Stage Of Disease Research Articles

The Role of Reactive Oxygen Species in Age-Related Macular Degeneration: A Comprehensive Review of Antioxidant Therapies

This review article delves into the intricate roles of reactive oxygen species (ROS) in the pathogenesis of age-related macular degeneration (AMD). It presents a detailed analysis of the oxidative stress mechanisms that contribute to the development and progression of these diseases. The review systematically explores the dual nature of ROS in ocular physiology and pathology, underscoring their essential roles in cellular signaling and detrimental effects when in excess. In the context of AMD, the focus is on the oxidative impairment in the retinal pigment epithelium and Bruch’s membrane, culminating in the deterioration of macular health. Central to this review is the evaluation of various antioxidant strategies in the prevention and management of AMD. It encompasses a wide spectrum of antioxidants, ranging from dietary nutrients like vitamins C and E, lutein, and zeaxanthin to pharmacological agents with antioxidative properties. The review also addresses novel therapeutic approaches, including gene therapy and nanotechnology-based delivery systems, aiming to enhance antioxidant defense mechanisms in ocular tissues. The article concludes by synthesizing current research findings, clinical trial data, and meta-analyses to provide evidence-based recommendations. It underscores the need for further research to optimize antioxidant therapies, considering individual patient factors and disease stages. This comprehensive review thus serves as a valuable resource for clinicians, researchers, and healthcare professionals in ophthalmology, offering insights into the potential of antioxidants in mitigating the burden of AMD.

Patterns of brain volume and metabolism predict clinical features in the progressive supranuclear palsy spectrum

Abstract Progressive supranuclear palsy (PSP) is a neurodegenerative tauopathy that presents with highly heterogenous clinical syndromes. We perform cross-sectional data driven discovery of independent patterns of brain atrophy and hypometabolism across the entire PSP spectrum. We then use these patterns to predict specific clinical features, to assess their relationship to phenotypic heterogeneity. We included 111 patients with PSP (60 with Richardson syndrome and 51with cortical and subcortical variant subtypes). Ninety-one were used as the training set and twenty as a test set. Presence and severity of granular clinical variables such as postural instability, parkinsonism, apraxia, and supranuclear gaze palsy was noted. Domains of akinesia, ocular motor impairment, postural instability, and cognitive dysfunction as defined by the Movement Disorders Society Criteria for PSP were also recorded. Non-negative matrix factorization was used on cross-sectional MRI and fluorodeoxyglucose positron emission tomography (FDG-PET) scans. Independent models for each as well as a combined model for MRI and FDG-PET were developed and used to predict the granular clinical variables. Both MRI and FDG-PET were better at predicting presence of a symptom than severity suggesting identification of disease state may be more robust than disease stage. FDG-PET predicted predominantly cortical abnormalities better than MRI such as ideomotor apraxia, apraxia of speech and frontal dysexecutive syndrome. MRI demonstrated prediction of cortical and more so subcortical abnormalities, such as parkinsonism. Distinct neuroanatomical foci were predictive in MRI and FDG-PET based models. For example, vertical gaze palsy was predicted by midbrain atrophy on MRI, but frontal eye field hypometabolism on FDG-PET. Findings also differed by scale or instrument used. For example, prediction of ocular motor abnormalities using the PSP Saccadic impairment Scale was stronger than with the Movement disorders society diagnostic criteria for PSP oculomotor impairment designation. Combination of MRI and FDG-PET demonstrated enhanced detection of parkinsonism and frontal syndrome presence and apraxia, cognitive impairment, and bradykinesia severity. Both MRI and FDG-PET patterns were able to predict some measures in the test set, however prediction of global cognition measured by Montreal Cognitive Assessment was the strongest. MRI predictions generalized more robustly to the test set. PSP leads to neurodegeneration in motor, cognitive and ocular motor networks at cortical and subcortical foci, leading to diverse yet overlapping clinical syndromes. To advance understanding of phenotypic heterogeneity in PSP, it is essential to consider data driven approaches to clinical-neuroimaging analyses.

Impact of Defense Mechanism Styles on Disease Progression in Patients with Gastrointestinal Cancer: Focusing on Clinical Staging.

