Mild Stage Research Articles

Functional Analysis of cag Pathogenicity Island Diversity in Helicobacter pylori Isolated from Multi-Ethnic Patients in Malaysia

cagPAI is an important pathogenic marker contributing to disease severity caused by H. pylori infection, encoding proteins for type four secretion systems (T4SS) and the pro-inflammatory cytokines (IL-8) induction in infected host. The heterogeneity of cagPAI genes results in different clinical outcomes in H. pylori infection. The aim of this study was to investigate the functional diversity of cagPAI genes in clinical H. pylori strains isolated from patients with gastroduodenal diseases. Among the isolates investigated, 6 out of 27 (22.2%) harbor intact cagPAI, 3 strains (11.1%) have complete absence of cagPAI, and 18 strains (66.7%) contain a partially deleted island. IL-8 concentration is observed significantly higher among cagPAI-positive isolates (P = 0.045). Of note, IL-8 secretion in the gastric cells infected with H. pylori strains isolated from Malay patients was significantly higher than the gastric cells infected with the strains from non-Malay patients (P = 0.046). The secretion of IL-8 appears in similarly high among intact and partially deleted cagPAI isolates. East Asian cagA isolates induce a notably higher level of IL-8 than that of Western cagA. Inflammatory activities are observed and IL-8 concentration in the milder stage was found in significantly higher quantities than in the severe stage (P = 0.027). The ‘hummingbird’ phenotype was observed to cause severe effects in infected cells with cagPAI-intact H. pylori. The genetic variability of cagPAI isolates from multi-ethnicity patients may have unique implications for disease outcomes with respect to proinflammatory secretion.

Mitochondrial respiration in peripheral arterial disease depends on stage severity.

Peripheral arterial disease (PAD) is an increasing cause of morbidity and its severity is graded based on clinical manifestation. To investigate the influence of the different stages on myopathy of ischemic muscle we analysed severity-dependent effects of mitochondrial respiration in PAD. Eighteen patients with severe PAD, defined as chronic limb-threatening ischemia, 47 patients with intermittent claudication (IC) and 22 non-ischemic controls were analysed. High-resolution respirometry (HRR) was performed on muscle biopsies of gastrocnemius and vastus lateralis muscle of patients in different PAD stages to investigate different respiratory states. Results from HRR are given as median and interquartile range and were normalized to citrate synthase activity (CSA), a marker for mitochondrial content. In order to account for inter-individual differences between patients and controls, we calculated the ratio of O₂-flux in gastrocnemius muscle over vastus muscle ('GV ratio'). CSA of the gastrocnemius muscle as a proxy for mitochondrial content was significantly lower in critical ischemia compared to controls. Mitochondrial respiration normalized to CSA was higher in IC compared to controls. Likewise, the GV ratio was significantly higher in IC compared to control. Mitochondrial respiration and CSA of PAD patients showed stage-dependent modifications with greater changes in the mild PAD stage group (IC).

A comparative study of three ultrasound techniques for liver fibrosis staging.

The aim of this study is to assess the agreement and performance of visual transient elastography (ViTE), sound touch elastography (STE), and sound touch quantification (STQ) for liver fibrosis staging using transient elastography (TE) as a standard. We finally enrolled 252 subjects with chronic liver disease (CLD) who underwent ViTE, STE, STQ, and TE examinations simultaneously in our hospital from October 2022 to July 2023. We evaluated the correlation and agreement between various technologies. And also evaluated the performances and optimal cutoffs of ViTE, STE, and STQ . The correlation coefficients of ViTE and TE, STE and TE, STQ and TE were 0.863, 0.709, and 0.727, respectively. The ICC among ViTE, STE, STQ, and TE was 0.911. The area under the receiver operating characteristics (AUROCs) of ViTE, STE, and STQ for detection of TE of ≥5 kPa, ≥10 kPa, ≥15 kPa, and ≥20 kPa were 0.867, 0.771, 0.804; 0.972, 0.935, 0.933; 0.998, 0.973, 0.968; and 1.000, 0.960, 0.954, respectively. The AUROCs of ViTE for detection of lower stages (TE≥5 kPa and ≥10 kPa) were significantly higher than STE and STQ in the overall cohort (ViTE vs. STE: Z = 2.766, for TE ≥5 kPa; ViTE vs. STE: Z = 2.145, ; ViTE vs. STQ: Z = 2.587, for TE ≥10 kPa) (all P < 0.05). These methods all have coincided with performance in more advanced stages (TE ≥15 kPa and ≥20 kPa) (all P > 0.05). These methods showed excellent correlation and agreement. ViTE performance in more advanced fibrosis differentiation is comparable to the STE and STQ while ViTE is more accurate than STE and STQ to identify patients with mild CLD stage, and can more effectively rule out compensated advanced CLD.

