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.

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.

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.

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.

Lactate metabolism in neurodegenerative diseases

Lactate,a byproduct of glycolysis,was thought to be a metabolic waste until the discovery of the Warburg effect.Lactate not only functions as a metabolic substrate to provide energy but can also function as a signaling molecule to modulate cellular functions under pathophysiological conditions.The Astrocyte-Neuron Lactate Shuttle has cla rified that lactate plays a pivotal role in the central nervous system.Moreover,protein lactylation highlights the novel role of lactate in regulating transcription,cellular functions,and disease development.This review summarizes the recent advances in lactate metabolism and its role in neurodegenerative diseases,thus providing optimal pers pectives for future research.

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.

Applications of deep learning for detecting ophthalmic diseases with ultrawide-field fundus

AIM:To summarize the application of deep learning in detecting ophthalmic disease with ultrawide-field fundus images and analyze the advantages,limitations,and possible solutions common to all tasks.METHODS:We searched three academic databases,including PubMed,Web of Science,and Ovid,with the date of August 2022.We matched and screened according to the target keywords and publication year and retrieved a total of 4358 research papers according to the keywords,of which 23 studies were retrieved on applying deep learning in diagnosing ophthalmic disease with ultrawide-field images.RESULTS:Deep learning in ultrawide-field images can detect various ophthalmic diseases and achieve great performance,including diabetic retinopathy,glaucoma,age-related macular degeneration,retinal vein occlusions,retinal detachment,and other peripheral retinal diseases.Compared to fundus images,the ultrawide-field fundus scanning laser ophthalmoscopy enables the capture of the ocular fundus up to 200°in a single exposure,which can observe more areas of the retina.CONCLUSION:The combination of ultrawide-field fundus images and artificial intelligence will achieve great performance in diagnosing multiple ophthalmic diseases in the future.

Microglia lactylation in relation to central nervous system diseases

The development of neurodegenerative diseases is closely related to the disruption of central nervous system homeostasis.Microglia,as innate immune cells,play important roles in the maintenance of central nervous system homeostasis,injury response,and neurodegenerative diseases.Lactate has been considered a metabolic waste product,but recent studies are revealing ever more of the physiological functions of lactate.Lactylation is an important pathway in lactate function and is involved in glycolysis-related functions,macrophage polarization,neuromodulation,and angiogenesis and has also been implicated in the development of various diseases.This review provides an overview of the lactate metabolic and homeostatic regulatory processes involved in microglia lactylation,histone versus non-histone lactylation,and therapeutic approaches targeting lactate.Finally,we summarize the current research on microglia lactylation in central nervous system diseases.A deeper understanding of the metabolic regulatory mechanisms of microglia lactylation will provide more options for the treatment of central nervous system diseases.

Polyoxidovanadates a new therapeutic alternative for neurodegenerative and aging diseases

Aging is a natural phenomenon characterized by a progressive decline in physiological integrity,leading to a deterioration of cognitive function and increasing the risk of suffering from chronic-degenerative diseases,including cardiovascular diseases,osteoporosis,cancer,diabetes,and neurodegeneration.Aging is considered the major risk factor for Parkinson’s and Alzheimer’s disease develops.Likewise,diabetes and insulin resistance constitute additional risk factors for developing neurodegenerative disorders.Currently,no treatment can effectively reverse these neurodegenerative pathologies.However,some antidiabetic drugs have opened the possibility of being used against neurodegenerative processes.In the previous framework,Vanadium species have demonstrated a notable antidiabetic effect.Our research group evaluated polyoxidovanadates such as decavanadate and metforminium-decavanadate with preventive and corrective activity on neurodegeneration in brain-specific areas from rats with metabolic syndrome.The results suggest that these polyoxidovanadates induce neuronal and cognitive restoration mechanisms.This review aims to describe the therapeutic potential of polyoxidovanadates as insulin-enhancer agents in the brain,constituting a therapeutic alternative for aging and neurodegenerative diseases.

Rethinking neurodegenerative diseases:neurometabolic concept linking lipid oxidation to diseases in the central nervous system

Currently,there is a lack of effective medicines capable of halting or reve rsing the progression of neurodegenerative disorde rs,including amyotrophic lateral sclerosis,Parkinson s disease,multiple sclerosis,or Alzheimer s disease.Given the unmet medical need,it is necessary to reevaluate the existing para digms of how to to rget these diseases.When considering neurodegenerative diseases from a systemic neurometabolic perspective,it becomes possible to explain the shared pathological features.This innovative approach presented in this paper draws upon exte nsive research conducted by the authors and researchers worldwide.In this review,we highlight the importance of metabolic mitochondrial dysfunction in the context of neurodegenerative diseases.We provide an overview of the risk factors associated with developing neurodegenerative disorders,including genetic,epigenetic,and environmental fa ctors.Additionally,we examine pathological mechanisms implicated in these diseases such as oxidative stress,accumulation of misfolded proteins,inflammation,demyelination,death of neurons,insulin resistance,dysbiosis,and neurotransmitter disturbances.Finally,we outline a proposal for the restoration of mitochondrial metabolism,a crucial aspect that may hold the key to facilitating curative therapeutic interventions for neurodegenerative disorders in forthcoming advancements.