The escalating incidence of cancer and the concurrent rise in mental health issues necessitate investigation into the potential for psychological factors to impede timely and effective treatment. This study examines the association between defense mechanism styles and disease progression, specifically focusing on clinical staging, in patients diagnosed with gastrointestinal (GI) cancer. Employing a descriptive correlational design, the study recruited 205 patients with GI cancer admitted to Javad Al-Aeme Hospital in Kerman, Iran, during the year 2022. Convenience sampling was utilized for participant selection. Data collection instruments included the Defense Style Questionnaire-40 (DSQ-40) and patients' documented clinical stage information. Correlation coefficients and ordinal logistic regression were employed for data analysis. Over half of 205 GI cancer patients were female (53.2%). The majority were married (85.8%) with an average age of 53.86 ± 8.21years. Nearly a quarter (23.9%) were in disease stage 1, with similar proportions in stages 2 (25.4%), 3 (27.3%), and 4 (23.4%). The findings revealed a significant inverse correlation between mature defense mechanism styles and clinical stage (r = - 0.55, p < 0.001), indicating that patients who employed more adaptive defense mechanisms had lower-stage cancer. Conversely, a significant positive correlation was observed between immature defense mechanism styles and clinical stage (r = - 0.49, p < 0.001), suggesting that patients who relied on less effective defense mechanisms had more advanced-stage cancer. However, no significant association was found between neurotic defense mechanism styles and clinical stage (r = - 0.12, p = 0.079). This study provides preliminary evidence that defense mechanism styles are associated with disease progression in patients with GI cancer. Mature defense mechanisms may promote slower disease progression, while immature defense mechanisms may contribute to more advanced disease stages. Further research is needed to confirm these findings and develop interventions to improve psychological well-being in this patient population.

Epidemiology, pathophysiology and clinical aspects of Hepatocellular Carcinoma in MAFLD patients.

Hepatocellular carcinoma (HCC) is undergoing a transformative shift, with metabolic-associated fatty liver disease (MAFLD) emerging as a dominant etiology. Diagnostic criteria for MAFLD involve hepatic steatosis and metabolic dysregulation. Globally, MAFLD prevalence stands at 38.77%, significantly linked to the escalating rates of obesity. Epidemiological data indicate a dynamic shift in the major etiologies of hepatocellular carcinoma (HCC), transitioning from viral to metabolic liver diseases. Besides the degree of liver fibrosis, several modifiable lifestyle risk factors, such as type 2 diabetes, obesity, alcohol use, smoking, and HBV, HCV infection contribute to the pathogenesis of HCC. Moreover gut microbiota and genetic variants maycontribute to HCCdevelopment.The pathophysiological link between MAFLD and HCC involves metabolic dysregulation, impairing glucose and lipid metabolism, inflammation and oxidative stress. Silent presentation poses challenges in early MAFLD-HCC diagnosis. Imaging, biopsy, and AI-assisted techniques aid diagnosis, while HCC surveillance in non-cirrhotic MAFLD patients remains debated.ITA.LI.CA. group proposes a survival-based algorithm for treatmentbased on Barcelona clinic liver cancer (BCLC) algorithm. Liver resection, transplantation, ablation, and locoregional therapies are applied based on the disease stage. Systemic treatments is promising, with initial immunotherapy results indicating a less favorable response in MAFLD-related HCC.Adopting lifestyle interventions and chemopreventive measures with medications, including aspirin, metformin, and statins, constitute promising approaches for the primary prevention of HCC.Prognosis is influenced by multiple factors, with MAFLD-HCC associated with prolonged survival. Emerging diagnostic biomarkers and epigenomic markers, show promising results for early HCC detection in the MAFLD population.

HLFSRNN-MIL: A Hybrid Multi-Instance Learning Model for 3D CT Image Classification