ChatGPT as a Diagnostic Aid in Alzheimer's Disease: An Exploratory Study.

The potential of ChatGPT in medical diagnosis has been explored in various medical conditions. We assessed whether ChatGPT can contribute to the diagnosis of Alzheimer's disease (AD). We provided ChatGPT with four generated cases (mild, moderate, or advanced stage AD dementia, or mild cognitive impairment), including descriptions of their complaints, physical examinations, as well as biomarker, neuroimaging, and neuropsychological data. ChatGPT accurately diagnosed the test cases similarly to two blinded specialists. While the use of generated cases can be a limitation to our study, our findings demonstrate that ChatGPT can be a useful tool for symptom assessment and the diagnosis of AD. However, while the use of ChatGPT in AD diagnosis is promising, it should be seen as an adjunct to clinical judgment rather than a replacement.

Comparative Analysis of CNN and Different R-CNN based Model for Prediction of Alzheimer’s Disease

INTRODUCTION: Medical images still need to be examined by medical personnel, which is a prolonged and vulnerable progression. The dataset used included 4 classes of 6400 training and test MRI images each and was collected from Kaggle such as cognitively normal (CN), Mild Cognitive Impairment stage (MCI), moderate cognitive impairment (Moderate MCI), and Severe stage of cognitive impairment (AD).&#x0D; OBJECTIVES: There was a glaring underrepresentation of the Alzheimer Disease (AD) class. The accuracy and effectiveness of diagnoses can be improved with the use of neural network models.&#x0D; METHODS: In order to establish which CNN-based algorithm performed the multi-class categorization of the AD patient's brain MRI images most accurately. Thus, examine the effectiveness of the popular CNN-based algorithms like Convolutional Neural Network (CNN), Region-based CNN (R-CNN), Fast R-CNN, and Faster R-CNN.&#x0D; RESULTS: On the confusion matrix, R-CNN performed the best.&#x0D; CONCLUSION: R-CNN is quick and offers a high precision of 98.67% with a low erroneous measure of 0.0133, as shown in the research.

Stimulation of PSTPIP1 to trigger proinflammatory responses in asymptomatic SARS-CoV-2 infections

BackgroundA hyperinflammatory response to severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection gravely worsens the clinical progression of coronavirus disease 2019 (COVID-19). Although the undesirable effects of inflammasome activation have been correlated to the severity of COVID-19, the mechanisms of this process in the asymptomatic infection and disease progression have not yet been clearly elucidated. MethodsWe performed strand-specific RNA sequencing in 39 peripheral blood mononuclear cell (PBMC) samples from asymptomatic individuals（n = 10）, symptomatic patients（n = 16） and healthy donors（n = 13）. ResultsDysregulation of pyrin inflammasomes along with the proline-serine-threonine phosphatase-interacting protein 1 (PSTPIP1) gene was identified in SARS-COV-2 infection. Notably, the PSTPIP1 expression level showed a significant negative correlation with an adjacent long-noncoding RNA (lncRNA) RP11-797A18.6 in the asymptomatic individuals compared with the healthy controls. In addition, a decline in the nuclear factor kappa B subunit 1 (NFKB1) gene expression was observed in asymptomatic infection, followed by a rise in the mild and moderate disease stages, suggesting that altered NFKB1 expression and associated proinflammatory signals may trigger a disease progression. ConclusionsOverall, our results indicate that PSTPIP1-dependent pyrin inflammasomes-mediated pyroptosis and NF-κB activation might be potential preventive targets for COVID-19 disease development and progression.

Mild Traumatic Brain Injury andCareer Stage Associate with Visible Perivascular Spaces in Special Operations Forces Soldiers.

