Modeling the Temperature-Dependent Size Change of Polydisperse Nano-objects using a Deep Generative Model.

Modern microscopy techniques can be used to investigate soft nano-objects at the nanometer scale. However, time-consuming microscopy measurements combined with low numbers of observable polydisperse objects often limit the statistics. We propose a method for identifying the most representative objects from their respective point clouds. These point cloud data are obtained, for example, through the localization of single emitters in super-resolution fluorescence microscopy. External stimuli, such as temperature, can cause changes in the shape and properties of adaptive objects. Due to the demanding and time-consuming nature of super-resolution microscopy experiments, only a limited number of temperature steps can be performed. Therefore, we propose a deep generative model that learns the underlying point distribution of temperature-dependent microgels, enabling the reliable generation of unlimited samples with an arbitrary number of localizations. Our method greatly cuts down the data collection effort across diverse experimental conditions, proving invaluable for soft condensed matter studies.

Advances in medical image analysis with vision Transformers: A comprehensive review.

The remarkable performance of the Transformer architecture in natural language processing has recently also triggered broad interest in Computer Vision. Among other merits, Transformers are witnessed as capable of learning long-range dependencies and spatial correlations, which is a clear advantage over convolutional neural networks (CNNs), which have been the de facto standard in Computer Vision problems so far. Thus, Transformers have become an integral part of modern medical image analysis. In this review, we provide an encyclopedic review of the applications of Transformers in medical imaging. Specifically, we present a systematic and thorough review of relevant recent Transformer literature for different medical image analysis tasks, including classification, segmentation, detection, registration, synthesis, and clinical report generation. For each of these applications, we investigate the novelty, strengths and weaknesses of the different proposed strategies and develop taxonomies highlighting key properties and contributions. Further, if applicable, we outline current benchmarks on different datasets. Finally, we summarize key challenges and discuss different future research directions. In addition, we have provided cited papers with their corresponding implementations in https://github.com/mindflow-institue/Awesome-Transformer.

A Review of micro RNAs changes in T2DM in animals and humans.

Type 2 diabetes mellitus (T2DM) and its associated complications have become a crucial public health concern in the world. According to the literature, chronic inflammation and the progression of T2DM have a close relationship. Accumulated evidence suggests that inflammation enhances the insulin secretion lost by islets of Langerhans and the resistance of target tissues to insulin action, which are two critical features in T2DM development. Based on recently highlighted research that plasma concentration of inflammatory mediators such as tumor necrosis factor α and interleukin-6 are elevated in insulin-resistant and T2DM, and it raises novel question marks about the processes causing inflammation in both situations. Over the past few decades, microRNAs (miRNAs), a class of short, noncoding RNA molecules, have been discovered to be involved in the regulation of inflammation, insulin resistance, and T2DM pathology. These noncoding RNAs are specifically comprised of RNA-induced silencing complexes and regulate the expression of specific protein-coding genes through various mechanisms. There is extending evidence that describes the expression profile of a special class of miRNA molecules altered during T2DM development. These modifications can be observed as potential biomarkers for the diagnosis of T2DM and related diseases. In this review study, after reviewing the possible mechanisms involved in T2DM pathophysiology, we update recent information on the miRNA roles in T2DM, inflammation, and insulin resistance.

Diffusion models in medical imaging: A comprehensive survey.

Denoising diffusion models, a class of generative models, have garnered immense interest lately in various deep-learning problems. A diffusion probabilistic model defines a forward diffusion stage where the input data is gradually perturbed over several steps by adding Gaussian noise and then learns to reverse the diffusion process to retrieve the desired noise-free data from noisy data samples. Diffusion models are widely appreciated for their strong mode coverage and quality of the generated samples in spite of their known computational burdens. Capitalizing on the advances in computer vision, the field of medical imaging has also observed a growing interest in diffusion models. With the aim of helping the researcher navigate this profusion, this survey intends to provide a comprehensive overview of diffusion models in the discipline of medical imaging. Specifically, we start with an introduction to the solid theoretical foundation and fundamental concepts behind diffusion models and the three generic diffusion modeling frameworks, namely, diffusion probabilistic models, noise-conditioned score networks, and stochastic differential equations. Then, we provide a systematic taxonomy of diffusion models in the medical domain and propose a multi-perspective categorization based on their application, imaging modality, organ of interest, and algorithms. To this end, we cover extensive applications of diffusion models in the medical domain, including image-to-image translation, reconstruction, registration, classification, segmentation, denoising, 2/3D generation, anomaly detection, and other medically-related challenges. Furthermore, we emphasize the practical use case of some selected approaches, and then we discuss the limitations of the diffusion models in the medical domain and propose several directions to fulfill the demands of this field. Finally, we gather the overviewed studies with their available open-source implementations at our GitHub.1 We aim to update the relevant latest papers within it regularly.

