Serum amyloid A and mitochondrial DNA in extracellular vesicles are novel markers for detecting traumatic brain injury in a mouse model.

This study investigates the potential use of circulating extracellular vesicles' (EVs) DNA and protein content as biomarkers for traumatic brain injury (TBI) in a mouse model. Despite an overall decrease in EVs count during the acute phase, there was an increased presence of exosomes (CD63+ EVs) during acute and an increase in microvesicles derived from microglia/macrophages (CD11b+ EVs) and astrocytes (ACSA-2+ EVs) in post-acute TBI phases, respectively. Notably, mtDNA exhibited an immediate elevation post-injury. Neuronal (NFL) and microglial (Iba1) markers increased in the acute, while the astrocyte marker (GFAP) increased in post-acute TBI phases. Novel protein biomarkers (SAA, Hp, VWF, CFD, CBG) specific to different TBI phases were also identified. Biostatistical modeling and machine learning identified mtDNA and SAA as decisive markers for TBI detection. These findings emphasize the importance of profiling EVs' content and their dynamic release as an innovative diagnostic approach for TBI in liquid biopsies.

Challenges and Opportunities in Deep Reinforcement Learning With Graph Neural Networks: A Comprehensive Review of Algorithms and Applications.

Deep reinforcement learning (DRL) has empowered a variety of artificial intelligence fields, including pattern recognition, robotics, recommendation systems, and gaming. Similarly, graph neural networks (GNNs) have also demonstrated their superior performance in supervised learning for graph-structured data. In recent times, the fusion of GNN with DRL for graph-structured environments has attracted a lot of attention. This article provides a comprehensive review of these hybrid works. These works can be classified into two categories: 1) algorithmic contributions, where DRL and GNN complement each other with an objective of addressing each other's shortcomings and 2) application-specific contributions that leverage a combined GNN-DRL formulation to address problems specific to different applications. This fusion effectively addresses various complex problems in engineering and life sciences. Based on the review, we further analyze the applicability and benefits of fusing these two domains, especially in terms of increasing generalizability and reducing computational complexity. Finally, the key challenges in integrating DRL and GNN, and potential future research directions are highlighted, which will be of interest to the broader machine learning community.

Sociodemographic profile, work practices, and disease awareness among stone mine workers having silicosis from Central Rajasthan.

Background and Objectives: Silicosis continues to pose an important health hazard among workers having occupational exposure to stone dust. Several studies have addressed clinical presentation, radiographic and pulmonary function abnormalities in workers with silicosis. This study was planned to analyze sociodemographic profile and awareness about several aspects of silicosis among the stone mine workers visiting our centre. Methods: A questionnaire was administered in a convenient sample of eligible subjects over six years period. The questionnaire was aimed to collect sociodemographic variables, like age, gender, educational status, residential background, smoking status etc., apart from information on work-related profile including protective measures taken. Also knowledge and attitude regarding silicosis was assessed. Silicosis awareness index was also calculated as per the response received. Results: Majority of the study subjects were male (96.6%) with rural background (98.5%). 54.1% subjects were in the age group of 30 to 50 years. 81.9% mine workers were illiterate. The common addictions observed among them included smoking (60%), tobacco chewing (34%), alcohol (20%) etc., Varying duration of work exposure was observed with more than 10 years in 63.4% and more than 20 years in 32.2% workers. The commonest work exposing them to stone dust was breaking stones by chisel and hammer (51%) followed by separation of stone slab (20%) and stone drilling (15%). 80.9% subjects were not aware of the term silicosis, more than 80% were not aware of the symptoms and causes of silicosis. Only one fifth subjects were having awareness of using protection against the disease. Overall awareness about silicosis was better among literate and youngers participants. Conclusions: Stone mining industry reflects male dominance, poor literacy, long working hours for many years, financial compulsion to start and continue the job and very poor awareness regarding the disease silicosis and importance of personal protection at workplace.

Early Detection of Pancreatic Cancers Using Liquid Biopsies and Hierarchical Decision Structure.

