Examining the factor structure of the Pittsburgh Sleep Quality Index and its determinants among GI cancer patients.

Background: Poor sleep quality is one of the prevalent manifestations experienced by cancer patients. There is a lack of research focusing specifically on sleep quality and affecting factors in Gastrointestinal (GI) cancer patients. This study aimed to assess the potential interaction between dietary, comorbid conditions, demographic, and socioeconomic determinants of sleep quality in GI cancer patients.

Poor Sleep Quality and Its Influencing Factors Among Iranian Patients with Esophageal and Gastric Cancer.

Background: Sleep quality is a notable factor of well-being. It also may play a role in the development and progression of chronic diseases and cancers. Therefore, this study was performed to investigate poor sleep quality and its influencing factors among Iranian patients with esophageal and gastric cancer.

Effect of Temperature and Salinity on Survival of Protoscoleces of Hydatid Cyst in Liver .

Hydatid cyst is the metacestode stage of that occurs in herbivores and humans as intermediate hosts by consuming parasite eggs through forage and vegetables. Carnivores, as definitive hosts, become infected by consuming infected vesicles of herbivores. The most effective treatment for a hydatid cyst is surgical operation.

Effects of irradiation on the survival of Sarcocystis bradyzoites in beef.

Background: Sarcocystis is a food-borne zoonotic protozoan whose final hosts are humans, dogs, cats, and other carnivores and intermediate hosts are birds and mammals, especially humans and herbivores. Humans become infected by eating raw and undercooked meat contaminated with bradyzoites or by consuming water or food contaminated with the sporocyst stage of the parasite.

Objectives: The aim of this study was to investigate the effects of gamma radiation and electron beam on the survival rate of Sarcocystis bradyzoites in infected beef and to determine the effective dose.

Detection of Toxoplasma gondii infection in buffaloes (Bubalus bubalis) and cattle (Bos taurus) at the Tabriz abattoir, Iran.

Background: Toxoplasma gondii is a widely prevalent zoonotic protozoan parasite in humans and warm-blooded animals worldwide. Infection of humans by this parasite can result in severe clinical symptoms, particularly in individuals with congenital toxoplasmosis or immunocompromised patients. Contamination mainly occurs through foodborne routes, especially the consumption of raw or undercooked meat from animals.

Prevalence and risk factors of Linguatula spp. in slaughter animals in Tabriz, Iran, and methods for nymphal stage inactivation.

Linguatula is a food born zoonotic parasite in which carnivores and herbivores serve as final and intermediate hosts, respectively. Human infection with Linguatula spp. occurs following consumption of raw or undercooked infected internal organs of the intermediate host and/or consumption of water and/or vegetables contaminated with eggs released from final hosts.

Antimicrobial Effects of Different Synbiotic Compounds against Pathogenic Bacteria Isolated from Beef, Mutton, and Chicken.

Today, there has been a growing interest in synbiotic usage in the food industry to solve the problems related to food contaminations. The present study aimed to evaluate the antibacterial effects of nine symbiotic compounds on bacteria isolated from different meat types. Pathogenic bacteria were isolated from 60 different meat samples.

Explaining the physics of transfer learning in data-driven turbulence modeling.

Transfer learning (TL), which enables neural networks (NNs) to generalize out-of-distribution via targeted re-training, is becoming a powerful tool in scientific machine learning (ML) applications such as weather/climate prediction and turbulence modeling. Effective TL requires knowing (1) how to re-train NNs? and (2) what physics are learned during TL? Here, we present novel analyses and a framework addressing (1)-(2) for a broad range of multi-scale, nonlinear, dynamical systems. Our approach combines spectral (e.

Technical and engineering considerations for designing therapeutics and delivery systems.

The newly-emerged pathological conditions and increased rates of drug resistance necessitate application of the state-of-the-art technologies for accelerated discovery of the therapeutic candidates and obtaining comprehensive knowledge about their targets, action mechanisms, and interactions within the body including those between the receptors and drugs. Using the physics- and chemistry-based modern techniques for theranostic purposes, preparing smart carriers, local delivery of genes or drugs, and enhancing pharmaceutical bioavailability could be of great value against the hard-to-treat diseases and growing drug resistance. Besides the artificial intelligence- and quantum-based techniques, crystal engineering capable of designing new molecules with appropriate characteristics, improving the stability and bioavailability of poorly soluble drugs, and efficient carrier development could play a crucial role in manufacturing efficient pharmaceuticals and reducing the adverse events.