Mild traumatic brain injury (mTBI) and occupational blast exposure in military Service Members may lead to impaired brain waste clearance which increases neurological disease risk. Perivascular spaces (PVS) are a key part of the glymphatic system which supports brain waste clearance, preferentially during sleep. Visible PVS on clinical magnetic resonance imaging have been previously observed in patients with neurodegenerative diseases and animal neurotrauma models. The purpose of this study was to determine associations between PVS morphological characteristics, military career stage, and mTBI history in Special Operations Forces (SOF)Soldiers. Participants underwent T2-weighed neuroimaging to capture three-dimensional whole brain volumes. Segmentation was performed using a previously validated, multi-scale deep convolutional encoder-decoder neural network. Only PVS clusters within the white matter mask were quantified for analyses. Due to non-normal PVS metric distribution, non-parametric Mann-Whitney U tests were used to determine group differences in PVS outcomes. In total, 223 healthy SOF combat Soldiers (age = 33.1 ± 4.3yrs) were included, 217 reported career stage. Soldiers with mTBI history had greater PVS number (z = 2.51, P = 0.013) and PVS volume (z = 2.42, P = 0.016). In-career SOF combat Soldiers had greater PVS number (z = 2.56, P = 0.01) and PVS volume (z = 2.28, P = 0.02) compared to a baseline cohort. Mild TBI history is associated with increased PVS burden in SOF combat Soldiers that are clinically recovered from mTBI. This may indicate ongoing physiological changes that could lead to impaired waste clearance via the glymphatic system. Future studies should determine if PVS number and volume are meaningful neurobiological outcomes for neurodegenerative disease risk and if clinical interventions such as improving sleep can reduce PVS burden.

Plasminogen activator inhibitor-1 serum levels in frontotemporal lobar degeneration.

Plasminogen activator inhibitor-1 (PAI-1) impedes brain plasmin synthesis. Reduced plasmin activity facilitates cumulation of amyloid beta (Aβ) in Alzheimer's disease (AD). Since plasmin also regulates the synaptic activity, it is possible that altered PAI-1 is present in other neurodegenerative disorders. We investigated whether PAI-1 and its counter-regulatory tissue plasminogen activator (tPA) are altered in serum of patients with dementia due to frontotemporal lobar degeneration (FTLD). Thirty five FTLD patients (21 in mild cognitive impairment stage (MCI) and 14 in dementia stage) and 10 cognitively healthy controls were recruited. Serum tPA and PAI-1 protein levels were measured by anova. Correlation between biochemical and demographic data were explored by measuring Pearson correlation coefficient. Serum PAI-1 levels were elevated in the FTLD dementia group as compared to FTLD MCI and controls. tPA serum levels and PAI-1/tPA ratio did not significantly differ among groups. There was a negative correlation between PAI-1 serum levels and disease severity measured by MMSE score. No correlations of tPA serum levels and PAI-1/tPA ratio with MMSE were found. Increased PAI-1 serum levels may serve as a marker of dementia in FTLD, suggesting that, besides Aβ pathway, the plasmin system may affect cognition through synaptic activity.

Categorization of Alzheimer's disease stages using deep learning approaches with McNemar's test.

Early diagnosis is crucial in Alzheimer's disease both clinically and for preventing the rapid progression of the disease. Early diagnosis with awareness studies of the disease is of great importance in terms of controlling the disease at an early stage. Additionally, early detection can reduce treatment costs associated with the disease. A study has been carried out on this subject to have the great importance of detecting Alzheimer's disease at a mild stage and being able to grade the disease correctly. This study's dataset consisting of MRI images from the Alzheimer's Disease Neuroimaging Initiative (ADNI) was split into training and testing sets, and deep learning-based approaches were used to obtain results. The dataset consists of three classes: Alzheimer's disease (AD), Cognitive Normal (CN), and Mild Cognitive Impairment (MCI). The achieved results showed an accuracy of 98.94% for CN vs AD in the one vs one (1 vs 1) classification with the EfficientNetB0 model and 99.58% for AD vs CNMCI in the one vs All (1 vs All) classification with AlexNet model. In addition, in the study, an accuracy of 98.42% was obtained with the EfficientNet121 model in MCI vs CN classification. These results indicate the significant potential for mild stage Alzheimer's disease detection of Alzheimer's disease. Early detection of the disease in the mild stage is a critical factor in preventing the progression of Alzheimer's disease. In addition, a variant of the non-parametric statistical McNemar's Test was applied to determine the statistical significance of the results obtained in the study. Statistical significance of 1 vs 1 and 1 vs all classifications were obtained for EfficientNetB0, DenseNet, and AlexNet models.