OBJECTIVE: Pancreatic cancer (PC) is a silent killer, because its detection is difficult and to date no effective treatment has been developed. In the US, the current 5-year survival rate of 11%. Therefore, PC has to be detected as early as possible. METHODS AND PROCEDURES: In this work, we have combined the use of ultrasensitive nanobiosensors for protease/arginase detection with information fusion based hierarchical decision structure to detect PC at the localized stage by means of a simple Liquid Biopsy. The problem of early-stage detection of pancreatic cancer is modelled as a multi-class classification problem. We propose a Hard Hierarchical Decision Structure (HDS) along with appropriate feature engineering steps to improve the performance of conventional multi-class classification approaches. Further, a Soft Hierarchical Decision Structure (SDS) is developed to additionally provide confidences of predicted labels in the form of class probability values. These frameworks overcome the limitations of existing research studies that employ simple biostatistical tools and do not effectively exploit the information provided by ultrasensitive protease/arginase analyses. RESULTS: The experimental results demonstrate that an overall mean classification accuracy of around 92% is obtained using the proposed approach, as opposed to 75% with conventional multi-class classification approaches. This illustrates that the proposed HDS framework outperforms traditional classification techniques for early-stage PC detection. CONCLUSION: Although this study is only based on 31 pancreatic cancer patients and a healthy control group of 48 human subjects, it has enabled combining Liquid Biopsies and Machine Learning methodologies to reach the goal of earliest PC detection. The provision of both decision labels (via HDS) as well as class probabilities (via SDS) helps clinicians identify instances where statistical model-based predictions lack confidence. This further aids in determining if more tests are required for better diagnosis. Such a strategy makes the output of our decision model more interpretable and can assist with the diagnostic procedure. CLINICAL IMPACT: With further validation, the proposed framework can be employed as a decision support tool for the clinicians to help in detection of pancreatic cancer at early stages.

A Non-Invasive Hydration Monitoring Technique Using Microwave Transmission and Data-Driven Approaches.

Dehydration in the human body arises due to inadequate replenishment of fluids. An appropriate level of hydration is essential for optimal functioning of the human body, and complications ranging from mild discomfort to, in severe cases, death, could result from a neglected imbalance in fluid levels. Regular and accurate monitoring of hydration status can provide meaningful information for people operating in stressful environmental conditions, such as athletes, military professionals and the elderly. In this study, we propose a non-invasive hydration monitoring technique employing non-ionizing electromagnetic power in the microwave band to estimate the changes in the water content of the whole body. Specifically, we investigate changes in the attenuation coefficient in the frequency range 2-3.5 GHz between a pair of planar antennas positioned across a participant's arm during various states of hydration. Twenty healthy young adults (10M, 10F) underwent controlled hypohydration and euhydration control bouts. The attenuation coefficient was compared among trials and used to predict changes in body mass. Volunteers lost 1.50±0.44% and 0.49±0.54% body mass during hypohydration and euhydration, respectively. The microwave transmission-based attenuation coefficient (2-3.5 GHz) was accurate in predicting changes in hydration status. The corresponding regression analysis demonstrates that building separate estimation models for dehydration and rehydration phases offer better predictive performance (88%) relative to a common model for both the phases (76%).

Complications of Cytoreductive Surgery and HIPEC in the Treatment of Peritoneal Metastases.

The combined treatment concept of cytoreductive surgery (CRS) and Hyperthermic intraperitoneal chemotherapy (HIPEC) has shown to be an efficient therapeutic option for selected patients with primary and secondary peritoneal carcinomatosis (PC). This strategy represents the standard of care for diseases like pseudomyxoma peritonei and peritoneal mesothelioma, and offers the best long-term results for PC from colorectal cancer. Despite these results, skepticism exists regarding this therapeutic approach partly because of its perceived high toxicity. In this article, we review the current evidence on complications that can occur after CRS and HIPEC and the risk factors associated with increased incidence of morbidity and mortality.

Unveiling Contacts within Macromolecular Assemblies by Solving Minimum Weight Connectivity Inference (MWC) Problems.

