Search results for: diagnosis based on EEG

Authors: E. Irankhah, M. Zarif, E. Mazrooei Rad, K. Ghandehari

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Alzheimer's prevalence is on the rise, and the disease comes with problems like cessation of treatment, high cost of treatment, and the lack of early detection methods. The pathology of this disease causes the formation of protein deposits in the brain of patients called plaque amyloid. Generally, the diagnosis of this disease is done by performing tests such as a cerebrospinal fluid, CT scan, MRI, and spinal cord fluid testing, or mental testing tests and eye tracing tests. In this paper, we tried to use the Medial Temporal Atrophy (MTA) method and the Leave One Out (LOO) cycle to extract the statistical properties of the three Fz, Pz, and Cz channels of ERP signals for early diagnosis of this disease. In the process of CT scan images, the accuracy of the results is 81% for the healthy person and 88% for the severe patient. After the process of ERP signaling, the accuracy of the results for a healthy person in the delta band in the Cz channel is 81% and in the alpha band the Pz channel is 90%. In the results obtained from the signal processing, the results of the severe patient in the delta band of the Cz channel were 89% and in the alpha band Pz channel 92%.

Keywords: Alzheimer's disease, image and signal processing, LOO cycle, medial temporal atrophy

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Authors: Monireh Ahmadi Bani, Adel Khorramrouz, Lalenoor Morvarid, Bagheri Mahtab

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Background:- Foot disorders are common in musculoskeletal problems. Plantar pressure distribution measurement is one the most important part of foot disorders diagnosis for quantitative analysis. However, the association of plantar pressure and foot disorders is not clear. With the growth of dataset and machine learning methods, the relationship between foot disorders and plantar pressures can be detected. Significance of the study:- The purpose of this study was to predict the probability of common foot disorders based on peak plantar pressure distribution and center of pressure during walking. Methodologies:- 2323 participants were assessed in a foot therapy clinic between 2015 and 2021. Foot disorders were diagnosed by an experienced physician and then they were asked to walk on a force plate scanner. After the data preprocessing, due to the difference in walking time and foot size, we normalized the samples based on time and foot size. Some of force plate variables were selected as input to a deep neural network (DNN), and the probability of any each foot disorder was measured. In next step, we used support vector machine (SVM) and run dataset for each foot disorder (classification of yes or no). We compared DNN and SVM for foot disorders prediction based on plantar pressure distributions and center of pressure. Findings:- The results demonstrated that the accuracy of deep learning architecture is sufficient for most clinical and research applications in the study population. In addition, the SVM approach has more accuracy for predictions, enabling applications for foot disorders diagnosis. The detection accuracy was 71% by the deep learning algorithm and 78% by the SVM algorithm. Moreover, when we worked with peak plantar pressure distribution, it was more accurate than center of pressure dataset. Conclusion:- Both algorithms- deep learning and SVM will help therapist and patients to improve the data pool and enhance foot disorders prediction with less expense and error after removing some restrictions properly.

Keywords: deep neural network, foot disorder, plantar pressure, support vector machine

Procedia PDF Downloads 316

Authors: Donya Ashtiani Haghighi, Amirali Baniasadi

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Coronavirus (COVID-19) disease has spread abruptly all over the world since the end of 2019. Computed tomography (CT) scans and X-ray images are used to detect this disease. Different Deep Neural Network (DNN)-based diagnosis solutions have been developed, mainly based on Convolutional Neural Networks (CNNs), to accelerate the identification of COVID-19 cases. However, CNNs lose important information in intermediate layers and require large datasets. In this paper, Capsule Network (CapsNet) is used. Capsule Network performs better than CNNs for small datasets. Accuracy of 0.9885, f1-score of 0.9883, precision of 0.9859, recall of 0.9908, and Area Under the Curve (AUC) of 0.9948 are achieved on the Capsule-based framework with hyperparameter tuning. Moreover, different dropout rates are investigated to decrease overfitting. Accordingly, a dropout rate of 0.1 shows the best results. Finally, we remove one convolution layer and decrease the number of trainable parameters to 146,752, which is a promising result.

Keywords: capsule network, dropout, hyperparameter tuning, classification

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Authors: V. Kuzhandai Velu, R. Ramesh

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Background and objectives: Acute myocardial infarction (AMI) is the most common cause of mortality and morbidity. Over the last decade, microRNAs (miRs) have emerged as a potential marker for detecting AMI. The current study evaluates the kinetics and importance of miRs in the differential diagnosis of ST-segment elevated MI (STEMI) and non-STEMI (NSTEMI) and its correlation to conventional biomarkers and to predict the immediate outcome of AMI for arrhythmias and left ventricular (LV) dysfunction. Materials and Method: A total of 100 AMI patients were recruited for the study. Routine cardiac biomarker and miRNA levels were measured during diagnosis and serially at admission, 6, 12, 24, and 72hrs. The baseline biochemical parameters were analyzed. The expression of miRs was compared between STEMI and NSTEMI at different time intervals. Diagnostic utility of miR-1, miR-133, miR-208, and miR-499 levels were analyzed by using RT-PCR and with various diagnostics statistical tools like ROC, odds ratio, and likelihood ratio. Results: The miR-1, miR-133, and miR-499 showed peak concentration at 6 hours, whereas miR-208 showed high significant differences at all time intervals. miR-133 demonstrated the maximum area under the curve at different time intervals in the differential diagnosis of STEMI and NSTEMI which was followed by miR-499 and miR-208. Evaluation of miRs for predicting arrhythmia and LV dysfunction using admission sample demonstrated that miR-1 (OR = 8.64; LR = 1.76) and miR-208 (OR = 26.25; LR = 5.96) showed maximum odds ratio and likelihood respectively. Conclusion: Circulating miRNA showed a highly significant difference between STEMI and NSTEMI in AMI patients. The peak was much earlier than the conventional biomarkers. miR-133, miR-208, and miR-499 can be used in the differential diagnosis of STEMI and NSTEMI, whereas miR-1 and miR-208 could be used in the prediction of arrhythmia and LV dysfunction, respectively.