SegPC-2021: A challenge & dataset on segmentation of Multiple Myeloma plasma cells from microscopic images.

Multiple Myeloma (MM) is an emerging ailment of global concern. Its diagnosis at the early stages is critical for recovery. Therefore, efforts are underway to produce digital pathology tools with human-level intelligence that are efficient, scalable, accessible, and cost-effective. Following the trend, a medical imaging challenge on "Segmentation of Multiple Myeloma Plasma Cells in Microscopic Images (SegPC-2021)" was organized at the IEEE International Symposium on Biomedical Imaging (ISBI), 2021, France. The challenge addressed the problem of cell segmentation in microscopic images captured from the slides prepared from the bone marrow aspirate of patients diagnosed with Multiple Myeloma. The challenge released a total of 775 images with 690 and 85 images of sizes 2040×1536 and 1920×2560 pixels, respectively, captured from two different (microscope and camera) setups. The participants had to segment the plasma cells with a separate label on each cell's nucleus and cytoplasm. This problem comprises many challenges, including a reduced color contrast between the cytoplasm and the background, and the clustering of cells with a feeble boundary separation of individual cells. To our knowledge, the SegPC-2021 challenge dataset is the largest publicly available annotated data on plasma cell segmentation in MM so far. The challenge targets a semi-automated tool to ensure the supervision of medical experts. It was conducted for a span of five months, from November 2020 to April 2021. Initially, the data was shared with 696 people from 52 teams, of which 41 teams submitted the results of their models on the evaluation portal in the validation phase. Similarly, 20 teams qualified for the last round, of which 16 teams submitted the results in the final test phase. All the top-5 teams employed DL-based approaches, and the best mIoU obtained on the final test set of 277 microscopic images was 0.9389. All these five models have been analyzed and discussed in detail. This challenge task is a step towards the target of creating an automated MM diagnostic tool.

Self-supervised Semantic Segmentation: Consistency over Transformation.

Accurate medical image segmentation is of utmost importance for enabling automated clinical decision procedures. However, prevailing supervised deep learning approaches for medical image segmentation encounter significant challenges due to their heavy dependence on extensive labeled training data. To tackle this issue, we propose a novel self-supervised algorithm, S3-Net, which integrates a robust framework based on the proposed Inception Large Kernel Attention (I-LKA) modules. This architectural enhancement makes it possible to comprehensively capture contextual information while preserving local intricacies, thereby enabling precise semantic segmentation. Furthermore, considering that lesions in medical images often exhibit deformations, we leverage deformable convolution as an integral component to effectively capture and delineate lesion deformations for superior object boundary definition. Additionally, our self-supervised strategy emphasizes the acquisition of invariance to affine transformations, which is commonly encountered in medical scenarios. This emphasis on robustness with respect to geometric distortions significantly enhances the model's ability to accurately model and handle such distortions. To enforce spatial consistency and promote the grouping of spatially connected image pixels with similar feature representations, we introduce a spatial consistency loss term. This aids the network in effectively capturing the relationships among neighboring pixels and enhancing the overall segmentation quality. The S3-Net approach iteratively learns pixel-level feature representations for image content clustering in an end-to-end manner. Our experimental results on skin lesion and lung organ segmentation tasks show the superior performance of our method compared to the SOTA approaches.

Laplacian-Former: Overcoming the Limitations of Vision Transformers in Local Texture Detection.