Cost-utility analysis of treating mild stage normal tension glaucoma by surgery in China: a decision-analytic Markov model

BackgroundMany individuals suffer from normal tension glaucoma (NTG) in China. This study utilized Markov models to evaluate the cost-utility of applying many medications and surgery for mild-stage NTG when disease progression occurred at a mild stage.MethodsA 10-year decision-analytic Markov model was developed for the cost-utility analysis of treating mild-stage NTG with surgery and increased application of medication. We hypothesized that all 100,000 samples with a mean age of 64 were in mild stages of NTG. Transitional probabilities from the mild to moderate to severe stages and the basic parameters acquired from the CNTGS were calculated. Incremental cost-utility ratios (ICUR) were calculated for treating all patients with NTG by probabilistic sensitivity analysis (PSA) and Monte Carlo simulation. One-way sensitivity analysis were conducted by adjusting the progression rate, cost of medications or trabeculectomy, cost of follow-up, and surgical acceptance rate.ResultsThe ICUR of treating mild stage NTG with medication over 10 years was $12743.93 per quality-adjusted life years (QALYs). The ICUR for treating mild stage NTG patients with a 25% and 50% surgery rate with medication were $8798.93 and $4851.93 per QALYs, respectively. In this model, the cost-utility of treating NTG was sensitive to disease progression rate, surgical treatment rate, and medication costs.ConclusionsAccording to the results of the cost-utility analysis, it was a reasonable and advantageous strategy to administer a lot of medication and surgery for NTG in the mild stages of the disease. In the model, the greater the probability of patients undergoing surgery, the strategy becomes more valuable.

A tensor decomposition scheme for EEG-based diagnosis of mild cognitive impairment

Mild Cognitive Impairment (MCI) is the primary stage of acute Alzheimer's disease, and early detection is crucial for the person and those around him. It is difficult to recognize since this mild stage does not have clear clinical signs, and its symptoms are between normal aging and severe dementia. Here, we propose a tensor decomposition-based scheme for automatically diagnosing MCI using Electroencephalogram (EEG) signals. A new projection is proposed, which preserves the spatial information of the electrodes to construct a data tensor. Then, using parallel factor analysis (PARAFAC) tensor decomposition, the features are extracted, and a support vector machine (SVM) is used to discriminate MCI from normal subjects. The proposed scheme was tested on two different datasets. The results showed that the tensor-based method outperformed conventional methods in diagnosing MCI with an average classification accuracy of 93.96% and 78.65% for the first and second datasets, respectively. Therefore, it seems that maintaining the spatial topology of the signals plays a vital role in the processing of EEG signals.

Effect of Preoperative Trabecular Meshwork Pigmentation and Other Eye Characteristics on Outcomes of Combined Phacoemulsification/Minimally Invasive Glaucoma Surgery

PurposeTo investigate associations between pigmentation of the trabecular meshwork (PTM) and other preoperative eye characteristics and outcomes of minimally invasive glaucoma surgery combined with phacoemulsification (Phaco/MIGS). DesignRetrospective interventional case series. ParticipantsAcademic glaucoma clinic patients with symptomatic cataract and glaucoma treated with combined Phaco/MIGS. MethodsAnalyzing preoperative PTM, intraocular pressure (IOP), IOP-lowering medications and visual acuity (VA) data in relation to Phaco/MIGS outcomes. Main Outcome MeasuresPTM and other preoperative eye characteristics in relation to Phaco/MIGS success defined as postoperative IOP between 5-21 mmHg and IOP reduction of ≥20% and/or a reduction of ≥1 IOP-medications compared to baseline, and final IOP, IOP-lowering medications and VA. Results265 eyes (172 patients, mean age, 73.5 [SD, 10.0], range 35-95 years, male 40.0%) were identified and categorized with high PTM (108 eyes, 40.8%) or low PTM (157 eyes, 59.2%). The high PTM group, compared with the low PTM group, demonstrated higher preoperative IOP (16.7 [SE 0.4] vs. 15.2 [0.4] mmHg, p=0.009), included more eyes with primary open-angle glaucoma (POAG, p=0.03), fewer eyes with normal-tension glaucoma (NTG, p=0.01), and fewer eyes with mild stage glaucoma (p=0.001). Compared to baseline, final IOP decreased by 6.5 [2.4]% and 13.4 [3.0]% (p=0.075) to 13.5 [0.3] mmHg and 13.6 [0.4] mmHg (p=0.77) in the low and high PTM groups, respectively, and IOP-lowering medications decreased by 34.6 [4.9]% (n=116) and 18.1 [7.3]% (n=85), respectively (p=0.062). Surgical success was 59.9% and 58.3%, respectively (p=0.87). It was positively associated with higher preoperative IOP (Hazard Ratio 1.08 [95% CI 1.04-1.12] p<0.0001) and higher number of preoperative IOP-medications (1.20 [1.05-1.37] p=0.007), negatively associated with history of Selective Laser Trabeculoplasty (SLT, 0.40 [0.23-0.68] p=0.0009) and longer axial length (0.87 [0.80-0.94], p=0.0006), but was not associated with PTM. ConclusionsHigher PTM was associated with POAG rather than NTG, with more severe glaucoma and higher preoperative IOP, but not with Phaco/MIGS success. Surgical success was positively associated with higher preoperative IOP and number of IOP-medications and negatively associated with history of SLT and longer axial length. These findings may help guide glaucoma surgeons in surgical planning and patient counselling.