Keywords: myocardial infarction, cardiac biomarkers, microRNA, arrhythmia, left ventricular dysfunction

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Authors: Mohamed El Morsy, Gabriela Achtenová

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Condition monitoring is used to increase machinery availability and machinery performance, whilst reducing consequential damage, increasing machine life, reducing spare parts inventories, and reducing breakdown maintenance. An efficient condition monitoring system provides early warning of faults by predicting them at an early stage. When a localized fault occurs in gears, the vibration signals always exhibit non-stationary behavior. The periodic impulsive feature of the vibration signal appears in the time domain and the corresponding gear mesh frequency (GMF) emerges in the frequency domain. However, one limitation of frequency-domain analysis is its inability to handle non-stationary waveform signals, which are very common when machinery faults occur. Particularly at the early stage of gear failure, the GMF contains very little energy and is often overwhelmed by noise and higher-level macro-structural vibrations. An effective signal processing method would be necessary to remove such corrupting noise and interference. In this paper, a new hybrid method based on optimal Morlet wavelet filter and autocorrelation enhancement is presented. First, to eliminate the frequency associated with interferential vibrations, the vibration signal is filtered with a band-pass filter determined by a Morlet wavelet whose parameters are selected or optimized based on maximum Kurtosis. Then, to further reduce the residual in-band noise and highlight the periodic impulsive feature, an autocorrelation enhancement algorithm is applied to the filtered signal. The test stand is equipped with three dynamometers; the input dynamometer serves as the internal combustion engine, the output dynamometers induce a load on the output joint shaft flanges. The pitting defect is manufactured on the tooth side of a gear of the fifth speed on the secondary shaft. The gearbox used for experimental measurements is of the type most commonly used in modern small to mid-sized passenger cars with transversely mounted powertrain and front wheel drive: a five-speed gearbox with final drive gear and front wheel differential. The results obtained from practical experiments prove that the proposed method is very effective for gear fault diagnosis.

Keywords: wavelet analysis, pitted gear, autocorrelation, gear fault diagnosis

Procedia PDF Downloads 361

Authors: Ioannis Panagiotopoulos, Aristidis Anastasakis, Konstantinos Toutouzas, Ioannis Iakovou, Charalampos Vlachopoulos, Vasilis Voudris, Georgios Tziomalos, Konstantinos Tsioufis, Efstathios Kastritis, Alexandros Briassoulis, Kimon Stamatelopoulos, Alexios Antonopoulos, Paraskevi Exadaktylou, Evanthia Giannoula, Anastasia Katinioti, Maria Kalantzi, Evangelos Leontiadis, Eftychia Smparouni, Ioannis Malakos, Nikolaos Aravanis, Argyrios Doumas, Maria Koutelou

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Introduction: Cardiac amyloidosis from Transthyretin (ATTR-CA) is an infiltrative disease characterized by the deposition of pathological transthyretin complexes in the myocardium. This study describes the characteristics of patients diagnosed with ATTR-CA from 2019 until present at the Nuclear Medicine Department of Onassis Cardiac Surgery Center and AHEPA Hospital. These centers have extensive experience in amyloidosis and modern technological equipment for its diagnosis. Materials and Methods: Records of consecutive patients (N=73) diagnosed with any type of amyloidosis were collected, analyzed, and prospectively followed. The diagnosis of amyloidosis was made using specific myocardial scintigraphy with Tc-99m DPD. Demographic characteristics, including age, gender, marital status, height, and weight, were collected in a database. Clinical characteristics, such as amyloidosis type (ATTR and AL), serum biomarkers (BNP, troponin), electrocardiographic findings, ultrasound findings, NYHA class, aortic valve replacement, device implants, and medication history, were also collected. Some of the most significant results are presented. Results: A total of 73 cases (86% male) were diagnosed with amyloidosis over four years. The mean age at diagnosis was 82 years, and the main symptom was dyspnea. Most patients suffered from ATTR-CA (65 vs. 8 with AL). Out of all the ATTR-CA patients, 61 were diagnosed with wild-type and 2 with two rare mutations. Twenty-eight patients had systemic amyloidosis with extracardiac involvement, and 32 patients had a history of bilateral carpal tunnel syndrome. Four patients had already developed polyneuropathy, and the diagnosis was confirmed by DPD scintigraphy, which is known for its high sensitivity. Among patients with isolated cardiac involvement, only 6 had left ventricular ejection fraction below 40%. The majority of ATTR patients underwent tafamidis treatment immediately after diagnosis. Conclusion: In conclusion, the experiences shared by the two centers and the continuous exchange of information provide valuable insights into the diagnosis and management of cardiac amyloidosis. Clinical suspicion of amyloidosis and early diagnostic approach are crucial, given the availability of non-invasive techniques. Cardiac scintigraphy with DPD can confirm the presence of the disease without the need for a biopsy. The ultimate goal still remains continuous education and awareness of clinical cardiologists so that this systemic and treatable disease can be diagnosed and certified promptly and treatment can begin as soon as possible.

Keywords: amyloidosis, diagnosis, myocardial scintigraphy, Tc-99m DPD, transthyretin

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Authors: Madeleine Cox

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Objective: discussion of diagnosis of vernix caseosa peritonitis, recovery and subsequent caesarean seciton Case: 30 year old G4P1 presented in labour at 40 weeks, planning a vaginal birth afterprevious caesarean section. She underwent an emergency caesarean section due to concerns for fetal wellbeing on CTG. She was found to have a thin lower segment with a very small area of dehiscence centrally. The operation was uncomplicated, and she recovered and went home 2 days later. She then represented to the emergency department day 6 post partum feeling very unwell, with significant abdominal pain, tachycardia as well as urinary retention. Raised white cell count of 13.7 with neutrophils of 11.64, CRP of 153. An abdominal ultrasound was poorly tolerated by the patient and did not aide in the diagnosis. Chest and abdominal xray were normal. She underwent a CT chest and abdomen, which found a small volume of free fluid with no apparent collection. Given no obvious cause of her symptoms were found and the patient did not improve, she had a repeat CT 2 days later, which showed progression of free fluid. A diagnostic laparoscopy was performed with general surgeons, which reveled turbid fluid, an inflamed appendix which was removed. The patient improved remarkably post operatively. The histology showed periappendicitis with acute appendicitis with marked serosal inflammatory reaction to vernix caseosa. Following this, the patient went on to recover well. 4 years later, the patient was booked for an elective caesarean section, on entry into the abdomen, there were very minimal adhesions, and the surgery and her subsequent recovery was uncomplicated. Discussion: this case represents the diagnostic dilemma of a patient who presents unwell without a clear cause. In this circumstance, multiple modes of imaging did not aide in her diagnosis, and so she underwent diagnostic surgery. It is important to evaluate if a patient is or is not responding to the typical causes of post operative pain and adjust management accordingly. A multiteam approach can help to provide a diagnosis for these patients. Conclusion: Vernix caseosa peritonitis is a rare cause of acute abdomen post partum. There are few reports in the literature of the initial presentation and no reports on the possible effects on future pregnancies. This patient did not have any complications in her following pregnancy or delivery secondary to her diagnosis of vernix caseosa peritonitis. This may assist in counselling other women who have had this uncommon diagnosis.