At present, many diseases are diagnosed by computer tomography (CT) image technology, which affects the health of the lives of millions of people. In the process of disease confrontation, it is very important for patients to detect diseases in the early stage by deep learning of 3D CT images. The paper offers a hybrid multi-instance learning model (HLFSRNN-MIL), which hybridizes high-low frequency feature fusion (HLFFF) with sequential recurrent neural network (SRNN) for CT image classification tasks. Firstly, the hybrid model uses Resnet-50 as the deep feature. The main feature of the HLFSRNN-MIL lies in its ability to make full use of the advantages of the HLFFF and SRNN methods to make up for their own weakness; i.e., the HLFFF can extract more targeted feature information to avoid the problem of excessive gradient fluctuation during training, and the SRNN is used to process the time-related sequences before classification. The experimental study of the HLFSRNN-MIL model is on two public CT datasets, namely, the Cancer Imaging Archive (TCIA) dataset on lung cancer and the China Consortium of Chest CT Image Investigation (CC-CCII) dataset on pneumonia. The experimental results show that the model exhibits better performance and accuracy. On the TCIA dataset, HLFSRNN-MIL with Residual Network (ResNet) as the feature extractor achieves an accuracy (ACC) of 0.992 and an area under curve (AUC) of 0.997. On the CC-CCII dataset, HLFSRNN-MIL achieves an ACC of 0.994 and an AUC of 0.997. Finally, compared with the existing methods, HLFSRNN-MIL has obvious advantages in all aspects. These experimental results demonstrate that HLFSRNN-MIL can effectively solve the disease problem in the field of 3D CT images.

ACUTE PANCREATITIS: IS THERE ANY RELATIONSHIP BETWEEN HEMATOCRIT AND SEVERITY OF THE DISEASE?

Introduction. Acute pancreatitis (AP) is one of the unsolved clinical problems in the practice of modern gastroenterology, surgery, and emergency medicine, which does not lose its relevance even with the advent of modern and high-tech diagnostic and therapeutic solutions. The failure of modern methods to diagnose and stratify the severity of the disease in the early stages after hospitalization of patients lead to the loss of precious time at the beginning of resuscitation measures in severe forms of AP. The aim. To assess the presence and strength of the correlation between hematocrit count and the severity of AP on the first day of the disease. Materials and methods. Polychoric analysis and receiver operating characteristic (ROC) analysis were used to assess the association and predictive value of hematocrit with the severity of AP. Only patients who arrived at the hospital within 24 hours of the onset of the disease were included in the study. Results. The correlation coefficient φ2 between hematocrit (at the reference value of 45%) and severity of AP is 0.34. The coefficient of mutual conjugation of K. Pearson (C) is equal to 0.5 which indicates the presence of a relationship between the hematocrit count and the severity of AP. After conducting the ROC analysis, it was determined that with the isolated use of hematocrit indicator provided its value of &gt; 44.4%, almost 70% of patients were diagnosed with severe AP; almost 90% of patients with a hematocrit level of £ 44.4% did not have severe AP. Also, due to the rather high values of the indicators of the prognostic capacity of the ROC analysis, it is possible to state about the presence of prognostic significance of hematocrit indicator for severe AP within the first day from the onset of the disease. Conclusions. Thus, the hematocrit number has a significant relationship with the severity of AP when calculated during the first day from the onset of the disease, which makes it promising for building systems of stratification of the severity of AP earlier than suggested by revised Atlanta criteria.

Alzheimer’s disease detection and stage identification from magnetic resonance brain images using vision transformer

Machine learning techniques applied in neuroimaging have prompted researchers to build models for early diagnosis of brain illnesses such as Alzheimer’s disease (AD). Although this task is difficult, advanced deep-learning (DL) approaches can be used. These DL models are effective, but difficult to interpret, time-consuming, and resource-intensive. Therefore, neuroscientists are interested in employing novel, less complex structures such as transformers that have superior pattern-extraction capabilities. In this study, an automated framework for accurate AD diagnosis and precise stage identification was developed by employing vision transformers (ViTs) with fewer computational resources. ViT, which captures the global context as opposed to convolutional neural networks (CNNs) with local receptive fields, is more efficient for brain image processing than CNN because the brain is a highly complex network with connected parts. The self-attention mechanism in the ViT helps to achieve this goal. Magnetic resonance brain images belonging to four stages were utilized to develop the proposed model, which achieved 99.83% detection accuracy, 99.69% sensitivity, 99.88% specificity, and 0.17% misclassification rate. Moreover, to prove the ability of the model to generalize, the mean distances of the transformer blocks and attention heat maps were visualized to understand what the model learned from the MRI input image.