Brain organoids are new tool for drug screening of neurological diseases

At the level of in vitro drug screening,the development of a phenotypic analysis system with highcontent screening at the core provides a strong platform to support high-throughput drug screening.There are few systematic reports on brain organoids,as a new three-dimensional in vitro model,in terms of model stability,key phenotypic fingerprint,and drug screening schemes,and particula rly rega rding the development of screening strategies for massive numbers of traditional Chinese medicine monomers.This paper reviews the development of brain organoids and the advantages of brain organoids over induced neurons or cells in simulated diseases.The paper also highlights the prospects from model stability,induction criteria of brain organoids,and the screening schemes of brain organoids based on the characteristics of brain organoids and the application and development of a high-content screening system.

Expert Consensus on the Diagnosis and Treatment of Anticancer Drug-Induced Interstitial Lung Disease

Drug-induced interstitial lung disease(DILD)is the most common pulmonary adverse event of anticancer drugs.In recent years,the incidence of anticancer DILD has gradually increased with the rapid development of novel anticancer agents.Due to the diverse clinical manifestations and the lack of specific diagnostic criteria,DILD is difficult to diagnose and may even become fatal if not treated properly.Herein,a multidisciplinary group of experts from oncology,respiratory,imaging,pharmacology,pathology,and radiology departments in China has reached the“expert consensus on the diagnosis and treatment of anticancer DILD”after several rounds of a comprehensive investigation.This consensus aims to improve the awareness of clinicians and provide recommendations for the early screening,diagnosis,and treatment of anticancer DILD.This consensus also emphasizes the importance of multidisciplinary collaboration while managing DILD.

Inflammasome links traumatic brain injury, chronic traumatic encephalopathy, and Alzheimer's disease

Traumatic brain injury, chronic traumatic encephalopathy, and Alzheimer's disease are three distinct neurological disorders that share common pathophysiological mechanisms involving neuroinflammation. One sequela of neuroinflammation includes the pathologic hyperphosphorylation of tau protein, an endogenous microtubule-associated protein that protects the integrity of neuronal cytoskeletons. Tau hyperphosphorylation results in protein misfolding and subsequent accumulation of tau tangles forming neurotoxic aggregates. These misfolded proteins are characteristic of traumatic brain injury, chronic traumatic encephalopathy, and Alzheimer's disease and can lead to downstream neuroinflammatory processes, including assembly and activation of the inflammasome complex. Inflammasomes refer to a family of multimeric protein units that, upon activation, release a cascade of signaling molecules resulting in caspase-induced cell death and inflammation mediated by the release of interleukin-1β cytokine. One specific inflammasome, the NOD-like receptor protein 3, has been proposed to be a key regulator of tau phosphorylation where it has been shown that prolonged NOD-like receptor protein 3 activation acts as a causal factor in pathological tau accumulation and spreading. This review begins by describing the epidemiology and pathophysiology of traumatic brain injury, chronic traumatic encephalopathy, and Alzheimer's disease. Next, we highlight neuroinflammation as an overriding theme and discuss the role of the NOD-like receptor protein 3 inflammasome in the formation of tau deposits and how such tauopathic entities spread throughout the brain. We then propose a novel framework linking traumatic brain injury, chronic traumatic encephalopathy, and Alzheimer's disease as inflammasomedependent pathologies that exist along a temporal continuum. Finally, we discuss potential therapeutic targets that may intercept this pathway and ultimately minimize long-term neurological decline.

Expectations and Level of Satisfaction of the Patient with Parkinson’s Disease Undergoing Deep Brain Stimulation Surgery at the National Institute of Neurology and Neurosurgery

Background: Deep brain stimulation (DBS) is an established treatment for patients with advanced Parkinson’s disease (PD). Reports show continued patient satisfaction after surgery despite not maintaining clinical improvement as measured by evolution scales. Objectives: The present study sought to explore expectations and level of satisfaction in patients after DBS surgery with a semi-structured questionnaire and subsequent correlation with functional scales, Quality of Life (QoL), and motor and non-motor symptoms. Methods: We performed descriptive statistics to represent demographic data, Wilcoxon rank tests to determine significant differences, and Spearman correlation between the applied scales. Results: We evaluated 20 patients with a history of DBS surgery. 45% were female, with a mean age of 55.7 ± 14.15 years, a mean disease duration of 13.42 ± 8.3 years, and a mean time after surgery of 3.18 ± 1.86 years. Patients reported surgery meeting expectations in 85.5% and continued satisfaction in 92%. These two variables showed a significant correlation. Conclusions: This sample of patients remained satisfied after DBS surgery, although we found no differences in motor and non-motor clinimetric scales. Further studies are needed to confirm the importance of assessing quality of life in patients with DBS.