Keywords: peritonitis, obstetrics, caesarean section, pain

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Authors: Abbas Pourreza

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MicroRNAs (miRNAs) are small single-stranded non-coding RNAs. They are almost 18-25 nucleotides long and very conservative through evolution. They are involved in adjusting the expression of numerous genes due to the existence of a complementary region, generally in the 3' untranslated regions (UTR) of target genes, against particular mRNAs in the cell. Also, miRNAs have been proven to be involved in cell development, differentiation, proliferation, and apoptosis. More than 2000 miRNAs have been recognized in human cells, and these miRNAs adjust approximately one-third of all genes in human cells. Dysregulation of miRNA originated from abnormal DNA methylation patterns of the locus, cause to down-regulated or overexpression of miRNAs, and it may affect tumor formation or development of it. Breast cancer (BC) is the most commonly identified cancer, the most prevalent cancer (23%), and the second-leading (14%) mortality in all types of cancer in females. BC can be classified based on the status (+/−) of the hormone receptors, including estrogen receptor (ER), progesterone receptor (PR), and the Receptor tyrosine-protein kinase erbB-2 (ERBB2 or HER2). Currently, there are four main molecular subtypes of BC: luminal A, approximately 50–60 % of BCs; luminal B, 10–20 %; HER2 positive, 15–20 %, and 10–20 % considered Basal (triple-negative breast cancer (TNBC)) subtype. Aberrant expression of miR-145, miR-21, miR-10b, miR-125a, and miR-206 was detected by Stem-loop real-time RT-PCR in BC cases. Breast tumor formation and development may result from down-regulation of a tumor suppressor miRNA such as miR-145, miR-125a, and miR-206 and/or overexpression of an oncogenic miRNA such as miR-21 and miR-10b. MiR-125a, miR-206, miR-145, miR-21, and miR-10b are hugely predicted to be new tumor markers for the diagnosis and prognosis of BC. MiR-21 and miR-125a could play a part in the treatment of HER-2-positive breast cancer cells, while miR-145 and miR-206 could speed up the evolution of cure techniques for TNBC. To conclude, miRNAs will be presented as hopeful molecules to be used in the primary diagnosis, prognosis, and treatment of BC and battle as opposed to its developed drug resistance.

Keywords: breast cancer, HER2 positive, miRNA, TNBC

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Authors: Rihab Gamaoun, Cynthia El Hage, Laura Ruiz, Erin Prosk, Antonio Vigano

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Introduction: Santé Cannabis (SC), a Canadian medical cannabis-specialized group of clinics based in Montreal and in the province of Québec, has served more than 5000 patients seeking cannabis-based treatment prescription for medical indications over the past five years. Within a research frame, data on the use of medical cannabis products from all the above patients were prospectively collected, leading to a large real-world database on the use of medical cannabis. The aim of this study was to gather information on the profiles of both patients and prescribed medical cannabis products at SC clinics and to assess the safety of medical cannabis among Canadian patients. Methods: Using a retrospective analysis of the database, records of 2585 patients who were prescribed medical cannabis products for therapeutic purposes between 01-November 2017 and 04-September 2019 were included. Patients’ demographics, primary diagnosis, route of administration, and chemovars recorded at the initial visits were investigated. Results: At baseline: 9% of SC patients were female, with a mean age of 57 (SD= 15.8, range= [18-96]); Cannabis products were prescribed mainly for patients with a diagnosis of chronic pain (65.9% of patients), cancer (9.4%), neurological disorders (6.5%), mood disorders (5.8 %) and inflammatory diseases (4.1%). Route of administration and chemovars of prescribed cannabis products were the following: 96% of patients received cannabis oil (51% CBD rich, 42.5% CBD:THC); 32.1% dried cannabis (21.3% CBD:THC, 7.4% THC rich, 3.4 CBD rich), and 2.1% oral spray cannabis (1.1% CBD:THC, 0.8% CBD rich, 0.2% THC rich). Most patients were prescribed simultaneously, a combination of products with different administration routes and chemovars. Safety analysis is undergoing. Conclusion: Our results provided initial information on the profile of medical cannabis products prescribed in a Canadian population and the experienced adverse events over the past three years. The Santé Cannabis database represents a unique opportunity for comparing clinical practices in prescribing and titrating cannabis-based medications across different centers. Ultimately real-world data, including information about safety and effectiveness, will help to create standardized and validated guidelines for choosing dose, route of administration, and chemovars types for the cannabis-based medication in different diseases and indications.

Keywords: medical cannabis, real-world data, safety, pharmacovigilance

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Authors: Elad Vashdi

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It is common to find speech deficit among children diagnosed with Autism. It can be found in the clinical field and recently in research. One of the DSM-V criteria suggests a speech delay (Delay in, or total lack of, the development of spoken language), but doesn't explain the cause of it. A common perception among professionals and families is that the inability to talk results from the autism. Autism is a name for a syndrome which just describes a phenomenon and is defined behaviorally. Since it is not based yet on a physiological gold standard, one can not conclude the nature of a deficit based on the name of the syndrome. A wide retrospective research (n=270) which included children with motor speech difficulties was conducted in Israel. The study analyzed entry evaluations in a private clinic during the years 2006-2013. The data was extracted from the reports. High percentage of children diagnosed with Autism (60%) was found. This result demonstrates the high relationship between Autism and motor speech problem. It also supports recent findings in research of Childhood apraxia of speech (CAS) occurrence among children with ASD. Only small percentage of the participants in this research (10%) were diagnosed with CAS even though their verbal deficits well fitted the guidelines for CAS diagnosis set by ASHA in 2007. This fact raises questions regarding the diagnostic procedure in Israel. The understanding that CAS might highly exist within Autism and can have a remarkable influence on the course of early development should be a guiding tool within the diagnosis procedure. CAS can explain the nature of the speech problem among some of the autistic children and guide the treatment in a more accurate way. Calculating the prevalence of CAS which includes the comorbidity with ASD reveals new numbers and suggests treating differently the CAS population.