Vision Transformer (ViT) models have demonstrated a breakthrough in a wide range of computer vision tasks. However, compared to the Convolutional Neural Network (CNN) models, it has been observed that the ViT models struggle to capture high-frequency components of images, which can limit their ability to detect local textures and edge information. As abnormalities in human tissue, such as tumors and lesions, may greatly vary in structure, texture, and shape, high-frequency information such as texture is crucial for effective semantic segmentation tasks. To address this limitation in ViT models, we propose a new technique, Laplacian-Former, that enhances the self-attention map by adaptively re-calibrating the frequency information in a Laplacian pyramid. More specifically, our proposed method utilizes a dual attention mechanism via efficient attention and frequency attention while the efficient attention mechanism reduces the complexity of self-attention to linear while producing the same output, selectively intensifying the contribution of shape and texture features. Furthermore, we introduce a novel efficient enhancement multi-scale bridge that effectively transfers spatial information from the encoder to the decoder while preserving the fundamental features. We demonstrate the efficacy of Laplacian-former on multi-organ and skin lesion segmentation tasks with +1.87% and +0.76% dice scores compared to SOTA approaches, respectively. Our implementation is publically available at GitHub.

Analytical methods in fatty acid analysis for microbial applications: the recent trends.

Fatty acids are among the most important components of many biological systems and have been highlighted in many research fields in recent decades. In the food industry, it is important to check the amount and types of fatty acids in edible oils, beverages and other foods products, and checking the fatty acids parameters are among the quality control parameters for those products. In medical applications, investigation of fatty acids in biological samples and comparing imbalances in them can help to diagnose some diseases. On the other hand, the development of cell factories for the production of biofuels and other valuable chemicals requires the accurate analysis of fatty acids, which serve as precursors in development of those products. As a result, given all these different applications of fatty acids, rapid and accurate methods for characterization and quantification of fatty acids are essential. In recent years, various methods for the analysis of fatty acids have been proposed, which according to the specific purpose of the analysis, some of them can be used with consideration of speed, accuracy and cost. In this article, the available methods for the analysis of fatty acids are reviewed with a special emphasis on the analysis of microbial samples to pave the way for more widespread metabolic engineering research.

Overexpression of protein kinase Mζ in the hippocampus mitigates Alzheimer's disease-related cognitive deficit in rats.

Accumulation of amyloid beta (Aß) soluble forms in the cerebral parenchyma is the mainstream concept underlying memory deficit in the early phase of Alzheimer's disease (AD). PKMζ plays a critical role in the maintenance of long-term memory. Yet, the role of this brain-specific enzyme has not been addressed in AD. We examined the impact of hippocampal PKMζ overexpression on AD-related memory impairment in rats. Oligomeric form of Aß (oAß) or vehicle was bilaterally microinjected into the dorsal hippocampus of male Wistar rats under stereotaxic surgery. One week later, 2 µl of lentiviral vector (108 T.U. / ml.) encoding PKMζ genome was microinjected into the dorsal hippocampus. Seven days later, behavioral performance was assessed using shuttle box and Morris water maze. The expression levels of GluA1, GluA2 and KCC2 were determined in the hippocampus using western blot technique. Our data showed that oAß impairs both passive avoidance and spatial learning and memory. However, overexpression of PKMζ in the dorsal hippocampus restored the behavioral performance. This improving effect was blocked by microinjection of ZIP, a PKMζ inhibitor, into the hippocampus. oAß or PKMζ did not significantly change GluA1 level in the hippocampus. Furthermore, PKMζ failed to restore elevated KCC2 level induced by oAß. However, oAß decreased GluA2 level, and overexpression of PKMζ restored its expression toward the control level. In conclusion, hippocampal overexpression of PKMζ restored memory dysfunction induced by amyloidopathy in part, through preserving hippocampal GluA2 containing AMPA receptors. PKMζ's signaling pathway could be considered as a therapeutic target to battle memory deficits in the early phase of AD.

Preparation and characterization of poly-lactic acid based films containing propolis ethanolic extract to be used in dry meat sausage packaging.

