Spectrum of soft tissue lesions in upper and lower extremities on fine needle aspiration cytology: A three years’ experience from Western Indian population

Soft tissue lesions have a wide spectrum which includes non- neoplastic, benign & malignant lesions. FNAC act as preliminary diagnostic tool providing a predictive diagnosis of a benign or malignant soft tissue neoplasm and thus helps for further intervention. This study discusses the spectrum of FNAC of soft tissue lesions in upper and lower limbs.All cytology smears of soft tissue lesions from both upper and lower limbs were included over a period of three years at tertiary care hospital. The most common age group was 31 to 40 years with male to female ratio being 1.3:1. The spectrum included broadly neoplastic (65.7%) & non-neoplastic (34.3%) cases. The neoplasms were further divided as benign (42.8 %), malignant (18.6%) and suspicious for neoplasm (4.3%) whereas (34.3 %) were non-neoplastic lesions. The most common lesion was giant cell tumor (GCT) of tendon sheath. The most common site was hand (24%) followed by feet (22.5%). FNAC of soft tissue lesions is useful for differentiating various lesions and neoplasms in extremities so as to help patients in further management.

Predicting Parkinson’s: Using Neural Networks to Evaluate the Genetic Risk Factors Behind the Disease

To date, researchers do not know the exact reasons for the loss of dopaminergic neurons in the substantia nigra pars compacta that leads to Parkinson’s Disease (PD). Thus, it is extremely difficult to predict whether or not a patient is likely to develop the disease later on, as their risk increases with age. However, once patients present with the common symptoms indicative of the illness, a substantial amount of dopaminergic neurons are already lost. Seeing as there are no current avenues of replacing those neurons, predictive diagnosis and preventive measures could be of extraordinary help in devising treatments. Our aim was to use the significant research into possible high-risk genetic factors from genome-wide association studies (GWAS) to formulate a predictive neural network model for Parkinson’s. We analyzed patient genomes for mutations in the top 20 genes associated with PD, as well as 21 genes implicated in axon guidance pathways, to determine whether the patients were at high or low risk for Parkinson’s. Our model produced an accuracy and AUROC of 94%. We found this significant because it showed a strong correlation between the single nucleotide polymorphisms (SNPs) we analyzed and PD. We believe our model can be further improved upon by adding considerations for other investigated risk factors, such as patient age, familial history of disease, or gut microbiota inconsistencies among others.

Gal-3BP Levels in Hospitalized Patients Correlate With COVID-19

The ongoing Covid-19 pandemic disease is still lacking effective treatments and relying on a predictive diagnosis for early individuation of patients which will progress to a severe disease can be crucial. To this aim, the search and the identification of new molecular targets of inflammation and disease progression to be used as predictive biomarker of disease severity is important.In this work Gal-3BP was explored as a potential biomarker for COVID-19 severity. We found highly increased circulating levels of Gal-3BP in COVID-19 patients compared to healthy controls. Furthermore, the serum levels of Gal-3BP were higher in those “non severe” patients which progressed to a “severe” disease and correlated with levels of IL-6, a known marker of disease progression in COVID-19 patients. These results suggest that Gal-3BP could be a predictor of Covid-19 severity in early infected patients contributing to extend the panel of the other already known biomarkers associated to Covid-19 severity and progression.

Sudden infant death syndrome: Melatonin, serotonin, and CD34 factor as possible diagnostic markers and prophylactic targets

Sudden infant death syndrome (SIDS) is one of the primary causes of death of infants in the first year of life. According to the WHO’s data, the global infant mortality rate is 0.64–2 per 1,000 live-born children. Molecular and cellular aspects of SIDS development have not been identified so far. The purpose of this paper is to verify and analyze the expression of melatonin 1 and 2 receptors, serotonin (as a melatonin precursor), and CD34 molecules (as hematopoietic and endothelial markers of cardiovascular damage) in the medulla, heart, and aorta in infants who died from SIDS. An immunohistochemical method was used to investigate samples of medulla, heart, and aorta tissues of infants 3 to 9 months of age who died from SIDS. The control group included children who died from accidents. It has been shown that the expression of melatonin receptors as well as serotonin and CD34 angiogenesis markers in tissues of the medulla, heart, and aorta of infants who died from SIDS is statistically lower as compared with their expression in the same tissues in children who died from accidents. The obtained data help to clarify in detail the role of melatonin and such signaling molecules as serotonin and CD34 in SIDS pathogenesis, which can open new prospects for devising novel methods for predictive diagnosis of development and targeted prophylaxis of SIDS.