Consider a set of oligomers listing the subunits involved in subcomplexes of a macromolecular assembly, obtained e.g. using native mass spectrometry or affinity purification. Given these oligomers, connectivity inference (CI) consists of finding the most plausible contacts between these subunits, and minimum connectivity inference (MCI) is the variant consisting of finding a set of contacts of smallest cardinality. MCI problems avoid speculating on the total number of contacts but yield a subset of all contacts and do not allow exploiting a priori information on the likelihood of individual contacts. In this context, we present two novel algorithms, MILP-W and MILP-WB. The former solves the minimum weight connectivity inference (MWCI), an optimization problem whose criterion mixes the number of contacts and their likelihood. The latter uses the former in a bootstrap fashion to improve the sensitivity and the specificity of solution sets.Experiments on three systems (yeast exosome, yeast proteasome lid, human eIF3), for which reference contacts are known (crystal structure, cryo electron microscopy, cross-linking), show that our algorithms predict contacts with high specificity and sensitivity, yielding a very significant improvement over previous work, typically a twofold increase in sensitivity.The software accompanying this paper is made available and should prove of ubiquitous interest whenever connectivity inference from oligomers is faced.

Successful transradial percutaneous coronary intervention in a patient with dextrocardia and situs inversus.

Dextrocardia with situs inversus is a rare clinical entity with an estimated incidence ranges from 1 in 8000 to 1 in10,000. Percutaneous intervention in patient with dextrocardia and situs inversus is clinically challenging due to abnormal orientation of coronary geometry and the intervention requires appropriate use of guiding catheters, engagement technique, appropriate radiological angles as well as views. In this case-report, we describe percutaneous intervention with stenting in 48-year-old male patient with dextrocardia and situs inversus. We successfully deployed drug-eluting stents in right coronary artery and left circumflex artery.

Informed consent for braces.

The influence of law on the orthodontic profession has greatly increased in the last few decades. Dental law has emerged today as a full-fedged specialty dealing with a variety of areas, like professional negligence, doctor-patient contracts, consumer protection laws, ethics, general and special health legislations and practice regulatory mechanisms. This article highlights the concept of informed consent which is based on the premise that each individual has a right to make decisions concerning his health, disease and treatment. How to cite this article: Jharwal V, Trehan M, Rathore N, Rathee P, Agarwal D, Mathur N. Informed Consent for Braces. Int J Clin Pediatr Dent 2014;7(2):105-108.

Effects of heating and glycation of beta-lactoglobulin on its recognition by IgE of sera from cow milk allergy patients.

beta-Lactoglobulin (beta-LG) is one of the cow's major milk proteins and the most abundant whey protein. This globular protein of about 18 kDa is folded, forming a beta-barrel (or calyx) structure. This structure is stabilized by two disulfide bonds and can be altered by heating above 65 degrees C. beta-LG is also one of the major allergens in milk. Heating is one of the most common technologic treatments applied during many milk transformations. During heating in the presence of reducing sugars, beta-LG is also submitted to the Maillard reaction, which at the first stage consists of the covalent fixation of sugars on the epsilon-amino groups of lysyl residues. The following steps are condensation and polymerization reactions leading to the formation of melanoidins (brown pigments). Despite the frequency of use of heating during milk transformation, the effects of heat-induced denaturation and of glycation of beta-LG on its recognition by IgE from cow's milk allergy (CMA) patients are not fully understood. The objectives of our work were to evaluate the effect of heat-induced denaturation of bovine beta-LG on binding of IgE from CMA patients and to determine the effect of moderate glycation on the degree of recognition by IgE. We showed that heat-induced denaturation (loss of tertiary and secondary structures) of beta-LG is associated with weaker binding of IgE from CMA patients. It was also shown that moderate glycation of beta-LG in early stages of Maillard reaction has only a small effect on its recognition by IgE, whereas a high degree of glycation has a clear "masking" effect on the recognition of epitopes. This demonstrates the importance of epsilon-amino groups of lysines in the definition of epitopes recognized by IgE.