In this study, active poly lactic acid (PLA) films containing 0, 10, 20 and 40% w/w propolis extract (PE), as active agent, were developed. A high amount of phenolic content (PC) was measured in PE. The antioxidant effect of active PLA films was determined by measuring the PC of sausage slices after 0, 2 and 4 days storage at refrigerator. Results showed that phenolic compounds of PE were released from PLA films in quantities proportional to PE concentration. Disc diffusion test indicated that PE showed an inhibitory effect against Staphylococcus aureus and Pseudomonas aeruginosa bacterial species but was more effective against gram-positive species. PE containing PLA films had antimicrobial effect on S. aureus while in the case of P. aeruginosa, PLA/PE films needed polyethylene glycol (PEG)/CaCO3 content to show inhibitory effect. Addition of PE changed the tensile strength, elongation at break and elastic modulus of PLA films negatively. However, addition of PEG/CaCO3 improved the film mechanical properties and antimicrobial effect of films.

Serum levels of nitrite/nitrate, lipid profile, and Fasting Plasma Glucose and their associations in subclinical hypothyroid women before and after a two month treatment by levothyroxine.

BACKGROUND: Subclinical hypothyroidism (sHT) is a condition defined by elevated TSH values with normal levels of free thyroid hormones. Altered metabolic status is one of the consequences of sHT which can affect serum levels of FPG, lipid profile, and nitric oxide which propounds cardiovascular consequences per se. The aim of this study was to determine the possible effects of sHT on nitrite/nitrate levels, as a marker of endothelial performance. MATERIALS AND METHODS: 50 females were enrolled in this study, 25 women as control group and 25 women as case group (evaluated two times: before and after levothyroxine therapy). Blood samples were collected and levels of FPG, lipid profile components, and nitric oxide were measured spectrophotometrically. The data were analyzed using SPSS version 18 software. RESULTS: The levels of lipid profile (except for TG between before and after group, p < 0.05) and nitric oxide did not differ in groups while FPG was significantly higher in case groups in comparison to control group (p < 0.001). Nitric oxide had no correlations with any of variables except for LDL in after treatment group (p < 0.05 and r = 0.397). CONCLUSION: Nitric oxide does not have correlation with components of lipid profile (except for LDL) or FPG and has no differences in subclinical hypothyroid patients and control group. Levothyroxine therapy during 2 months cannot alter the levels of nitric oxide in subclinical hypothyroid patients.

Evaluation of oxidative stress and thyroid hormone status in hemodialysis patients in Gorgan.

AIMS: The aim of this study focused on serum malondialdehyde (MDA) levels and erythrocyte superoxide dismutase (SOD) and catalase (CAT) activities in hemodialysis patients and compared with control groups. MATERIALS AND METHODS: Forty-five hemodialyzed patients and 45 control groups recruited in this study. Serum creatinine and urea, thyroid hormones (THs) levels and erythrocyte antioxidant enzyme activities were determined. RESULTS: Hemodialysis (HD) patients showed higher levels of MDA than control groups (P < 0.01), but the levels of thyroxin (T3), free triiodothyronine (fT3), and free thyroxin (fT4), SOD and CAT were low in HD patients (P < 0.01). Serum T3, fT3, and fT4 levels were significantly negative correlated with MDA (P < 0.01). CONCLUSION: It is concluded that serum lipid peroxidation is markedly increased in HD patients. This means that elevated reactive oxygen species may interact with the lipid molecules in HD patients. HD may cause significant changes in TH levels. Thyroid-stimulating hormone level in HD patients is slightly similar to that of control groups. This suggests that thyroid is able to resynthesize for hormonal urinary losses.

The comparison of acute myocardial infarction serum anticardiolipin antibody with healthy subjects, in Gorgan (Northern Iran).

Arthrosclerosis considered one of the most important causes of morbidity and mortality in industrial and developing countries. The rate of myocardial infarction in some countries is about 2 million annually with 1/4 of them is leading to death. This study was a case-control research, which was carried out as cross-sectional project in two groups, healthy and case subjects. The mean age and standard deviation of patients and control groups were nearly equal (55 +/- 10). The ELISA technique was applied to measure the serum antibody level. The sample populations in each group were exactly the same (120 person in each group). The gender distribution in case and control group was 44 female, 76 male and 45 female and 75 male, respectively. The Mean +/- SD of IgM in case group and control groups were 3.10 +/- 2.54 and 1.54 +/- 1.00, respectively. The Mean +/- SD for IgG in case and control groups were 5.90 +/- 3.84 and 3.08 +/- 1.95, respectively. The differences, between the mean of IgM and IgG in two groups of cases and control statistically were significant (p = 0.0001). In case group the Mean +/- SD for IgM for men and woman were 2.98 +/- 1.97 and 3.17 +/- 2.83, respectively, which this differences statistically, were not significant. In case group the Mean +/- SD for IgG were 5.14 +/- 3.45 and 6.35. The above findings indicated that the average of both IgG and IgM are higher among men compared to women. Due to high prevalence of acute coronary syndrome in Iran, the determination of anticardiolipin antibody (IgG) are applied for suspected acute coronary syndrome patients and further prevention measure should be taken for patient with higher serum anticardiolipin antibody.