Lung quantitative ultrasound to stage and monitor interstitial lung diseases

Chronic interstitial lung diseases (ILDs) require frequent point-of-care monitoring. X-ray-based methods lack resolution and are ionizing. Chest computerized tomographic (CT) scans are expensive and provide more radiation. Conventional ultrasound can detect severe lung damage via vertical artifacts (B-lines). However, this information is not quantitative, and the appearance of B-lines is operator- and system-dependent. Here we demonstrate novel ultrasound-based biomarkers to assess severity of ILDs. Lung alveoli scatter ultrasound waves, leading to a complex acoustic signature, which is affected by changes in alveolar density due to ILDs. We exploit ultrasound scattering in the lung and combine quantitative ultrasound (QUS) parameters, to develop ultrasound-based biomarkers that significantly correlate (p = 1e−4 for edema and p = 3e−7 for fibrosis) to the severity of pulmonary fibrosis and edema in rodent lungs. These innovative QUS biomarkers will be very significant for monitoring severity of chronic ILDs and response to treatment, especially in this new era of miniaturized and highly portable ultrasound devices.

A treadmill training program in a gamified virtual reality environment combined with transcranial direct current stimulation in Parkinson's Disease: Study protocol for a randomized controlled trial.

Parkinson's Disease (PD) affects movement and cognition, and physiotherapy, particularly treadmill gait training, has potential in addressing movement dysfunctions in PD. However, treadmill training falls short in addressing cognitive aspects and adherence. Virtual reality (VR) and gamification can enhance motor and cognitive retraining and improve adherence. People with Parkinson's Disease (PWPD) have decreased motor skill learning efficiency, but tDCS can improve motor and cognitive learning. 78 participants with PD will be randomly allocated in a 1:1:1 ratio to one of three groups: (1) treadmill + Gamified Virtual Reality Environment (GVRE) + tDCS training group; (2) treadmill + GVRE training group or (3) treadmill training group. Participants will follow a 6-week, 12-session treadmill gait training plan, gradually increasing session duration from 20 to 45 minutes. Participants in (1) and (2) will undergo a GVRE training protocol, with (1) also receiving tDCS for the first 20 minutes of each session. Assessments will occur at baseline, post-intervention, and at a 6-week follow-up. The primary outcome measure will be gait speed during single and dual-task performance. Secondary measures will include additional gait parameters, executive tests for cognitive performance, and clinical outcomes for disease stage, cognitive status, and physical condition. This randomized clinical trial presents an innovative neurorehabilitation protocol that aims to improve gait and cognition in PWPD. The study also examines how tDCS can enhance motor and cognitive training. Results could contribute to enhancing the motor and cognitive state of PWPD through a GVRE and tDCS-based neurorehabilitation protocol. NCT05243394. 28/02/2024 -v3.2.

Assessing the Stability of Clusters of Neuropsychiatric Symptoms in Alzheimer's Disease and Mild Cognitive Impairment.

The aim of the study was to investigate the factors that underpin neuropsychiatric symptoms and how they might evolve over time in people with Mild Cognitive Impairment (MCI) and Alzheimer's disease (AD) dementia. Neuropsychiatric symptoms are psychiatric and behavioural manifestations that occur in people with AD. These are highly prevalent along the continuum of the disease, including at the stage of MCI, as well as before cognitive decline. Various small- and large-scale projects have investigated the underlying factors that underpin these symptoms; however, the identification of clear clusters is still a matter of debate; furthermore, no study has investigated how the clusters might change across the development of AD pathology by comparing different time points. Our objective was to investigate the factors that underpin neuropsychiatric symptoms in Alzheimer's disease (AD) and Mild Cognitive Impairment (MCI) and to assess how the loadings might differ based on considerations such as the disease stage of the samples. Data was obtained from the Alzheimer's Disease Neuroimaging Initiative database (adni. loni.usc.edu), using scores from the Neuropsychiatric Inventory, followed up yearly from baseline until month 72. Participant groups included those with MCI or AD dementia, or a mixture of both, with all participants presenting with at least one neuropsychiatric symptom. A series of exploratory Principal Component and Factor (Principal Axis) Analyses were performed using Direct Oblimin rotation. The best-fitting structure was interpreted for each time point. A consistent, unique structure could not be identified, as the factors were unstable over time, both within the MCI and AD groups. However, some symptoms showed a tendency to load on the same factors across most measurements (i.e., agitation with irritability, depression with anxiety, elation with disinhibition, delusions with hallucinations). Although the analyses revealed some degree of co-occurrence of neuropsychiatric symptoms across time points/samples, there was also considerable variation. In the AD group, more discrete syndromes were evident at the early time points, whereas a more complex picture of co-occurring symptoms, with differences likely reflecting disease staging, was seen at later time points. As a clear and distinctive factor structure was not consistently identified across time points/ samples, this highlights the potential importance of sample selection (e.g., disease stage and/or heterogeneity) when studying, for example, the neurobiological underpinnings of neuropsychiatric symptoms.

