pneumoconiosis and silicosis.Silicosis involves diffuse or nodular interstitial pulmonary fibrosis caused by exposure to asbestos or silica dust.China is thought to have the highest number of silicosis cases,with 6,000 new cases reported annually^([1]).Currently,the clinical diagnosis and monitoring of silicosis relies mainly on a history of occupational exposure and radiological abnormalities^([2]).Therefore,determining further indicators is crucial to reflect the severity of silicosis.

Alzheimer’s visual variant: a report of a diagnosis easily missed on ophthalmic examination

Dear Editor,We present a case of posterior cortical atrophy(PCA),which is a syndromic entity caused by different neurodegenerative diseases,mainly Alzheimer’s disease,but it has been described in several other entities.Its frequency is reported to be as high as 5%of all cases;however,it remains widely under-recognized among ophthalmologists and optometrists due to scarce reports in visual health-related journals[1].

Indirubin alleviates retinal neurodegeneration through the regulation of PI3K/AKT signaling

Retinal neurodegenerative disease is a leading cause of blindness among the elderly in developed countries,including glaucoma,diabetic retinopathy,traumatic optic neuropathy and optic neuritis,etc.The current clinical treatment is not very effective.We investigated indirubin,one of the main bioactive components of the traditional Chinese medicine Danggui Longhui Pill,in the present study for its role in retinal neurodegeneration.Indirubin exhibited no detectable tissue toxicity in vivo or cytotoxicity in vitro.Moreover,indirubin improved visual function and ameliorated retinal neurodegeneration in mice after optic nerve crush injury in vivo.Furthermore,indirubin reduced the apoptosis of retinal ganglion cells induced by oxidative stress in vitro.In addition,indirubin significantly suppressed the increased production of intracellular reactive oxygen species and the decreased activity of superoxide dismutase induced by oxidative stress.Mechanically,indirubin played a neuroprotective role by regulating the PI3K/AKT/BAD/BCL-2 signaling.In conclusion,indirubin protected retinal ganglion cells from oxidative damage and alleviated retinal neurodegeneration induced by optic nerve crush injury.The present study provides a potential therapeutic medicine for retinal neurodegenerative diseases.