Female reproduction physiology adversely manipulated by thyroid disorders: a review of literature.

Proper thyroid function is vital to have a healthy reproduction system. Female sex hormones are altered due to hypothyroidism and hyperthyroidism. Female reproduction system is negatively manipulated by both hyperthyroidism and hypothyroidism and menstrual disorders are the ultimate consequences. Hypomenorrhea, polymenorrhea and oligomenorrhea are the clinical manifestation associated with hyperthyroidism and hypothyroidism, respectively. The female infertility is also adversely affected by thyrotoxicosis and myxedema, the clinical presentation of hyper and hypothyroidism. The simultaneous existence of autoimmunity which is present among some portion of pregnant women may aggravate the clinical manifestation of thyroid disorders in female reproductive physiology. Abortion, premature infants, low birth infant, are among clinical presentation of overt hypothyroidism. Auto antibody against thyroid stimulating hormone receptor and eventual hyperthyroidism considered as risk factors which require extra attention while the thyroid disorder is clinically managed during pregnancy to prevent the fetus from abnormal metabolism. The aim of this review is to elaborate the adverse role of hyperthyroidism and hypothyroidism in female reproduction physiology.

Comparing efficiency and root surface morphology after scaling with Er:YAG and Er,Cr:YSGG lasers.

The purpose of this study was to investigate the root morphology of teeth and efficiency of scaling after using Er:YAG and Er,Cr:YSGG lasers. Thirty-two periodontally hopeless teeth were extracted. The border of an appropriate calculus was marked using a diamond bur on each tooth, and the calculus was divided into two almost equal parts. An Er,Cr:YSGG laser with pulse energy of 50 mJ, power of 1 W, and energy density of 17.7 J/cm2 and an Er:YAG laser with pulse energy of 200 mJ, power of 2.4 W, and energy density of 21 J/cm2 were used to remove the calculus. The time for scaling was recorded for each group, and using stereomicroscopic analysis, the calculus remnant, carbonization, and number of craters were investigated. The mean time required for calculus removal in the Er,Cr:YSGG and Er:YAG laser groups was 15.22 ± 6.18 seconds and 7.12 ± 4.11 seconds, respectively. The efficiency of calculus removal in the Er:YAG laser group was significantly higher than in the Er,Cr:YSGG laser group. Under stereomicroscope examination, no carbonization or remaining calculus was found in samples from either group, but all samples had craters. The number of craters in the Er,Cr:YSGG laser group was significantly higher than in the Er:YAG laser group. According to the parameters used and limitations of this study, there was no significant difference in efficiency per power for calculus removal between the two groups.

Genetic Polymorphism of Cytochrome p450 (2C19) Enzyme in Iranian Turkman Ethnic Group.