Different Splice Isoforms of Peripheral Triggering Receptor Expressed on Myeloid Cells 2 mRNA Expressions are Associated With Cognitive Decline in Mild Dementia Due to Alzheimer's Disease and Reflect Central Neuroinflammation.

Triggering receptor expressed on myeloid cells 2 (TREM2) is upregulated in activated microglia and may be related to cognitive decline in patients with Alzheimer's disease (AD). There is conflicting evidence regarding the association of peripheral TREM2 mRNA expression/soluble TREM2 (the extracellular domain of TREM2) with cognitive function/neuroinflammation in patients with AD. Herein, we studied the TREM2 and TREM2alt mRNA expression and their association with the cognitive performance in subjects with mild dementia due to AD and healthy controls. In a subgroup of patients with AD, magnetic resonance spectroscopy was used to measure the myo-inositol level in the posterior cingulate cortex, a surrogate marker for neuroinflammation. The results showed that increased TREM2 and TREM2alt mRNA expression is associated with AD pathogenesis at the mild dementia stage, thereby serving as a potential biomarker for early symptomatic stage of AD. TREM2 may exert protective effects on both cognition and central neuroinflammation.

Serologic Biomarkers for Hepatic Fibrosis in Obese Children with Nonalcoholic Steatohepatitis.

The prevalence of nonalcoholic steatohepatitis (NASH) is increasing with the increasing prevalence of childhood obesity. Although NASH has a high risk of progression to liver fibrosis and cirrhosis, few studies have reported noninvasive markers for predicting hepatic fibrosis in children. This study aimed to evaluate and compare the diagnostic accuracies of serologic biomarkers and scoring systems for hepatic fibrosis in obese children with NASH. A total of 96 children were diagnosed with NASH based on liver biopsy findings and divided into two groups according to the degree of liver fibrosis: mild (stage 0-1) or advanced (stage 2-4). Clinical and laboratory parameters and serum levels of hyaluronic acid and type IV collagen were measured. The aspartate aminotransferase/platelet ratio index (APRI) and fibrosis-4 (FIB-4) score were calculated. Among the noninvasive markers, only serum type IV collagen level and FIB-4 were significantly different between the two groups. The area under the receiver operating curve of each biomarker and scoring system was 0.80 (95% confidence interval [CI]: 0.70-0.90) for type IV collagen at an optimal cutoff of 148 ng/mL (sensitivity 69.8%, specificity 84.6%), followed by 0.69 (95% CI: 0.57-0.83) for APRI, 0.68 (95% CI: 0.56-0.80) for FIB-4, and 0.65 (95% CI: 0.53-0.77) for hyaluronic acid. Type IV collagen as a single noninvasive serologic biomarker for hepatic fibrosis and FIB-4 as a hepatic fibrosis score are beneficial in predicting advanced hepatic fibrosis and determining proper diagnosis and treatment strategies before fibrosis progresses in obese children with NASH.

Language impairments in Alzheimer´s disease: What changes can be found between mild and moderate stages of the disease?

