Notes

N-methylene phosphonic chitosan aerogels regarding effective capture regarding Cu2+ and Pb2+ via aqueous environment.
Lingual thyroid accounts for 90% of cases of ectopic thyroid tissue. Zuckerkandl tubercles (ZTs) have been detected in 55% of all thyroid lobes. Prominent ZTs are frequently observed in thyroid lobes affected by autoimmune thyroiditis compared with normal lobes or nodular lobes (P = 0.006). The correct interpretation of the ultrasound characteristics of these variants is essential to establish the clinical diagnosis. In the preoperative assessment, the identification of these cervical anomalies via ultrasound examination is indispensable.
To prevent Alzheimer's disease (AD) from progression to dementia, early prediction and classification of AD plays a crucial role in medical image analysis.

In this study, we employed transfer learning technique to classify Magnetic Resonance (MR) images using a pre-trained convolutional neural network (CNN).

To address the early diagnosis of AD, we employed computer-assisted technique specifically deep learning (DL) model to detect AD.

In particular, we classified Alzheimer's disease (AD), mild cognitive impairment (MCI) and normal control (NC) subjects using whole slide two-dimensional (2D) images. To illustrate this approach, we made use of state-of-the-art CNN base models, i.e., the residual networks ResNet-101, ResNet-50 and ResNet-18, and compared their effectiveness to identifying AD. To evaluate this approach, an AD Neuroimaging Initiative (ADNI) dataset was utilized. We have also showed uniqueness by using MR images selected only from the central slice containing left and right hippocampus regions to evaluate the models.

All the three models used randomly split data in the ratio 7030 for training and testing. Among the three, ResNet-101 showed 98.37% accuracy, better than the other two ResNet models, and performed well in multiclass classification. The promising results emphasize the benefit of using transfer learning specifically when the dataset is low.

From this study, we can assure that transfer learning helps to overcome DL problems mainly when the data available is insufficient to train a model from scratch. This approach is highly advantageous in medical image analysis to diagnose diseases like AD.
From this study, we can assure that transfer learning helps to overcome DL problems mainly when the data available is insufficient to train a model from scratch. This approach is highly advantageous in medical image analysis to diagnose diseases like AD.
Patients with rheumatic diseases are more likely to suffer from anxiety, depression and insomnia. Yet, little is known about mental health status during COVID-19 pandemic.

This study aims to measure the prevalence of mental health disorders among patients with rheumatic diseases in the era of COVID-19 pandemic and to determine potential risk factors for major symptoms of depression, anxiety, and insomnia in participants.

Participants with rheumatic diseases were asked to complete a questionnaire using a telephonic interview. Sociodemographic and rheumatic disease characteristics were recorded. Mental health status was assessed by the patient health questionnaire-9 (PHQ-9), generalized anxiety disorder (GAD)-7, and insomnia severity index (ISI) questionnaires to detect depression, anxiety and insomnia symptoms, respectively.

We included 307 patients in the survey. Rheumatoid arthritis was the most frequent diagnosis (55%). Of all participants, 7.5% had known depression and 5.5% known anxiety. Mental hes was found in rheumatic patients. Rheumatologists should be aware of these comorbidities, especially in the era of COVID-19 pandemic.Irritable bowel syndrome (IBS) is the commonest cause of recurrent abdominal pain in children in both more developed and developing parts of the world. It is characterized by abdominal pain that is improved by defecation and whose onset is associated with a change in stool form and/or frequency and is not explained by structural or biochemical abnormalities. A number of potential patho-physiological mechanisms have been described, but so far the exact underlying etiology of IBS is unclear. Likewise, no optimal treatment has ever been found neither for adult nor for pediatric patients. Current therapeutic options include drugs, dietary interventions and biopsychosocial therapies. selleck chemicals The present review aims at evaluating the scientific evidence supporting the efficacy of these treatments for children with IBS.The advent of new genome-wide sequencing technologies has uncovered abnormal RNA modifications and RNA editing in a variety of human cancers. The discovery of reversible RNA N6-methyladenosine (RNA m6A) by fat mass and obesity-associated protein (FTO) demethylase has led to exponential publications on the pathophysiological functions of m6A and its corresponding RNA modifying proteins (RMPs) in the past decade. Some excellent reviews have summarized the recent progress in this field [1-11]. Compared to the extent of research into RNA m6A and DNA 5-methylcytosine (DNA m5C) [12], much less is known about other RNA modifications and their associated RMPs, such as the role of RNA m5C and its RNA cytosine methyltransferases (RCMTs) in cancer therapy and drug resistance [13-17]. In this review, we will summarize the recent progress surrounding the function, intramolecular distribution and subcellular localization of several major RNA modifications, including 5' cap N7-methylguanosine (m7G) and 2'-O-methylation (Nm), m6A, m5C, A-to-I editing, and the associated RMPs. We will then discuss dysregulation of those RNA modifications and RMPs in cancer and their role in cancer therapy and drug resistance.Cardiovascular Diseases (CVD) remain the leading cause of mortality and morbidity worldwide. To date, significant progress has been made in developing stimuli-responsive nanosystems that can intrinsically interact with pathological microenvironment to achieve site-specific delivery along with on-demand drug release for precise CVD treatment. Herein, this review summarizes recent advances on smart nanosystems in response to a wide range of biological cues, including pH, enzymes, ROS, shear force, ATP, etc., which can boost drug delivery performance or monitor disease progression in a non-invasive manner. The designs, compositions and main outcomes of the single and multi- responsive nanosystems for drug delivery and/or detection purposes are provided and discussed.
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