Application of Magnetic Nanoparticles for Rapid Detection and In Situ Diagnosis in Clinical Oncology

Screening, monitoring, and diagnosis are critical in oncology treatment. However, there are limitations with the current clinical methods, notably the time, cost, and special facilities required for radioisotope-based methods. An alternative approach, which uses magnetic beads, offers faster analyses with safer materials over a wide range of oncological applications. Magnetic beads have been used to detect extracellular vesicles (EVs) in the serum of pancreatic cancer patients with statistically different EV levels in preoperative, postoperative, and negative control samples. By incorporating fluorescence, magnetic beads have been used to quantitatively measure prostate-specific antigen (PSA), a prostate cancer biomarker, which is sensitive enough even at levels found in healthy patients. Immunostaining has also been incorporated with magnetic beads and compared with conventional immunohistochemical methods to detect lesions; the results suggest that immunostained magnetic beads could be used for pathological diagnosis during surgery. Furthermore, magnetic nanoparticles, such as superparamagnetic iron oxide nanoparticles (SPIONs), can detect sentinel lymph nodes in breast cancer in a clinical setting, as well as those in gallbladder cancer in animal models, in a surgery-applicable timeframe. Ultimately, recent research into the applications of magnetic beads in oncology suggests that the screening, monitoring, and diagnosis of cancers could be improved and made more accessible through the adoption of this technology.

Motor Nerve Conduction Block Estimation in Demyelinating Neuropathies by Deconvolution

A deconvolution method is proposed for conduction block (CB) estimation based on two compound muscle action potentials (CMAPs) elicited by stimulating a nerve proximal and distal to the region in which the block is suspected. It estimates the time delay distributions by CMAPs deconvolution, from which CB is computed. The slow afterwave (SAW) is included to describe the motor unit potential, as it gives an important contribution in case of the large temporal dispersion (TD) often found in patients. The method is tested on experimental signals obtained from both healthy subjects and pathological patients, with either Chronic Inflammatory Demyelinating Polyneuropathy (CIDP) or Multifocal Motor Neuropathy (MMN). The new technique outperforms the clinical methods (based on amplitude and area of CMAPs) and a previous state-of-the-art deconvolution approach. It compensates phase cancellations, allowing to discriminate among CB and TD: estimated by the methods of amplitude, area and deconvolution, CB showed a correlation with TD equal to 39.3%, 29.5% and 8.2%, respectively. Moreover, a significant decrease of percentage reconstruction errors of the CMAPs with respect to the previous deconvolution approach is obtained (from a mean/median of 19.1%/16.7% to 11.7%/11.2%). Therefore, the new method is able to discriminate between CB and TD (overcoming the important limitation of clinical approaches) and can approximate patients’ CMAPs better than the previous deconvolution algorithm. Then, it appears to be promising for the diagnosis of demyelinating polyneuropathies, to be further tested in the future in a prospective clinical trial.

Root and Root Canal Morphology: Study Methods and Classifications

Background: Morphology of the root canal system is divergent and unpredictable, and rather linked to clinical complications, which directly affect the treatment outcome. This objective necessitates continuous informative update of the effective clinical and laboratory methods for identifying this anatomy, and classification systems suitable for communication and interpretation in different situations. Data: Only electronic published papers were searched within this review. Sources: “PubMed” website was the only source used to search for data by using the following keywords "root", "canal", "morphology", "classification". Study selection: 153 most relevant papers to the topic were selected, especially the original articles and review papers, from 1970 till the 28th of July 2021. Conclusions: This review divided the root canal analysis methods into two approaches; clinical and in vitro techniques. The latter has shown more precise non-subjective readings, on the other hand; the clinical methods provide direct chair side diagnosis for the clinical cases. The classification systems reviewed in the present study, started with the oldest trials that simply presented the root canal systems, according to the degree of angulation, or by coded Latin numbers or English letters. Then, the most recent systems were also presented that were persisted with continuous editions up to date. These new systems could briefly describe the root and root canal’s internal and external details in a small formulation, without confusion and in an easily communicated manner, highly recommended specially for students, teachers, and researchers

Clinical characteristics and cellular immunity in children with rotavirus infection

Objective. To determine clinical course and state of cellular immunity in young children with rotavirus infection.Children characteristics and research methods. The scientists examined children without infectious pathology and with rotavirus infection (20 patients in each group) using general clinical methods. Rotavirus infection was diagnosed by polymerase chain reaction and immunochromatography. Cellular immunity parameters were determined by flow cytometry.Results. All the children under observation had a moderate form of the disease with symptoms of exsicosis of the II degree. Changes in the immune status were mainly of a regulatory, adaptive nature, which contributed to the favorable course of rotavirus infection in children, however, the dynamics of the number of cells expressing Toll-like receptors indicates the immunosuppressive properties of rotavirus.Conclusion. Currently, rotavirus infection in young children is typical with watery diarrhea as the most pronounced and long-lasting clinical symptom. Shifts in immunogram indices in general indicate a deficiency of the cellular link of immunity and a violation of its regulation with simultaneous activation of the immune system in an effort to achieve the eradication of the rotavirus with immunosuppressive properties.