ObjectiveTo investigate how language deteriorates over the Alzheimer's Disease course. MethodsA cross-sectional, observational study was carried out. 35 patients diagnosed with dementia due to AD using the NINCDS-ARDRA criteria and undergoing treatment for AD with a therapeutic dose of acetylcholinesterase inhibitors were assessed by the Boston Diagnostic Aphasia Examination (BDAE). The sample comprised 15 patients with mild AD (MMSE > 23, CDR = 0 or 0.5‒1.0) and 20 patients with moderate AD (MMSE = 13‒23, CDR = 2). The results for the 2 groups on all language tasks were compared. ResultsA statistically significant difference was found between the mild and moderate AD groups for total score on the BDAE (95% CI 47.10‒114.08, t = 5.0, DF = 21, p = 0.000*), as well as on several tasks involving oral and writing comprehension, language oral expression and writing. ConclusionThe study results showed major changes in the moderate stage. Also, the decline in language performance correlated with the worsening of dementia syndrome, independently of sociodemographic variables.

Optical Insights into Fibrotic Livers: Applications of Near-Infrared Spectroscopy and Machine Learning

Background: Liver fibrosis staging is critical for patient selection and management prior to transplantation, but biopsy is invasive and serum biomarkers lack accuracy. Near-infrared spectroscopy (NIRS) is an emerging non-invasive technology that can detect liver fibrosis via changes in tissue composition. Machine learning (ML) enables analysis of NIRS data for diagnostic modeling. Purpose: To review the potential of NIRS-ML approaches for rapid, point-of-care liver fibrosis detection, including technological principles, promising applications, current limitations, and future directions. Main body of the abstract: NIRS leverages unique near-infrared absorbance patterns reflecting collagen accumulation, lipid reduction, and other chemical alterations in fibrotic liver. Handheld/hyperspectral systems acquire tissue spectroscopic data in minutes. Multiple human studies correlate NIRS with histological fibrosis scores. ML techniques like partial least squares regression, neural networks, support vector machines, and random forests analyze spectra to develop optimized diagnostic algorithms. Initial models differentiate mild versus advanced fibrosis and stage cirrhosis with high accuracy, outperforming traditional biomarkers. Recent advances include smartphone-based scanning, cloud computing, and integrated user-friendly platforms. However, further large validation trials, standardization, assessment of confounding factors, improved ML methodology, and cost-effectiveness data are required before widespread clinical implementation. Conclusion: With ongoing research to address remaining barriers, NIRS-ML approaches hold great disruptive potential for rapid, non-invasive point-of-care quantification of liver fibrosis, including optimizing transplant surgery planning and management.

Biomarkers of heart failure: advances in omics studies.

Heart failure is a complex syndrome characterized by progressive circulatory dysfunction, manifesting clinically as pulmonary and systemic venous congestion, alongside inadequate tissue perfusion. The early identification of HF, particularly at the mild and moderate stages (stages B and C), presents a clinical challenge due to the overlap of signs, symptoms, and natriuretic peptide levels with other cardiorespiratory pathologies. Nonetheless, early detection coupled with timely pharmacological intervention is imperative for enhancing patient outcomes. Advances in high-throughput omics technologies have enabled researchers to analyze patient-derived biofluids and tissues, discovering biomarkers that are sensitive and specific for HF diagnosis. Due to the diversity of HF etiology, it is insufficient to study the diagnostic data of early HF using a single omics technology. This study reviewed the latest progress in genomics, transcriptomics, proteomics, and metabolomics for the identification of HF biomarkers, offering novel insights into the early clinical diagnosis of HF. However, the validity of biomarkers depends on the disease status, intervention time, genetic diversity and comorbidities of the subjects. Moreover, biomarkers lack generalizability in different clinical settings. Hence, it is imperative to conduct multi-center, large-scale and standardized clinical trials to enhance the diagnostic accuracy and utility of HF biomarkers.

Baseline symptom severity and efficacy of Silexan in patients with anxiety disorders: A symptom-based, patient-level analysis of randomized, placebo-controlled trials.

The influence of baseline severity on the efficacy of Silexan, a proprietary essential oil from Lavandula angustifolia, in anxiety disorders has not been investigated in a pooled dataset. We report on an individual patient data analysis of all five double-blind, randomized, placebo-controlled trials with Silexan in anxiety disorders. Eligible participants received Silexan 80mg/d or placebo for 10 weeks. Analyses were based on the Hamilton Anxiety Rating Scale (HAMA), its psychic and somatic anxiety subscores, and the Clinical Global Impressions (CGI) scale. To correlate baseline severity with outcome, patients were segregated into mild, moderate, and severe cases. Altogether 1,172 patients (Silexan, n=587; placebo, n=585) were analyzed. For the HAMA total score, we found a significant association between the score at baseline and the treatment effect of Silexan versus placebo at week 10 (p<0.001). HAMA items from the somatic domain scored lower at baseline and showed less improvement than items from the psychic domain, particularly in patients with mild or moderate baseline symptoms. For CGI item 2 (global improvement), significant efficacy favoring Silexan were observed in mild, moderate, and severe baseline symptom severity. Although significant improvements were found for all subsets, the more severe the initial symptoms, the greater the treatment effects documented by the HAMA. Overall this analysis confirms that Silexan is an effective treatment option in early or mild stages of anxiety disorder. Given its favorable safety profile, Silexan can thus fill a therapeutic gap in the treatment of (subsyndromal) anxiety disorders.