A clinical perspective on the revised criteria for diagnosis and staging of Alzheimer's disease.

A clinical perspective on the revised criteria for diagnosis and staging of Alzheimer's disease.

Progressive Kidney Failure by Angiotensinogen Inactivation in the Germline.

Autosomal recessive renal tubular dysgenesis is a rare, usually fatal inherited disorder of the REN (renin)-angiotensin system. Herein, we report an adolescent individual experiencing an unknown chronic kidney disease and aim to provide novel insights into disease mechanisms. Exome sequencing for a gene panel associated with renal disease was performed. The REN-angiotensin system was assessed by comprehensive biochemical analysis in blood. REN expression was determined in primary tubular cells by quantitative polymerase chain reaction and in situ hybridization on kidney biopsy samples. Allele frequencies of heterozygous and biallelic deleterious variants were determined by analysis of the Genomics England 100,000 Genomes Project. The patient was delivered prematurely after oligohydramnios was detected during pregnancy. Postnatally, he recovered from third-degree acute kidney injury but developed chronic kidney disease stage G3b over time. Exome sequencing revealed a previously reported pathogenic homozygous missense variant, p.(Arg375Gln), in the AGT (angiotensinogen) gene. Blood AGT concentrations were low, but plasma REN concentration and gene expression in kidney biopsy, vascular, and tubular cells revealed strong upregulation of REN. Angiotensin II and aldosterone in blood were not abnormally elevated. Renal tubular dysgenesis may present as chronic kidney disease with a variable phenotype, necessitating broad genetic analysis for diagnosis. Functional analysis of the renin-angiotensin system in a patient with AGT mutation revealed novel insights regarding compensatory upregulation of REN in vascular and tubular cells of the kidney and in plasma in response to depletion of AGT substrate as a source of Ang II (similarly observed with hepatic AGT silencing for the treatment of hypertension).

Establishing Criteria for Tumor Necrosis as Prognostic Indicator in Colorectal Cancer.

Tumor necrosis has been reported to represent an independent prognostic factor in colorectal cancer, but its evaluation methods have not been described in sufficient detail to introduce tumor necrosis evaluation into clinical use. To study the potential of tumor necrosis as a prognostic indicator in colorectal cancer, criteria for 3 methods for its evaluation were defined: the average percentage method (tumor necrosis percentage of the whole tumor), the hotspot method (tumor necrosis percentage in a single hotspot), and the linear method (the diameter of the single largest necrotic focus). Cox regression models were used to calculate cancer-specific mortality hazard ratios (HRs) for tumor necrosis categories in 2 colorectal cancer cohorts with more than 1800 cases. For reproducibility assessment, 30 cases were evaluated by 9 investigators, and Spearman's rank correlation coefficients and Cohen's kappa coefficients were calculated. We found that all 3 methods predicted colorectal cancer-specific survival independent of other prognostic parameters, including disease stage, lymphovascular invasion, and tumor budding. The greatest multivariable HRs were observed for the average percentage method (cohort 1: HR for ≥ 40% vs. <3% 3.03, 95% CI, 1.93-4.78; cohort 2: HR for ≥ 40% vs. < 3% 2.97; 95% CI, 1.63-5.40). All 3 methods had high reproducibility, with the linear method showing the highest mean Spearman's correlation coefficient (0.91) and Cohen's kappa (0.70). In conclusion, detailed criteria for tumor necrosis evaluation were established. All 3 methods showed good reproducibility and predictive ability. The findings pave the way for the use of tumor necrosis as a prognostic factor in colorectal cancer.