Keywords: childhood apraxia of speech, Autism, treatment, speech

Procedia PDF Downloads 249

Authors: Ibrahim Aly, Manal Ahmed, Mahmoud M. El-Shall

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Filariasis is a parasitic disease caused by small roundworms. The filarial worms are transmitted and spread by blood-feeding black flies and mosquitoes. Lymphatic filariasis (Elephantiasis) is caused by Wuchereriabancrofti, Brugiamalayi, and Brugiatimori. Elimination of Lymphatic filariasis necessitates an increasing demand for valid, reliable, and rapid diagnostic kits. Nanodiagnostics involve the use of nanotechnology in clinical diagnosis to meet the demands for increased sensitivity, specificity, and early detection in less time. The aim of this study was to evaluate the nano-based enzymelinked immunosorbent assay (ELISA) and novel nano-chip card as a rapid test for detection of filarial antigen in serum samples of human filariasis in comparison with traditional -ELISA. Serum samples were collected from an infected human with filarial gathered across Egypt's governorates. After receiving informed consenta total of 45 blood samples of infected individuals residing in different villages in Gharbea governorate, which isa nonendemic region for bancroftianfilariasis, healthy persons living in nonendemic locations (20 persons), as well as sera from 20 other parasites, affected patients were collected. The microfilaria was checked in thick smears of 20 µl night blood samples collected during 20-22 hrs. All of these individuals underwent the following procedures: history taking, clinical examination, and laboratory investigations, which included examination of blood samples for microfilaria using thick blood film and serological tests for detection of the circulating filarial antigen using polyclonal antibody- ELISA, nano-based ELISA, and nano-chip card. In the present study, a recently reported polyoclonal antibody specific to tegumental filarial antigen was used in developing nano-chip card and nano-ELISA compared to traditional ELISA for the detection of circulating filarial antigen in sera of patients with bancroftianfilariasis. The performance of the ELISA was evaluated using 45 serum samples. The ELISA was positive with sera from microfilaremicbancroftianfilariasis patients (n = 36) with a sensitivity of 80 %. Circulating filarial antigen was detected in 39/45 patients who were positive for circulating filarial antigen using nano-ELISA with a sensitivity of 86.6 %. On the other hand, 42 out of 45 patients were positive for circulating filarial antigen using nano-chip card with a sensitivity of 93.3%.In conclusion, using a novel nano-chip assay could potentially be a promising alternative antigen detection test for bancroftianfilariasis.

Keywords: lymphatic filariasis, nanotechnology, rapid diagnosis, elisa technique

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Authors: Sanjeewa Seneviratne, Ian Campbell, Nina Scott, Ross Lawrenson

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Introduction: Indigenous Māori women experience a 60% higher breast cancer mortality rate compared with European women in New Zealand. We explored the impact of difference in the rate of screen detected breast cancer between Māori and European women on more advanced disease at diagnosis and lower survival in Māori women. Methods: All primary in-situ and invasive breast cancers diagnosed in screening age women (as defined by the New Zealand National Breast Cancer Screening Programme) between 1999 and 2012 in the Waikato area were identified from the Waikato Breast Cancer Register and the national screening database. Association between screen versus non-screen detection and cancer stage at diagnosis and survival were compared by ethnicity and socioeconomic deprivation. Results: Māori women had 50% higher odds of being diagnosed with more advance staged cancer compared with NZ European women, a half of which was explained by the lower rate of screen detected cancer in Māori women. Significantly lower breast cancer survival rates were observed for Māori compared with NZ European and most deprived compared with most affluent socioeconomic groups for symptomatically detected breast cancer. No significant survival differences by ethnicity or socioeconomic deprivation were observed for screen detected breast cancer. Conclusions: Low rate of screen detected breast cancer appears to be a major contributor for more advanced stage disease at diagnosis and lower breast cancer survival in Māori compared with NZ European women. Increasing screening participation for Māori has the potential to substantially reduce breast cancer mortality inequity between Māori and NZ European women.

Keywords: breast cancer, screening, ethnicity, inequity

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Authors: Mbonigaba Swale

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Malaria is an infection or disease caused by parasites (Plasmodium Falciparum — causes severe Malaria, plasmodium Vivax, Plasmodium Ovale, and Plasmodium Malariae), transmitted by bites of infected anopheles (female) mosquitoes to humans. These vectors comprise of two types in Africa, particularly in Uganda, i.e. anopheles fenestus and Anopheles gambaie (‘example Anopheles arabiensis,,); feeds on man inside the house mainly at dusk, mid-night and dawn and rests indoors and makes them effective transmitters (vectors) of the disease. People in both urban and rural areas have consistently become prone to repetitive attacks of malaria, causing a lot of deaths and significantly increasing the poverty levels of the rural poor. Malaria is a national problem; it causes a lot of maternal pre-natal and antenatal disorders, anemia in pregnant mothers, low birth weights for the newly born, convulsions and epilepsy among the infants. Cumulatively, it kills about one million children every year in sub-Saharan Africa. It has been estimated to account for 25-35% of all outpatient visits, 20-45% of acute hospital admissions and 15-35% of hospital deaths. Uganda is the leading victim country, for which Rakai and Masaka districts are the most affected. So, it is not clear whether these abhorrent situations and episodes of recurrences and failure to cure from the disease are a result of poor diagnosis, prescription and dosing, treatment habits and compliance of the patients to the drugs or the ethical domain of the stake holders in relation to the main stream methodology of malaria management. The research is aimed at offering an alternative approach to manage and deal absolutely with problem by using a knowledge based software model of Artificial Intelligence (Al) that is capable of performing common-sense and cognitive reasoning so as to take decisions like the human brain would do to provide instantaneous expert solutions so as to avoid speculative simulation of the problem during differential diagnosis in the most accurate and literal inferential aspect. This system will assist physicians in many kinds of medical diagnosis, prescribing treatments and doses, and in monitoring patient responses, basing on the body weight and age group of the patient, it will be able to provide instantaneous and timely information options, alternative ways and approaches to influence decision making during case analysis. The computerized system approach, a new model in Uganda termed as “Software Aided Treatment” (SAT) will try to change the moral and ethical approach and influence conduct so as to improve the skills, experience and values (social and ethical) in the administration and management of the disease and drugs (combination therapy and generics) by both the patient and the health worker.

Keywords: knowledge based software, management, treatment, diagnosis

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Authors: Antonios Panagopoulos, Stamoulis Georgios

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Dyslexia's diagnosis is made taking into account reading and writing skills and is carried out by qualified scientific staff. In addition, there are screening tests that are designed to give an indication of possible dyslexic difficulties. Their main advantage is that they create a pleasant environment for the user and reduce the stress that can lead to false results. An online application was created for the first time, as far as authors' knowledge, for screening Dyslexia in Greek high school students named «DyScreTe». Thus, a sample of 240 students between 16 and 18 years old in Greece was taken, of which 120 were diagnosed with dyslexia by an official authority in Greece, and 120 were typically developed. The main hypothesis that was examined is that students who were diagnosed with dyslexia by official authorities in Greece had significantly lower performance in the respective software tests. The results verified the hypothesis we made those children with dyslexia in each test had a lower performance com-pared to the type developed in successful responses, except for the intelligence test. After random sampling, it was shown that the new online application was a useful tool for screening dyslexia. However, computer evaluation cannot replace the diagnosis by a professional expert, but with the results of this application, the interdisciplinary team that deals with the differential diagnosis will create and evaluate, at a later time, the appropriate intervention program.