A Review: Diagnosis of Diabetic Retinopathy by using Machine Learning &amp; Deep Learning Techniques

This study focuses to detect all the stages of diabetic retinopathy (DR) using end-to-end deep ensemble networks. One of the most prominent and significant micro-vascular ramifications of diabetes mellitus involves diabetic retinopathy (DR). If the treatment is not received for this degenerative condition on time, it damages the retina, visual impairment and blindness may result. It impacts on the affected persons can be lessened by having a thorough understanding of the pathophysiology, on receiving a quick diagnosis, and using efficient management techniques. The complex pathophysiology of diabetic retinopathy (DR) is reviewed in this abstract, which includes a range of biochemical, molecular, and hemodynamic mechanisms brought on by persistent hyperglycaemia. Chronic hyperglycaemia contributes to the onset and progression of diabetic disease DR by causing microvascular changes, oxidative stress, inflammation, and neurodegeneration. Many hand-on engineering and end-to-end learning-based approaches are used to detect the DR using Kaggle dataset. The detection of the mild stage is important for the early control of this fatal disease. This study focuses to detect all the stages of DR using end-to-end deep ensemble networks. The results show that the proposed approach outperforms state-of-the-art methods. To get the finest mass image dataset to train models, it takes Pre-processing steps, like data augmentation will increase the number of training examples, and data normalization will precisely predict classification. So, they could train the latest CNNs model (AlexNet, VggNet, GoogleNet and ResNet) to recognize the slight differences between the image classes for DR Detection. Transfer learning and hyper-parameter tuning methods are adopted and the experimental results have demonstrated the better accuracy than non-transferring learning methodology on DR image classification.

Optical Coherence Tomography Angiography in the Evaluation of Macular Retinal Microvasculature in Patients in the Early Clinical Stage of Alzheimer’s Disease – A Preliminary Report

IntroductionAlzheimer’s disease (AD) is a chronic neurodegenerative condition causing cognitive decline among elderly individuals. In addition to neurological deficits, a key role in the onset and subsequent progression of AD Alzheimer’s disease-specific changes is attributed to vascular factors. Retinal microvasculature and cerebral small vessels share similar anatomical features and physiological characteristics, and damage to the retinal vascular bed can be observed at any stage of AD Alzheimer’s disease. Retinal microvasculature can be visualized and assessed non-invasively using the technique of optical coherence tomography angiography (OCT-A).Aim of studyEvaluation of macular retinal microvasculature in the superficial capillary plexus based on OCT-A optical coherence tomography angiography images in patients diagnosed with the mild stage of Alzheimer’s dementia.Material and methodsThe study involved a total of 21 patients diagnosed with mild Alzheimer’s dementia confirmed by neuropsychological tests and neuroimaging studies. optical coherence tomography angiography OCT-A was used to determine vessel density in the superficial retinal capillary plexus (SRCP), i.e. generate a density map within five-field ETDRS circles with diameters of 3 mm and 6 mm.ResultsAlterations in the macular retinal capillary network were noted in all eyes studied. In the majority of eyes, there was a marked reduction in vessel density reflected in the quantitative metrics in the density map around the FAZ in at least one of the scans obtained for a given eye (3 mm x 3 mm and/ or 6 mm x 6 mm) and in the macroscopic evaluation of the superficial and deep capillary plexuses, capillary thinning, and irregular area/mexpansion of the FAZ.ConclusionsOptical coherence tomography angiography OCT-A has the potential to serve as a valuable tool for screening or monitoring the response to therapies in patients with Alzheimer’s disease AD. However, further research is necessary to standardize study methodologies and determine whether impaired retinal microvasculature in patients with Alzheimer-type MCI or preclinical Alzheimer’s disease AD correlates with the advancement of cognitive decline and loss of neurons and cerebral blood vessels in individual Alzheimer’s disease AD patients and, hence, has clinical utility.