Nanobionics: From plant empowering to the infectious disease treatment.

Infectious diseases (ID) are serious threats against the global health and socio-economic conditions. Vaccination usually plays a key role in disease prevention, however, insufficient efficiency or immunogenicity may be quite challenging. Using the advanced vectors for delivery of vaccines with suitable efficiency, safety, and immune-modulatory activity, and tunable characteristics could be helpful, but there are no systematic reviews confirming the capabilities of the vaccine delivery systems for covering various types of pathogens.

Discovery of interpretable structural model errors by combining Bayesian sparse regression and data assimilation: A chaotic Kuramoto-Sivashinsky test case.

Models of many engineering and natural systems are imperfect. The discrepancy between the mathematical representations of a true physical system and its imperfect model is called the model error. These model errors can lead to substantial differences between the numerical solutions of the model and the state of the system, particularly in those involving nonlinear, multi-scale phenomena.

Presenting a bioactive nanotherapeutic agent for colon cancer treatment.

Colon cancer (CC) is one of the major causes of death worldwide. Insufficient drug concentration, non-specificity, or serious adverse effects of the conventional chemotherapeutic agents necessitate application of more effective treatment options. Herein, poly-ursolic acid, a polymer with anticancer effect, has been self-assembled for producing nanoparticles (NPs) for delivery of irinotecan (IRN) which is usually associated with poor solubility and severe adverse effects.

Molecular detection, biotyping and serotyping of Yersinia enterocolitica isolated from chicken livers in Tabriz.

Introduction And Purpose: Yersinia enterocolitica belongs to the family of Enterobacteriaceae and is a psychrophilic pathogen that is associated with foodborne infections. It usually causes gastroenteritis, mesenteric lymphadenitis, and septicemia. This study aimed to molecular detection, biotyping, and serotyping of Yersinia enterocolitica isolated from chicken livers in Tabriz.

A Pilot study on correlation between Zinc and Magnesium serum concentrations and coronary slow flow phenomenon.

Background: The pathophysiology of slow flow includes microvascular disorders, endothelial dysfunction, subclinical atherosclerosis, inflammation and anatomical factors. The role of magnesium and zinc in the development of microvascular and endothelial dysfunctions as well as atherosclerosis has been proven in previous studies, and the mechanism of the development has been studied. The aim of current study was to evaluate the serum concentration of zinc and magnesium in patients with epicardial coronary artery slow flow.

Development of a novel nanoformulation against the colorectal cancer.

Colorectal cancer (CRC) with high metastasis rates has been known as a major cause of death worldwide. Lack of the specificity and insufficient concentrations of traditional chemotherapeutics at tumor site and their severe adverse effects necessitate development of new treatment strategies such as designing suitable nanocarriers for delivery of drugs, improving their pharmacological profiles and reducing adverse effects. We have developed a platform based on the poly-ursolic acid (poly-UA), a polymeric system with potential anticancer effect.

The biomedical significance of multifunctional nanobiomaterials: The key components for site-specific delivery of therapeutics.

The emergence of nanotechnology has provided the possibilities to overcome the potential problems associated with the development of pharmaceuticals including the low solubility, non-specific cellular uptake or action, and rapid clearance. Regarding the biomaterials (BMs), huge efforts have been made for improving their multi-functionalities via incorporation of various nanomaterials (NMs). Nanocomposite hydrogels with suitable properties could exhibit a variety of beneficial effects in biomedicine particularly in the delivery of therapeutics or tissue engineering.

The significance of bioengineered nanoplatforms against SARS-CoV-2: From detection to genome editing.