Keywords: dyslexia, screening tests, deficits, application

Procedia PDF Downloads 52

Authors: Teresa Isabel Lozano Pérez, Sandra Soca Lozano

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In Cuba, the childcare is one of the programs prioritized by the Ministry of Public Health and the birth of a child becomes a desired and rewarding event for the family, which is prepared for the reception of a healthy child. When this does not happen and after the first months of the child's birth begin to appear developmental deviations that indicate the presence of a disorder, the event becomes a live event potentially negative and generates disruptions in the family health. A quantitative, descriptive, and cross-sectional research methodology was conducted to describe the impact on family health of diagnosis of autism in a sample of 25 families of children diagnosed with infantile autism at the University Pediatric Hospital Juan Manuel Marquez Havana, Cuba; in the period between January 2014 and May 2015. The sample was non probabilistic and intentional from the inclusion criteria selected. As instruments, we used a survey to identify the structure of the family, life events inventory and an instrument to assess the relative impact, adaptive resources of family and social support perceived (IRFA) to identify the diagnosis of autism as life event. The main results indicated that the majority of families studied were nuclear, small and medium and in the formation stage. All households surveyed identified the diagnosis of autism in a child as an event of great importance and negative significance for the family, taking in most of the families studied a high impact on the four areas of family health and impact enhancer of involvement in family health. All the studied families do not have sufficient adaptive resources to face this situation, sensing that they received social support frequently, mainly in information and emotional areas. We conclude that the diagnosis of autism one of the members of the families studied is valued as a life event highly significant with unfavorably way causing an enhancer impact of involvement in family health especially in the areas ‘health’ and ‘socio-psychological’. Among the social support networks health institutions, partners and friends are highlighted. We recommend developing intervention strategies in families of these children to support them in the process of adapting the diagnosis.

Keywords: family, family health, infantile autism, life event

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Authors: Sofia Papadimitriou

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INTRODUCTION: The differential diagnosis between acute viral and bacterial infections is an important cost-effectiveness parameter at the stage of the treatment process in order to achieve the maximum benefits in therapeutic intervention by combining the minimum cost to ensure the proper use of antibiotics.The discovery of sensitive and robust molecular diagnostic tests in response to the role of the host in infections has enhanced the accurate diagnosis and differentiation of infections. METHOD: The study used a sample of six independent blood samples (total=756) which are associated with human proteins-proteins, each of which at the transcription stage expresses a different response in the host network between viral and bacterial infections.Τhe individual blood samples are subjected to a sequence of computer filters that identify a gene panel corresponding to an autonomous diagnostic score. The data set and the correspondence of the gene panel to the diagnostic patents a new Bangalore -Viral Bacterial (BL-VB). FINDING: We use a biomarker based on the blood of 10 genes(Panel-VB) that are an important prognostic value for the detection of viruses from bacterial infections with a weighted average AUROC of 0.97(95% CL:0.96-0.99) in eleven independent samples (sets n=898). We discovered a base with a patient score (VB 10 ) according to the table, which is a significant diagnostic value with a weighted average of AUROC 0.94(95% CL: 0.91-0.98) in 2996 patient samples from 56 public sets of data from 19 different countries. We also studied VB 10 in a new cohort of South India (BL-VB,n=56) and found 97% accuracy in confirmed cases of viral and bacterial infections. We found that VB 10 (a)accurately identifies the type of infection even in unspecified cases negative to the culture (b) shows its clinical condition recovery and (c) applies to all age groups, covering a wide range of acute bacterial and viral infectious, including non-specific pathogens. We applied our VB 10 rating to publicly available COVID 19 data and found that our rating diagnosed viral infection in patient samples. RESULTS: Τhe results of the study showed the diagnostic power of the biomarker VB 10 as a diagnostic test for the accurate diagnosis of acute infections in recovery conditions. We look forward to helping you make clinical decisions about prescribing antibiotics and integrating them into your policies management of antibiotic stewardship efforts. CONCLUSIONS: Overall, we are developing a new property of the RNA-based biomarker and a new blood test to differentiate between viral and bacterial infections to assist a physician in designing the optimal treatment regimen to contribute to the proper use of antibiotics and reduce the burden on antimicrobial resistance, AMR.

Keywords: acute infections, antimicrobial resistance, biomarker, blood transcriptome, systems biology, classifier diagnostic score

Procedia PDF Downloads 128

Authors: Ioannis Panagiotopoulos, Efstathios Kastritis, Anastasia Katinioti, Georgios Efthymiadis, Argyrios Doumas, Maria Koutelou

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Introduction: Transthyretin amyloidosis (ATTR) is a rare but serious infiltrative disease. Myocardial scintigraphy with DPD has emerged as the most effective, non-invasive, highly sensitive, and highly specific diagnostic method for cardiac ATTR amyloidosis. However, there are cases in which additional laboratory investigations reveal AL amyloidosis or other diseases despite a positive DPD scintigraphy. We describe the experience from the Onassis Cardiac Surgery Center and the monitoring center for infiltrative myocardial diseases of the cardiology clinic at AHEPA. Materials and Methods: All patients with clinical suspicion of cardiac or extracardiac amyloidosis undergo a myocardial scintigraphy scan with Tc-99m DPD. In this way, over 500 patients have been examined. Further diagnostic approach based on clinical and imaging findings includes laboratory investigation and invasive techniques (e.g., biopsy). Results: Out of 76 patients in total with positive myocardial scintigraphy Grade 2 or 3 according to the Perugini scale, 8 were proven to suffer from AL Amyloidosis during the investigation of paraproteinemia. Among these patients, 3 showed Grade 3 uptake, while the rest were graded as Grade 2, or 2 to 3. Additionally, one patient presented diffuse and unusual radiopharmaceutical uptake in soft tissues throughout the body without cardiac involvement. These findings raised suspicions, leading to the analysis of κ and λ light chains in the serum, as well as immunostaining of proteins in the serum and urine of these specific patients. The final diagnosis was AL amyloidosis. Conclusion: The value of DPD scintigraphy in the diagnosis of cardiac amyloidosis from transthyretin is undisputed. However, positive myocardial scintigraphy with DPD should not automatically lead to the diagnosis of ATTR amyloidosis. Laboratory differentiation between ATTR and AL amyloidosis is crucial, as both prognosis and therapeutic strategy are dramatically altered. Laboratory exclusion of paraproteinemia is a necessary and essential step in the diagnostic algorithm of ATTR amyloidosis for all positive myocardial scintigraphy with diphosphonate tracers since >20% of patients with Grade 3 and 2 uptake may conceal AL amyloidosis.

