Franklinlauesen6899

This article reviews the potential mechanisms of endogenous CM regeneration in neonatal mouse hearts and discusses possible therapeutic targets and future research directions.BACKGROUND Clinically amyopathic dermatomyositis (CADM) is a unique sub-type of idiopathic inflammatory myopathies with a high prevalence of interstitial lung disease (ILD). Poor prognosis of the patients was strongly associated with rapid progressive ILD. The aim of this study was to identify risk factors for prediction of different types of ILD in CADM. METHODS In this study, data of 108 inpatients with CADM were collected, including 87 with ILD. The baseline clinical data and laboratory parameters, including myositis-specific and associated antibodies and tumor-associated antigens were analyzed to identify risk factors for acute or subacute interstitial pneumonitis (A/SIP) and chronic interstitial pneumonitis (CIP). RESULTS In 87 patients with CADM-ILD, 39 (36.1%) were A/SIP, and 48 (44.4%) were CIP. There were 22 (20.4%) patients with asymptomatic ILD who were detected by routine high resolution computed tomography. Cytokeratin-19 fragment (CYFRA21-1) was significantly higher in CADM-ILD than that in CADM patients without ILD; carcinoembryonic antigen and neuron-specific enolase were significantly elevated in A/SIP than that in CIP. Patients with A/SIP had a higher positive rate of anti-melanoma differentiation-associated gene 5 (MDA5), while patients with CIP had a higher positive rate of anti PL-12 and anti-Ro-52. M6620 in vitro Logistic regression analysis indicated that elevation of CYFRA21-1 was a risk factor for ILD, higher titer of anti-MDA5 indicated increased likelihood for A/SIP, and higher titer of anti-Ro-52 was also clearly associated with CIP. CONCLUSIONS This study indicated that the prevalence of ILD was high in CADM. Asymptomatic ILD has been previously underestimated. Anti-MDA5 was a risk factor for the presence of A/SIP, and CYFRA21-1 was a risk factor for ILD.Machine learning shows enormous potential in facilitating decision-making regarding kidney diseases. With the development of data preservation and processing, as well as the advancement of machine learning algorithms, machine learning is expected to make remarkable breakthroughs in nephrology. Machine learning models have yielded many preliminaries to moderate and several excellent achievements in the fields, including analysis of renal pathological images, diagnosis and prognosis of chronic kidney diseases and acute kidney injury, as well as management of dialysis treatments. However, it is just scratching the surface of the field; at the same time, machine learning and its applications in renal diseases are facing a number of challenges. In this review, we discuss the application status, challenges and future prospects of machine learning in nephrology to help people further understand and improve the capacity for prediction, detection, and care quality in kidney diseases.BACKGROUND Texture features were the intrinsic properties of the human tissues and could efficiently detect the subtle functional changes of involved tissue. The pathologic changes of the lateral pterygoid muscle (LPM) were significantly correlated with the temporomandibular disc displacement. However, the occult functional changes of LPM could not be detected by the naked eye on the medical images. The current study was aimed to evaluate the functional changes of the LPM in the patients with temporomandibular disorders (TMDs) using texture analysis. METHODS Twenty-nine patients with TMD were performed with magnetic resonance (MR) imaging on a 3.0T MR scanner, who were consecutively recruited from the TMD clinic of Hainan Hospital of Chinese People's Liberation Army General Hospital from February 2019 to September 2019. The patients were classified into three groups according to the disc displacement disc without displacement (DWoD), disc displacement with reduction (DDWR) and disc displacement without reductcontrast and entropy could identify the altered functional status of LPM in patients with TMD and could be considered as the effective imaging biomarker to evaluate the functional changes of LPM in TMD.PURPOSE OF REVIEW Epilepsy surgery is the therapy of choice for 30-40% of people with focal drug-resistant epilepsy. Currently only ∼60% of well selected patients become postsurgically seizure-free underlining the need for better tools to identify the epileptogenic zone. This article reviews the latest neurophysiological advances for EZ localization with emphasis on ictal EZ identification, interictal EZ markers, and noninvasive neurophysiological mapping procedures. RECENT FINDINGS We will review methods for computerized EZ assessment, summarize computational network approaches for outcome prediction and individualized surgical planning. We will discuss electrical stimulation as an option to reduce the time needed for presurgical work-up. We will summarize recent research regarding high-frequency oscillations, connectivity measures, and combinations of multiple markers using machine learning. This latter was shown to outperform single markers. The role of NREM sleep for best identification of the EZ interictally will be discussed. We will summarize recent large-scale studies using electrical or magnetic source imaging for clinical decision-making. SUMMARY New approaches based on technical advancements paired with artificial intelligence are on the horizon for better EZ identification. They are ultimately expected to result in a more efficient, less invasive, and less time-demanding presurgical investigation.PURPOSE OF REVIEW Frontotemporal dementia (FTD) is a rare dementia, that accounts for about 15% of all dementia cases. Despite consensus diagnostic criteria, FTD remains difficult to diagnose in life because of its complex and variable clinical phenomenology and heterogeneous disorders. This review provides an update on the current knowledge of the main FTD syndromes -- the behavioural variant, semantic variant, and nonfluent/agrammatic variant-- their brain abnormalities and genetic profiles. RECENT FINDINGS The complexity of the clinical features in FTD has become increasingly apparent, particularly in the domain of behaviour. Such behaviour changes are now also being recognized in the language variants of FTD. Initial interest on emotion processing and social cognition is now complemented by studies on other behavioural disturbance, that spans gambling, antisocial behaviours, repetitive behaviours, and apathy. At a biological level, novel pathological subcategories continue to be identified. From a genetic viewpoint, abnormalities in three genes explain nearly three quarters of familial cases of FTD.