COVID-19 outbreak can impose serious negative impacts on the infrastructures of societies including the healthcare systems. Despite the increasing research efforts, false positive or negative results that may be associated with serologic or even RT-PCR tests, inappropriate or variable immune response, and high rates of mutations in coronavirus may negatively affect virus detection process and effectiveness of the vaccines or drugs in development. Nanotechnology-based research attempts via developing state-of-the-art techniques such as nanomechatronics ones and advanced materials including the sensors for detecting the pathogen loads at very low concentrations or site-specific delivery of therapeutics, and real-time protections against the pandemic outbreaks by nanorobots can provide outstanding biomedical breakthroughs.

The capabilities of nanoelectronic 2-D materials for bio-inspired computing and drug delivery indicate their significance in modern drug design.

Remarkable advancements in the computational techniques and nanoelectronics have attracted considerable interests for development of highly-sophisticated materials (Ms) including the theranostics with optimal characteristics and innovative delivery systems. Analyzing the huge amounts of multivariate data and solving the newly-emerged complicated problems including the healthcare-related ones have created increasing demands for improving the computational speed and minimizing the consumption of energy. Shifting towards the non-von Neumann approaches enables performing specific computational tasks and optimizing the processing of signals.

Physics-informed machine learning: case studies for weather and climate modelling.

Machine learning (ML) provides novel and powerful ways of accurately and efficiently recognizing complex patterns, emulating nonlinear dynamics, and predicting the spatio-temporal evolution of weather and climate processes. Off-the-shelf ML models, however, do not necessarily obey the fundamental governing laws of physical systems, nor do they generalize well to scenarios on which they have not been trained. We survey systematic approaches to incorporating physics and domain knowledge into ML models and distill these approaches into broad categories.

Coating of ferulic acid-loaded silk fibroin nanoparticles with neutrophil membranes: A promising strategy against the acute pancreatitis.

Nanotechnology-based approaches have enabled overcoming the challenging issues such as the rapid clearance, poor solubility, and non-specific action or cellular uptake of drugs. In this study, we have evaluated the therapeutic effects of the phenolic compound, ferulic acid (FA), in the acute pancreatitis (AP) as this phenolic compound has demonstrated promising effects against the oxidative stress and inflammatory reactions. In order to overcome the poor solubility and bioavailability of FA, it was entrapped into the nanoparticles (NPs) based on the silk fibroin (SF) as a biomimetic substance.

Zoonotic parasitic organisms on vegetables: Impact of production system characteristics on presence, prevalence on vegetables in northwestern Iran and washing methods for removal.

Fresh vegetables are essential components of a healthy and nutritious diet, but if consumed raw without proper washing and/or disinfection, can be important agents of transmission of enteric pathogens. This study aimed to determine the prevalence of zoonotic parasites on vegetables freshly harvested and "ready to eat" vegetables from greengrocers and markets in northwestern Iran. In addition, the effect of cropping system and season on contamination levels were assessed as well as the efficacy of washing procedures to remove parasites from the vegetables.

Prevalence of Linguatula sp., a food-borne zoonotic aberrant arthropod, in river buffaloes slaughtered at Tabriz slaughterhouse, Iran.

Background: Canids and herbivores are the definitive and intermediate hosts of Linguatula sp., respectively.

Methods: Mesenteric lymph nodes (n=32 525) were randomly collected from 7585 buffaloes from July 2016 to July 2019 and examined macroscopically.

Nanotheranostics against COVID-19: From multivalent to immune-targeted materials.

Destructive impacts of COVID-19 pandemic worldwide necessitates taking more appropriate measures for mitigating virus spread and development of the effective theranostic agents. In general, high heterogeneity of viruses is a major challenging issue towards the development of effective antiviral agents. Regarding the coronavirus, its high mutation rates can negatively affect virus detection process or the efficiency of drugs and vaccines in development or induce drug resistance.

Analog Forecasting of Extreme-Causing Weather Patterns Using Deep Learning.

Numerical weather prediction models require ever-growing computing time and resources but, still, have sometimes difficulties with predicting weather extremes. We introduce a data-driven framework that is based on analog forecasting (prediction using past similar patterns) and employs a novel deep learning pattern-recognition technique (capsule neural networks, CapsNets) and an impact-based autolabeling strategy. Using data from a large-ensemble fully coupled Earth system model, CapsNets are trained on midtropospheric large-scale circulation patterns (Z500) labeled 0-4 depending on the existence and geographical region of surface temperature extremes over North America several days ahead.