Keywords: AL amyloidosis, amyloidosis, ATTR, myocardial scintigraphy, Tc-99m DPD

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Authors: Jay L. Fu

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Patients with Alzheimer's disease progressively lose their memory and thinking skills and, eventually, the ability to carry out simple daily tasks. The disease is irreversible, but early detection and treatment can slow down the disease progression. In this research, publicly available MRI data and demographic data from 373 MRI imaging sessions were utilized to build models to predict dementia. Various machine learning models, including logistic regression, k-nearest neighbor, support vector machine, random forest, and neural network, were developed. Data were divided into training and testing sets, where training sets were used to build the predictive model, and testing sets were used to assess the accuracy of prediction. Key risk factors were identified, and various models were compared to come forward with the best prediction model. Among these models, the random forest model appeared to be the best model with an accuracy of 90.34%. MMSE, nWBV, and gender were the three most important contributing factors to the detection of Alzheimer’s. Among all the models used, the percent in which at least 4 of the 5 models shared the same diagnosis for a testing input was 90.42%. These machine learning models allow early detection of Alzheimer’s with good accuracy, which ultimately leads to early treatment of these patients.

Keywords: Alzheimer's disease, clinical diagnosis, magnetic resonance imaging, machine learning prediction

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Authors: Felicia N. Obialo, Aliza Wingo, Thomas Wingo

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Psychosocial factors such as personality traits and social networks influence cognitive aging and dementia outcomes both positively and negatively. The inherent complexity of these factors makes defining the underlying mechanisms of their influence difficult; however, exploring their interactions affords promise in the field of cognitive aging. The objective of this study was to elucidate some of these interactions by determining the relationship between social network size and dementia outcomes and by determining whether personality traits mediate this relationship. The longitudinal Alzheimer’s Disease (AD) database provided by Rush University’s Religious Orders Study/Memory and Aging Project was utilized to perform retrospective regression and mediation analyses on 3,591 participants. Participants who were cognitively impaired at baseline were excluded, and analyses were adjusted for age, sex, common chronic diseases, and vascular risk factors. Dementia outcome measures included cognitive trajectory, clinical dementia diagnosis, and postmortem beta-amyloid plaque (AB), and neurofibrillary tangle (NT) accumulation. Personality traits included agreeableness (A), conscientiousness (C), extraversion (E), neuroticism (N), and openness (O). The results show a positive correlation between social network size and cognitive trajectory (p-value = 0.004) and a negative relationship between social network size and odds of dementia diagnosis (p = 0.024/ Odds Ratio (OR) = 0.974). Only neuroticism mediates the positive relationship between social network size and cognitive trajectory (p < 2e-16). Agreeableness, extraversion, and neuroticism all mediate the negative relationship between social network size and dementia diagnosis (p=0.098, p=0.054, and p < 2e-16, respectively). All personality traits are independently associated with dementia diagnosis (A: p = 0.016/ OR = 0.959; C: p = 0.000007/ OR = 0.945; E: p = 0.028/ OR = 0.961; N: p = 0.000019/ OR = 1.036; O: p = 0.027/ OR = 0.972). Only conscientiousness and neuroticism are associated with postmortem AD pathologies; specifically, conscientiousness is negatively associated (AB: p = 0.001, NT: p = 0.025) and neuroticism is positively associated with pathologies (AB: p = 0.002, NT: p = 0.002). These results support the study’s objectives, demonstrating that social network size and personality traits are strongly associated with dementia outcomes, particularly the odds of receiving a clinical diagnosis of dementia. Personality traits interact significantly and beneficially with social network size to influence the cognitive trajectory and future dementia diagnosis. These results reinforce previous literature linking social network size to dementia risk and provide novel insight into the differential roles of individual personality traits in cognitive protection.

Keywords: Alzheimer’s disease, cognitive trajectory, personality traits, social network size

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Authors: Sultan Çağırıcı, Arsida Bajrami, Beyza Aslan, Hacı Ali Erdoğan, Nejla Sözer Topçular, Dilek Bozkurt, Vildan Yayla

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Objective: Corticobasal syndrome (CBS) is phenotypically characterized by asymmetric rigidity, apraxia, alien-limb phenomenon, cortical sensory loss, dystonia and myoclonus. The underlying pathologies consists of corticobasal degeneration (CBD), progressive supra nuclear palsy, Alzheimer's, Creutzfeldt-Jakob and frontotemporal degeneration. CBD is a degenerative disease with clinical symptoms related to the prominent involvement of cerebral cortex and basal ganglia. CBD is a pathological diagnosis and antemortem clinical diagnosis may change many times. In this paper, we described the clinical features and discussed a cases diagnosed with symmetric CBS because of its rarity. Case: Seventy-five-year-old woman presented with a three years history of difficulty in speaking and reading. Involuntary hand jerks and slowness of movement also had began in the last six months. In the neurological examination the patient was alert but not fully oriented. The speech was non-fluent, word finding difficulties were present. Bilateral limited upgaze, bradimimia, bilateral positive cogwheel' rigidity but prominent in the right side, postural tremor and negative myoclonus during action on the left side were detected. Receptive language was normal but expressive language and repetition were impaired. Acalculia, alexia, agraphia and apraxia were also present. CSF findings were unremarkable except for elevated protein level (75 mg/dL). MRI revealed bilateral symmetric cortical atrophy prominent in the frontoparietal region. PET showed hypometabolism in the left caudate nucleus. Conclusion: The increase of data related to neurodegenerative disorders associated with dementia, movement disorders and other findings results in an expanded range of diagnosis and transitions between clinical diagnosis. When considered the age of onset, clinical symptoms, imaging findings and prognosis of this patient, clinical diagnosis was CBS and pathologic diagnosis as probable CBD. Imaging of CBD usually consist of typical asymmetry between hemispheres. Still few cases with clinical appearance of CBD may show symmetrical cortical cerebral atrophy. It is presented this case who was diagnosed with CBD although we found symmetrical cortical cerebral atrophy in MRI.

Keywords: symmetric cortical atrophy, corticobasal degeneration, corticobasal syndrome

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Authors: Sheema Shuja Khattak, Gule Saman, Imran Khan, Abdus Salam

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Image segmentation plays an important role in medical imaging applications. Therefore, accurate methods are needed for the successful segmentation of medical images for diagnosis and detection of various diseases. In this paper, we have used maximum entropy to achieve image segmentation. Maximum entropy has been calculated using Shannon, Renyi, and Tsallis entropies. This work has novelty based on the detection of skin lesion caused by the bite of a parasite called Sand Fly causing the disease is called Cutaneous Leishmaniasis.