OBJECTIVE: Different findings indicate that CYP2C plays a clinical role in determining interindividual and interethnic differences in drug effectiveness. The ethnic differences in the frequency of CYP2C19 mutant alleles continue to be a significant study topic. The aim of the present study was to assess the frequency of allelic variants of CYP2C19 in Turkman ethnic groups and compare them with the frequencies in other ethnic populations. METHODS: The study group included 140 unrelated healthy ethnic Turkman subject referred to the Health Center. Genotyping of CYP2C19 alleles (CYP2C19*1, CYP2C19*2, and CYP2C19*3 alleles) was carried out by Polymerase Chain Reaction-Restriction Fragment Length Polymorphism technique. RESULTS: The allele frequency of CYP2C19*1, CYP2C19*2 and CYP2C19*3 were 56.43%, 23.57% and 20%, respectively. The result also showed that 39.7% of subjects expressed the CYP2C19*1/*1 genotype. While 42.1%, 9.3%, 9.3% and 1.4% expressed CYP2C19*1/*2, CYP2C19*1/*3, CYP2C19*2/*2 and CYP2C19*3/*3 genotypes, respectively. The genotype CYP2C19*2/*3 was not expressed in this study population. The findings suggested that 10% of subjects were poor metabolizers by expressing CYP2C19*2/*2 and CYP2C19*3/*3 genotypes. Fifty one percent of subjects were intermediate metabolizers having CYP2C19*1/*2, CYP2C19*2/*3 and CYP2C19*1/*3 genotypes and 37.86% were found to be extensive metabolizers expressing CYP2C19*1/*1 genotype. The frequency of intermediate metabolizers genotype was high (51%) in Turkman ethnic groups. CONCLUSION: This study showed that the determined allelic variants of CYP2C19 (CYP2C19*2 and CYP2C19*3 mutations) in Turkman ethnic group are comparable to other populations. These findings could be useful for the clinicians in different country to determine optimal dosage and effectiveness of drugs metabolized by this polymorphic enzyme.

Spectrophotometric determination of sulfide based on peroxidase inhibition by detection of purpurogallin formation.

This paper presents a new method for spectrophotometirc detection of sulfide applying fungal peroxidase immobilized on sodium alginate. The sensing scheme was based on decrease of the absorbance of the orange compound, purpurogallin produced from pyrogallol and H2O2 as substrates, due to the inhibition of peroxidase by sulfide. Absorbance of purpurogallin was detected at 420nm by using a spectrophotometer. The proposed method could successfully detect the sulfide in the concentration range of 0.6-7.0µM with a detection limit of 0.4µM. The kinetic parameters of Michaelis-Menten with and without sulfide were also calculated. Possible inhibition mechanism of peroxidase by sulfide was deduced according to the variation of parameters and uncompetitive mechanism was observed with respect to hydrogen peroxide. The current method provides an easy to use method for sulfide detection in water samples.

A review of literatures on the adverse effects of thyroid abnormalities and liver disorders: an overview on liver dysfunction and hypothyroidism.

The healthy thyroid is vital for the liver metabolism. The liver also plays an important role in the metabolism of thyroid hormones. Thyroid and liver diseases can apparently have an adverse effects on each other organs. The main concept behind this present review is to analyze the coordination existed among thyroid and liver and the pathophysiology surrounding these two vital organs in human metabolism.

A review of literature on the adverse effects of hyperthyroidism on the heart functional behavior.

Thyroid hormones play an important role on the physiological chemistry of heart and vascular systems in healthy subjects. Any thyroid disorders accompanied with alteration of effective concentration of thyroid hormones cause heart dysfunctions. Thyrotoxicosis is a term given for the clinical manifestation of hyperthyroidism which can invoke heart and vascular abnormalities through the mechanism at heart muscle cells nuclear level. Thyrotoxicosis can play positive roles for heart disorders including atrial fibrillation, left ventricular hypertrophy and right ventricular systolic dysfunction, which are considered as major risk factors for heart abnormalities. Miscalculation of heart dysfunctions related thyrotoxicosis in cardiovascular patients might be avoided through careful laboratory measurements of T4 and T3 to exclude any possible thyroid hormone-related heart diseases.

Effect of peppermint oil on serum lipid peroxidation and hepatic enzymes after immobility stress in mice.

This study was undertaken to determine the influences of various doses of peppermint oil on the hepatic en-zymes, alanine transaminase, apartate tranaminase, alkaline phosphotase and gamma glutamyl transferase and the level of malondialdehyde in the serum of mice with and without immobility stress. The mice exposed to drink water, 0.9, 27 and 60 mg/kg peppermint oil from the days 1 to 5 for a period of 4 h before and after immobility stress. Serum MDA in-creased in treatment group II, III and IV after immobility stress. There was a significant decrease in ALT in treatment group III and IV after immobility stress. There were also significant decreases in ALP and GGT in treatment group IV af-ter immobility stress. This result may suggest that, MDA level is higher in immobilization stress group than in the un-immobilized animals in serum and this results show that enzyme activities decreased after immobilization stress.