Expediting Finite Element Analyses for Subject-Specific Studies of Knee Osteoarthritis: A Literature Review

Osteoarthritis (OA) is a degenerative disease that affects the synovial joints, especially the knee joint, diminishing the ability of patients to perform daily physical activities. Unfortunately, there is no cure for this nearly irreversible musculoskeletal disorder. Nowadays, many researchers aim for in silico-based methods to simulate personalized risks for the onset and progression of OA and evaluate the effects of different conservative preventative actions. Finite element analysis (FEA) has been considered a promising method to be developed for knee OA management. The FEA pipeline consists of three well-established phases: pre-processing, processing, and post-processing. Currently, these phases are time-consuming, making the FEA workflow cumbersome for the clinical environment. Hence, in this narrative review, we overviewed present-day trends towards clinical methods for subject-specific knee OA studies utilizing FEA. We reviewed studies focused on understanding mechanisms that initiate knee OA and expediting the FEA workflow applied to the whole-organ level. Based on the current trends we observed, we believe that forthcoming knee FEAs will provide nearly real-time predictions for the personalized risk of developing knee OA. These analyses will integrate subject-specific geometries, loading conditions, and estimations of local tissue mechanical properties. This will be achieved by combining state-of-the-art FEA workflows with automated approaches aided by machine learning techniques.

BurnoutWords - Detecting Burnout for a Clinical Setting

Burnout, a syndrome conceptualized as resulting from major workplace stress that has not been successfully managed, is a major problem of today's society, in particular in crisis times such as a global pandemic situation. Burnout detection is hard, because the symptoms often overlap with other diseases and syndromes. Typical clinical approaches are using inventories to assess burnout for their patients, even though free-text approaches are considered promising. In research of natural language processing (NLP) applied to mental health, often data from social media is used and not real patient data, which leads to some limitations for the application in clinical use cases. In this paper, we fill the gap and provide a dataset using extracts from interviews with burnout patients containing 216 records. We train a support vector machine (SVM) classifier to detect burnout in text snippets with an accuracy of around 80%, which is clearly higher than the random baseline of our setup. This provides the foundation for a next generation of clinical methods based on NLP.

Flexor tendon repair: recent changes and current methods

The current clinical methods of flexor tendon repair are remarkably different from those used 20 years ago. This article starts with a review of the current methods, followed by presentation of past experience and current status of six eminent hand surgery units from four continents/regions. Many units are using, or are moving toward using, the recent strong (multi-strand) core suture method together with a simpler peripheral suture. Venting of the critical pulleys over less than 2 cm length is safe and favours functional recovery. These repair and recent motion protocols lead to remarkably more reliable repairs, with over 80% good or excellent outcomes achieved rather consistently after Zone 2 repair along with infrequent need of tenolysis. Despite slight variations in repair methods, they all consider general principles and should be followed. Outcomes of Zone 2 repairs are not dissimilar to those in other zones with very low to zero incidence of rupture.

Measuring accuracy of Dataset using Deep Learning Algorithm RMSProp Algorithm

Over the last few years, there have been many significant improvements in the field of AI, machine learning, deep learning are being used in various industries and research. In order to train the deep learning models learning of parameters plays a major role, here the reduction of loss incurred during the training process is the main objective. In a supervised mode of learning, a model is given the data samples and their respective outcomes. When a model generates an output, it compares it with the desired output and then takes the difference of generated and desired outputs and then attempts to bring the generated output close to the desired output. This is achieved through optimization algorithms. Though many kinds of clinical methods have been employed to detect whether patients have heart disease or not by number of features from patients. but it’s still a challenging task due to the multitude of data elements involved. The motive of our project is to save human resources in medical centers and improve accuracy of diagnosis. In our project we used an RMS prop optimizer. The purpose is to decide how many hidden layers need to be selected and how many neurons need to be selected in each and every hidden layer by looking at the dataset and to give the application of deep learning to the health care sector so that we can minimize the costs of treatment and help in proactive actions. We want to show that we can increase the accuracy of the project by taking stability along with accuracy into consideration. Index Terms: RMS Prop, Machine Learning, Deep Learning, number of features, proactive actions

The reaction of spermaturia in male marsh frogs in the diagnosis of miscarriage

Currently, there are still no perfect clinical methods for early diagnosis of miscarriage, as a result of which the final diagnosis is usually made only after long-term observation of patients. The disappearance of reliable signs of pregnancy does not always solve the problem. The difficulty of diagnosis lies in differentiating a miscarriage from a normal pregnancy, from a threatening and incipient miscarriage.