Keywords: shannon, maximum entropy, Renyi, Tsallis entropy

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Authors: Nayer Mofidtabatabaei

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Advancements in artificial intelligence (AI) have transformed various fields, including psychology and behavioral sciences. This paper explores the diverse ways in which AI is applied to enhance research, diagnosis, therapy, and understanding of human behavior and mental health. We discuss the potential benefits and challenges associated with AI in these fields, emphasizing the ethical considerations and the need for collaboration between AI researchers and psychological and behavioral science experts. Artificial Intelligence (AI) has gained prominence in recent years, revolutionizing multiple industries, including healthcare, finance, and entertainment. One area where AI holds significant promise is the field of psychology and behavioral sciences. AI applications in this domain range from improving the accuracy of diagnosis and treatment to understanding complex human behavior patterns. This paper aims to provide an overview of the various AI applications in psychological and behavioral sciences, highlighting their potential impact, challenges, and ethical considerations. Mental Health Diagnosis AI-driven tools, such as natural language processing and sentiment analysis, can analyze large datasets of text and speech to detect signs of mental health issues. For example, chatbots and virtual therapists can provide initial assessments and support to individuals suffering from anxiety or depression. Autism Spectrum Disorder (ASD) Diagnosis AI algorithms can assist in early ASD diagnosis by analyzing video and audio recordings of children's behavior. These tools help identify subtle behavioral markers, enabling earlier intervention and treatment. Personalized Therapy AI-based therapy platforms use personalized algorithms to adapt therapeutic interventions based on an individual's progress and needs. These platforms can provide continuous support and resources for patients, making therapy more accessible and effective. Virtual Reality Therapy Virtual reality (VR) combined with AI can create immersive therapeutic environments for treating phobias, PTSD, and social anxiety. AI algorithms can adapt VR scenarios in real-time to suit the patient's progress and comfort level. Data Analysis AI aids researchers in processing vast amounts of data, including survey responses, brain imaging, and genetic information. Privacy Concerns Collecting and analyzing personal data for AI applications in psychology and behavioral sciences raise significant privacy concerns. Researchers must ensure the ethical use and protection of sensitive information. Bias and Fairness AI algorithms can inherit biases present in training data, potentially leading to biased assessments or recommendations. Efforts to mitigate bias and ensure fairness in AI applications are crucial. Transparency and Accountability AI-driven decisions in psychology and behavioral sciences should be transparent and subject to accountability. Patients and practitioners should understand how AI algorithms operate and make decisions. AI applications in psychological and behavioral sciences have the potential to transform the field by enhancing diagnosis, therapy, and research. However, these advancements come with ethical challenges that require careful consideration. Collaboration between AI researchers and psychological and behavioral science experts is essential to harness AI's full potential while upholding ethical standards and privacy protections. The future of AI in psychology and behavioral sciences holds great promise, but it must be navigated with caution and responsibility.

Keywords: artificial intelligence, psychological sciences, behavioral sciences, diagnosis and therapy, ethical considerations

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Authors: Ghazanfar Ali, Fahad N Almajhdi

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This study provides data on the viral diagnosis and molecular epidemiology of influenza A(H3N2) virus isolated in Riyadh, Saudi Arabia. Nasopharyngeal aspirates from 80 clinically infected patients in the peak of the 2010-2011 winter seasons were processed for viral diagnosis by RT-PCR. Sequencing of entire HA and NA genes of representative isolates and molecular epidemiological analysis were performed. A total of 06 patients were positive for influenza A, B and respiratory syncytial viruses by RT-PCR assays; out of these only one sample was positive for influenza A(H3N2) by RT-PCR. Phylogenetic analysis of the HA and NA gene sequences showed identities higher than 99-98.8 % in both genes. They were also similar to reference isolates in HA sequences (99 % identity) and in NA sequences (99 % identity). Amino acid sequences predicted for the HA gene were highly identical to reference strains. The NA amino acid substitutions identified did not include the oseltamivir-resistant H275Y substitution. Conclusion: Viral isolation and RT-PCR together were useful for diagnosis of the influenza A (H3N2) virus. Variations in HA and NA sequences are similar to those identified in worldwide reference isolates and no drug resistance was found.

Keywords: influenza A (H3N2), genetic characterization, viral isolation, RT-PCR, Saudi Arabia

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Authors: Santos Cabrera, Halbert Yesid, Moncada Nino, Alvaro Fernando, Rincon Cuta, Yeisson Alexis

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The present work evaluates the feasibility of implementing a health and use monitoring system (HUMS), based on vibration analysis as a condition-based maintenance program for the UH60L 'Blackhawk' helicopters. The mixed approach used consists of contributions from national and international experts, the analysis of data extracted from the software (Meridium), the correlation of variables derived from the diagnosis of availability, the development, and application of the HUMS system, the evaluation of the latter through of the use of instruments designed for the collection of information using the DELPHI method and data capture with the device installed in the helicopter studied. The results obtained in the investigation reflect the context of maintenance in aerial operations, a reduction of operation and maintenance costs of over 2%, better use of human resources, improvement in availability (5%), and fulfillment of the aircraft’s security standards, enabling the implementation of the monitoring system (HUMS) in the condition-based maintenance program. New elements are added to the study of maintenance based on condition -specifically, in the determination of viability based on qualitative and quantitative data according to the methodology. The use of condition-based maintenance will allow organizations to adjust and reconfigure their strategic, logistical, and maintenance capabilities, aligning them with their strategic objectives of responding quickly and adequately to changes in the environment and operational requirements.

Keywords: air transportation sustainability, HUMS, maintenance based condition, maintenance blackhawk capability

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Authors: Carlos Roncero-Parra, Alfonso Parreño-Torres, Jorge Mateo Sotos, Alejandro L. Borja

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In this research, different methods based on machine/deep learning algorithms are presented for the classification and diagnosis of patients with mental disorders such as alzheimer. For this purpose, the signals obtained from 32 unipolar electrodes identified by non-invasive EEG were examined, and their basic properties were obtained. More specifically, different well-known machine learning based classifiers have been used, i.e., support vector machine (SVM), Bayesian linear discriminant analysis (BLDA), decision tree (DT), Gaussian Naïve Bayes (GNB), K-nearest neighbor (KNN) and Convolutional Neural Network (CNN). A total of 668 patients from five different hospitals have been studied in the period from 2011 to 2021. The best accuracy is obtained was around 93 % in both ADM and ADA classifications. It can be concluded that such a classification will enable the training of algorithms that can be used to identify and classify different mental disorders with high accuracy.