Ayurvedic Management Of Sheetada With Special Reference To Gingivitis

Sheetada is one of the common problems of Dantamoola which is portrayed by the Raktasrava (Bleeding gum), Durghandha (halitosis), Krushnta (Discolouration of gums), Kledata (Excessive dischargre) Mruduta (Softnes of gums), Sheernata (gum downturn). It very well may be associated by Gingivitis. Gingivitis is caused mainly by the accumulation of debris, plaque, and calculus at the tooth margin, due to neglect of oral care . Among all periodontal disease Sheetada is an early stage of periodontal disease. It is caused by vitiated Kapha and Rakta which produces spontaneous bleeding from dark, slimy and soft gums with offensive odour and gum recession. In present study we have discussed a case of 52 year old female patient came to our OPD with chief complaints of foul smell from mouth,bleeding from gums on brushing,pain and swollen gums from last 6 months. This case was intervened with Dantamoola shodhana(dental scaling) followed by Pratisarana with Kushtadi Churna and Gandoosha with Lakshadi Taila.These intervention showed a potential improvement in the management of Sheetada.

Deterministic modelling of asymptomatic spread and disease stage progression in vaccine preventable infectious diseases

AbstractThis study introduces a deterministic formulation for modelling the asymptotic spread of a vaccine preventable disease as well as the different stages for the progression of the disease. We derive the formula for the associated basic reproduction number. To illustrate the proposed model, we use data from the 2017–2018 diphtheria outbreak in Yemen and fit the parameters of the model. A sensitivity analysis of the basic reproduction number, with respect to the model parameters, show that this number increases with an increase of the transmission rate while this number decreases when vaccination rate increases.

Longitudinal Increases in Time to Surgery for Patients with Breast Cancer: A National Cohort Study.

Longer time to surgery (TTS) isassociated with worse survival in patients with breast cancer. Whether this associationhas encouraged more prompt care delivery remains unknown. The National Cancer Database was used to identify patients ≥18 years of agediagnosed with clinical stage 0-III breast cancer between 2006 and 2019 for whom surgery was the first mode of treatment.A linear-by-linear test fortrend assessed median TTS acrossthe interval. Adjusted linear regression modelingwas used to examineTTS trends across patient subgroups. Overall, 1,435,584 patients met the inclusion criteria. The median age was 63 years (interquartile range [IQR] 53-72), 84.3% of patients were White, 91.1% were non-Hispanic, and 99.2% were female. The median TTS in 2006 was 26 days (IQR 16-39) versus39 days in 2019(IQR 27-56)[p<0.001]. In a multivariable linear regression model, TTS increased significantly, with an annualincrease of 0.83 days(95% confidence interval 0.82-0.85; p<0.001).A consistent, significant increase in TTS was observed on subgroup analyses by surgery type, reconstruction, patient race, hospital type, and disease stage. Black race, Hispanic ethnicity, and having either Medicaid or being uninsured were significantly associated with prolonged TTS, as were mastectomy and reconstructive surgery. Despiteevidence that longer TTS is associated with poorer outcomes in patients with breast cancer, TTS has steadily increased, which may be particularly detrimental to marginalized patients. Further studies are needed to ensure the delivery of timely care to all patients.

Multiparametric Whole-Body MRI: A Game Changer in Metastatic Prostate Cancer

Prostate cancer ranks among the most prevalent tumours globally. While early detection reduces the likelihood of metastasis, managing advanced cases poses challenges in diagnosis and treatment. Current international guidelines support the concurrent use of 99Tc-Bone Scintigraphy and Contrast-Enhanced Chest and Abdomen CT for the staging of metastatic disease and response assessment. However, emerging evidence underscores the superiority of next-generation imaging techniques including PSMA-PET/CT and whole-body MRI (WB-MRI). This review explores the relevant scientific literature on the role of WB-MRI in metastatic prostate cancer. This multiparametric imaging technique, combining the high anatomical resolution of standard MRI sequences with functional sequences such as diffusion-weighted imaging (DWI) and bone marrow relative fat fraction (rFF%) has proved effective in comprehensive patient assessment, evaluating local disease, most of the nodal involvement, bone metastases and their complications, and detecting the increasing visceral metastases in prostate cancer. It does have the advantage of avoiding the injection of contrast medium/radionuclide administration, spares the patient the exposure to ionizing radiation, and lacks the confounder of FLARE described with nuclear medicine techniques. Up-to-date literature regarding the diagnostic capabilities of WB-MRI, though still limited compared to PSMA-PET/CT, strongly supports its widespread incorporation into standard clinical practice, alongside the latest nuclear medicine techniques.