Automated Brain Tumor Diagnosis Using Deep Residual U-Net Segmentation Model

Automated segmentation and classification of biomedical images act as a vital part of the diagnosis of brain tumors(BT).A primary tumor brain analysis suggests a quicker response from treatment that utilizes for improving patient survival rate.The location and classification of BTs from huge medicinal images database,obtained from routine medical tasks with manual processes are a higher cost together in effort and time.An automatic recognition,place,and classifier process was desired and useful.This study introduces anAutomatedDeepResidualU-Net Segmentation with Classification model(ADRU-SCM)for Brain Tumor Diagnosis.The presentedADRUSCM model majorly focuses on the segmentation and classification of BT.To accomplish this,the presented ADRU-SCM model involves wiener filtering(WF)based preprocessing to eradicate the noise that exists in it.In addition,the ADRU-SCM model follows deep residual U-Net segmentation model to determine the affected brain regions.Moreover,VGG-19 model is exploited as a feature extractor.Finally,tunicate swarm optimization(TSO)with gated recurrent unit(GRU)model is applied as a classification model and the TSO algorithm effectually tunes theGRUhyperparameters.The performance validation of the ADRU-SCM model was tested utilizing FigShare dataset and the outcomes pointed out the better performance of the ADRU-SCM approach on recent approaches.

Computer-Aided Diagnosis Model Using Machine Learning for Brain Tumor Detection and Classification

The Brain Tumor(BT)is created by an uncontrollable rise of anomalous cells in brain tissue,and it consists of 2 types of cancers they are malignant and benign tumors.The benevolent BT does not affect the neighbouring healthy and normal tissue;however,the malignant could affect the adjacent brain tissues,which results in death.Initial recognition of BT is highly significant to protecting the patient’s life.Generally,the BT can be identified through the magnetic resonance imaging(MRI)scanning technique.But the radiotherapists are not offering effective tumor segmentation in MRI images because of the position and unequal shape of the tumor in the brain.Recently,ML has prevailed against standard image processing techniques.Several studies denote the superiority of machine learning(ML)techniques over standard techniques.Therefore,this study develops novel brain tumor detection and classification model using met heuristic optimization with machine learning(BTDC-MOML)model.To accomplish the detection of brain tumor effectively,a Computer-Aided Design(CAD)model using Machine Learning(ML)technique is proposed in this research manuscript.Initially,the input image pre-processing is performed using Gaborfiltering(GF)based noise removal,contrast enhancement,and skull stripping.Next,mayfly optimization with the Kapur’s thresholding based segmentation process takes place.For feature extraction proposes,local diagonal extreme patterns(LDEP)are exploited.At last,the Extreme Gradient Boosting(XGBoost)model can be used for the BT classification process.The accuracy analysis is performed in terms of Learning accuracy,and the validation accuracy is performed to determine the efficiency of the proposed research work.The experimental validation of the proposed model demonstrates its promising performance over other existing methods.

Novel multi-parametric diagnosis of non-alcoholic fatty liver disease using ultrasonography,body mass index,and Fib-4 index

BACKGROUND Shear wave speed(SWS),shear wave dispersion(SWD),and attenuation imaging(ATI)are new diagnostic parameters for non-alcoholic fatty liver disease.To differentiate between non-alcoholic steatohepatitis(NASH)and non-alcoholic fatty liver(NAFL),we developed a clinical index we refer to as the“NASH pentagon”consisting of the 3 abovementioned parameters,body mass index(BMI),and Fib-4 index.AIM To investigate whether the area of the NASH pentagon we propose is useful in discriminating between NASH and NAFL.METHODS This non-invasive,prospective,observational study included patients diagnosed with fatty liver by abdominal ultrasound between September 2021 and August 2022 in whom shear wave elastography,SWD,and ATI were measured.Histological diagnosis based on liver biopsy was performed in 31 patients.The large pentagon group(LP group)and the small pentagon group(SP group),using an area of 100 as the cutoff,were compared;the NASH diagnosis rate was also investigated.In patients with a histologically confirmed diagnosis,receiveroperating characteristic(ROC)curve analyses were performed.RESULTS One hundred-seven patients(61 men,46 women;mean age 55.1 years;mean BMI 26.8 kg/m2)were assessed.The LP group was significantly older(mean age:60.8±15.2 years vs 46.4±13.2 years;P<0.0001).Twenty-five patients who underwent liver biopsies were diagnosed with NASH,and 6 were diagnosed with NAFL.On ROC curve analyses,the areas under the ROC curves for SWS,dispersion slope,ATI value,BMI,Fib-4 index,and the area of the NASH pentagon were 0.88000,0.82000,0.58730,0.63000,0.59333,and 0.93651,respectively;the largest was that for the area of the NASH pentagon.CONCLUSION The NASH pentagon area appears useful for discriminating between patients with NASH and those with NAFL.