Tear Liquid for Predictive Diagnosis of Alzheimer’s Disease

The common approach of the diagnosis of Alzheimer’s Disease (AD) is made with an analysis of the cerebrospinal fluid or the study of retinal fundus and the plaques formation through optical corneal tomography (OCT), or more simply with a fundus camera. Tears analysis is widely discussed in literature as an essential method to describe molecular and biochemical alterations in different diseases. The aim of our study was the identification with immunocytochemistry of Amyloid Beta-42 in tears from patients with or without familiarity for Alzheimer Disease, in order to make the diagnosis earlier and more accessible compared to other invasive methods. Our study was performed on tears from three phenotypically healthy subjects: two of them were Caucasian with Alzheimer familiarity (48 and 55 years old) and the other one was Asian without Alzheimer familiarity (45 years old) and affected by an adenoviral keratoconjunctivitis at the moment of withdrawal. Tear samples were collected from eye fornix and were examinated by immunocytochemistry (ICC) assay using anti-Amyloid Beta X-42 antibody. Two out of three tears samples showed positive Amyloid Beta-42. Considering that our patients were phenotypically healthy, the identification of Amyloid Beta-42 by ICC could be a candidable method to make the diagnosis of the disease earlier and more accessible and available then other current and invasive methods and it could be a candidate for a screening method too.

Enhancing the weighted voting ensemble algorithm for tuberculosis predictive diagnosis

Tuberculosis has the most considerable death rate among diseases caused by a single micro-organism type. The disease is a significant issue for most third-world countries due to poor diagnosis and treatment potentials. Early diagnosis of tuberculosis is the most effective way of managing the disease in patients to reduce the mortality rate of the infection. Despite several methods that exist in diagnosing tuberculosis, the limitations ranging from the cost in carrying out the test to the time taken to obtain the results have hindered early diagnosis of the disease. This work aims to develop a predictive model that would help in the diagnosis of TB using an extended weighted voting ensemble method. The method used to carry out this research involved analyzing tuberculosis gene expression data obtained from GEO (Transcript Expression Omnibus) database and developing a classification model to aid tuberculosis diagnosis. A classifier combination of Naïve Bayes (NB), and Support Vector Machine (SVM) was used to develop the classification model. The weighted voting ensemble technique was used to improve the classification model's performance by combining the classification results of the single classifier and selecting the group with the highest vote based on the weights given to the single classifiers. Experimental analysis indicates a performance accuracy of the enhanced ensemble classifier as 0.95, which showed a better performance than the single classifiers, which had 0.92, and 0.87 obtained from SVM and NB, respectively. The developed model can also assist health practitioners in the timely diagnosis of tuberculosis, which would reduce the mortality rate caused by the disease, especially in developing countries.

Diagnostic Value And Safety of Medical Thoracoscopy In The Pleural Effusion of Different Causes

Background: Pleural effusions are caused by various reasons, whose diagnosis remains challenging in spite of various means of diagnosis. Medical thoracoscopy, greatly improves the diagnostic efficacy and gets preference for managements, especially undiagnosed pleural effusions. This study aimed to assess the diagnostic efficacy and safety of medical thoracoscopy in patients with pleural effusion of different causes. Methods: Between January 1st 2012 and April 30th 2021, patients with pleural effusion underwent medical thoracoscopy in the Department of Respiratory Medicine, the Second Affiliated Hospital of Xi'an Jiaotong University. According to the discharge diagnosis, patients were grouped into three, including malignant, tuberculous and inflammatory group. General information, tuberculosis-related and effusion-related indices of three groups were analyzed. The diagnostic yield, diagnostic accuracy, performance under thoracoscopy and complications of patients were compared in three groups. Then, the significant factors for predictive diagnosis between the malignant and tuberculous group were analyzed. Results: During this 10-year study, 106 patients were included, with 67 males and 39 females, mean age 57.1±14.184 years. In 74 patients confirmed under thoracoscopy, 41 patients (38.7%) were malignant, 21 patients (19.8%) tuberculous and 32 patients (30.2%) undiagnostic. The diagnostic yield of medical thoracoscopy is 69.8%, and 75.9% in the malignant, 48.8% in the tuberculous, and 75.0% in the inflammatory. The diagnostic accuracies are 100%, 87.5%, and 75.0%, respectively. Under thoracoscopy, we observed single or multiple pleural nodules in 81.1%, pleural adhesions in 34.0% of patients with pleural effusions. The most common complication was chest pain (41.5%), following by chest tightness (11.3%), fever (10.4%). Multivariate logistic regression analysis showed that effusion appearance (OR=0.001, 95%CI: 0.000-0.204, P=0.010), CEA (OR=0.243, 95%CI: 0.081-0.728, P=0.011) were significant in the differentiation of malignant and tuberculous pleural effusion. Conclusion: Medical thoracoscopy is an effective, safe, less invasive procedure with high diagnostic yield for the pleural effusion of different causes. Medical thoracoscopy has a promising prospect.

Patient and Graph Embeddings for Predictive Diagnosis of Drug Iatrogenesis

In the context of the IA.TROMED project we intend to develop and evaluate original algorithmic methods that will rely on semantic enrichment of embeddings by combining new deep learning algorithms, such as models founded on transformers, and symbolic artificial intelligence. The documents’ embeddings, the graphs’ embeddings of biomedical concepts, and patients’ embeddings, all of them semantically enriched with aligned formal ontologies and semantic networks, will constitute a layer that will play the role of a queryable and searchable knowledge base that will supply the IA.TROMED’s clinical, predictive, and iatrogenic diagnosis support module.