Microstructural characterization of multiple sclerosis lesion phenotypes using multiparametric longitudinal analysis.

In multiple sclerosis (MS), slowly expanding lesions were shown to be associated with worse disability and prognosis. Their timely detection from cross-sectional data at early disease stages could be clinically relevant to inform treatment planning. Here, we propose to use multiparametric, quantitative MRI to allow a better cross-sectional characterization of lesions with different longitudinal phenotypes. We analysed T1 and T2 relaxometry maps from a longitudinal cohort of MS patients. Lesions were classified as enlarging, shrinking, new or stable based on their longitudinal volumetric change using a newly developed automated technique. Voxelwise deviations were computed as z-scores by comparing individual patient data to T1, T2 and T2/T1 normative values from healthy subjects. We studied the distribution of microstructural properties inside lesions and within perilesional tissue. Stable lesions exhibited the highest T1 and T2 z-scores in lesion tissue, while the lowest values were observed for new lesions. Shrinking lesions presented the highest T1 z-scores in the first perilesional ring while enlarging lesions showed the highest T2 z-scores in the same region. Finally, a classification model was trained to predict the longitudinal lesion type based on microstructural metrics and feature importance was assessed. Z-scores estimated in lesion and perilesional tissue from T1, T2 and T2/T1 quantitative maps carry discriminative and complementary information to classify longitudinal lesion phenotypes, hence suggesting that multiparametric MRI approaches are essential for a better understanding of the pathophysiological mechanisms underlying disease activity in MS lesions.

Skeletal Muscle Index-Based Cachexia Index as a Predictor of Prognosis in Patients With Cancer: A Meta-Analysis and Systematic Review.

Cachexia is associated with poor survival rates. In the clinical setting, the diagnosis of cancer cachexia is challenging. The cachexia index (CXI), a new index for predicting survival time, is a promising tool for diagnosing cancer cachexia; however, its efficacy in predicting patient survival has not been validated. This meta-analysis and systematic review aimed to explore the CXI's prognostic value in patients with cancer. The PubMed, Embase, MEDLINE, and Cochrane Library databases were searched for relevant studies to determine the association between CXI findings and prognosis. The outcomes were overall survival (OS), progression-, disease-, and recurrence-free survival (PFS/DFS/RFS) rates, and the rate of complete response. The QUality In Prognostic Studies (QUIPS) tool was used to evaluate the quality of the included trials. This meta-analysis comprised 14 studies involving 2777 patients. A low CXI was associated with decreased OS (hazard ratio [HR] 2.34, 95% confidence interval [CI] 2.01-2.72; P < .001), PFS/DFS/RFS (HR 1.93, 95% CI 1.68-2.22; P < .001), and complete response (odds ratio [OR] 0.49, 95% CI 0.36-0.66; P < .001). Patients with a low CXI had a lower body mass index (mean difference [MD] -0.75, 95% CI -1.00 to 0.50; P < .001), skeletal muscle index (standardized MD -0.80, 95% CI -0.98 to -0.61; P < .001), and serum albumin level (MD -0.23, 95% CI -0.26 to -0.20; P < .001); and a higher neutrophil-lymphocyte ratio (MD 1.88, 95% CI 1.29-2.47; P < .001) and more advanced disease stages (OR 0.80, 95% CI 0.71-0.91; P = .001). A low CXI was found to be associated with poor survival in patients with cancer. While the CXI is a promising marker for predicting cancer cachexia, further studies are required to verify its usefulness.

Assessment of the quality of life of patients suffering from chronic kidney disease depending on the clinical variability of the disease

This article describes in detail: features of the clinical course of the disease, assessment of quality of life, diagnostic criteria and modern classification of chronic kidney disease. Various pathogenesis of chronic kidney disease is revealed, taking into account the correspondence of the stages of chronic kidney disease to the ICD-10 coding. The article also details the modern methods of antihypertensive therapy in patients with chronic kidney disease, taking into account the latest clinical recommendations. Various modern studies of assessing the quality of life of patients using the SF-36 questionnaire are described, which includes 8 scales, namely: physical functioning (PF), role functioning due to physical condition (RFPC), pain intensity (PI), general health status (GHS), vital activity (VA), social functioning (SF), role functioning due to emotional state (RFES) and mental health (MH).