Keywords: alzheimer, machine learning, deep learning, EEG

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Authors: Rajesh Ranjan, Marimuthu Palaniswami, A. A. Hashmi

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With the changing lifestyle and environment around us, the prevalence of the critical and incurable disease has proliferated. One such condition is the neurological disorder which is rampant among the old age population and is increasing at an unstoppable rate. Most of the neurological disorder patients suffer from some movement disorder affecting the movement of their body parts. Tremor is the most common movement disorder which is prevalent in such patients that infect the upper or lower limbs or both extremities. The tremor symptoms are commonly visible in Parkinson’s disease patient, and it can also be a pure tremor (essential tremor). The patients suffering from tremor face enormous trouble in performing the daily activity, and they always need a caretaker for assistance. In the clinics, the assessment of tremor is done through a manual clinical rating task such as Unified Parkinson’s disease rating scale which is time taking and cumbersome. Neurologists have also affirmed a challenge in differentiating a Parkinsonian tremor with the pure tremor which is essential in providing an accurate diagnosis. Therefore, there is a need to develop a monitoring and assistive tool for the tremor patient that keep on checking their health condition by coordinating them with the clinicians and caretakers for early diagnosis and assistance in performing the daily activity. In our research, we focus on developing a system for automatic classification of tremor which can accurately differentiate the pure tremor from the Parkinsonian tremor using a wearable accelerometer-based device, so that adequate diagnosis can be provided to the correct patient. In this research, a study was conducted in the neuro-clinic to assess the upper wrist movement of the patient suffering from Pure (Essential) tremor and Parkinsonian tremor using a wearable accelerometer-based device. Four tasks were designed in accordance with Unified Parkinson’s disease motor rating scale which is used to assess the rest, postural, intentional and action tremor in such patient. Various features such as time-frequency domain, wavelet-based and fast-Fourier transform based cross-correlation were extracted from the tri-axial signal which was used as input feature vector space for the different supervised and unsupervised learning tools for quantification of severity of tremor. A minimum covariance maximum correlation energy comparison index was also developed which was used as the input feature for various classification tools for distinguishing the PT and ET tremor types. An automatic system for efficient classification of tremor was developed using feature extraction methods, and superior performance was achieved using K-nearest neighbors and Support Vector Machine classifiers respectively.

Keywords: machine learning approach for neurological disorder assessment, automatic classification of tremor types, feature extraction method for tremor classification, neurological movement disorder, parkinsonian tremor, essential tremor

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Authors: Tianle Yang

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As a common mental disorder, depression is a leading cause of various diseases worldwide. Early detection and treatment of depression can dramatically promote remission and prevent relapse. However, conventional ways of depression diagnosis require considerable human effort and cause economic burden, while still being prone to misdiagnosis. On the other hand, recent studies report that physical characteristics are major contributors to the diagnosis of depression, which inspires us to mine the internal relationship by neural networks instead of relying on clinical experiences. In this paper, neural networks are constructed to predict depression from physical characteristics. Two initialization methods are examined - Xaiver and Kaiming initialization. Experimental results show that a 3-layers neural network with Kaiming initialization achieves 83% accuracy.

Keywords: depression, neural network, Xavier initialization, Kaiming initialization

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Authors: Yaya Gao, Jifeng Liu, Yafeng Liu

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Objectives: This study aimed to improve clinicians' understanding and diagnosis of the Mammary analogue secretory carcinoma of the salivary gland（MASC）. Methods: The clinical features of a MASC patient who was admitted to WestChina Hospital of Sichuan University in July 2020 were reviewed and analyzed. A 49-year-old woman with left upper neck pain for three months was admitted to the hospital. She underwent adenoma resection of the left submandibular gland 14 years ago and mucoepidermoid carcinoma resection surgery five years ago. Three months before admission, the patient developed pain in the left mandibular angle after "fatigue" and gradually developed radiation pain in the left ear, which could be relieved after rest. A mass of 1cm could be touched at the mandibular, with tenderness, poor mobility, and hard texture. No swelling, heat, pain, rupture, or pus was found on the surrounding skin. Color doppler ultrasonography of the salivary gland indicated a weak echo mass of 23*14*17mm in the left parotid gland. Results: Surgical excision was completed. Immunohistochemistry of the tumor samples after operation showed that P63（a few，+）, CK7（+）, S100（+）, DOG1（-）, Ki67（MIB-1）（+，5%），pan-TRK（+）, PAS(+) . ETV-6 gene translocation was detected in FISH in postoperative pathology, which indicated MASC. After this diagnosis, the patient sent the postoperative specimen of the second submandibular tumor to our hospital for consultation. The morphology of the two was similar. FISH detected ETV-6 gene translocation, so the second pathological diagnosis was revised to MASC. Conclusion: MASC of the salivary gland is a rare salivary gland tumor whose diagnosis depends on the result of the ETV6-NTRK3 fusion gene.

Keywords: mammary analogue secretory carcinoma, ETV6-NTRK3, salivary gland, misdiagnosed

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Authors: Rachel Shukrun, Szilvia Baron, Victoria Fidel, Anna Shusterman, Osnat Sher, Netanya Kollender, Dror Levin, Yair Peled, Yair Gortzak, Yoav Ben-Shahar, Revital Caspi, Sagi Gordon, Michal Manisterski, Ronit Elhasid

Abstract:

Ewing sarcoma (EWS) is a highly aggressive cancer with a survival rate of 70–80% for patients with localized disease and under 30% for those with metastatic disease. Tumor-infiltrating neutrophils (TIN) can generate extracellular net-like DNA structures known as neutrophil extracellular traps (NETs). However, little is known about the presence and prognostic significance of tumor-infiltrating NETs in EWS. Herein, we investigated 46 patients diagnosed with EWS and treated in the Tel Aviv Medical Center between 2010 and 2021. TINs and NETs were identified in diagnostic biopsies of EWS by immunofluorescent. In addition, NETs were investigated in neutrophils isolated from peripheral blood samples of EWS patients at diagnosis and following neoadjuvant chemotherapy. The relationships between the presence of TINs and NETs, pathological and clinical features, and outcomes were analyzed. Our results demonstrate that TIN and NETs at diagnosis were higher in EWS patients with metastatic disease compared to those with local disease. High NETs formation at diagnosis predicted poor response to neo-adjuvant chemotherapy, relapse, and death from disease (P < .05). NETs formation in peripheral blood samples at diagnosis was significantly elevated among patients with EWS compared to pediatric controls and decreased significantly following neoadjuvant chemotherapy. In conclusion, NETs formation seems to have a role in the EWS immune microenvironment. Their presence can refine risk stratification, predict chemotherapy resistance and survival, and serve as a therapeutic target in patients with EWS.

Keywords: Ewing sarcoma, tumor microenvironment, neutrophil, neutrophil extracellular traps (NETs), prognosis

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Authors: Kemka C. Ihemelandu, Chukwuemeka U. Ihemelandu

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The incidence of melanoma has been increasing rapidly over the past two decades, making melanoma a current public health crisis. Unfortunately, even as screening efforts continue to expand in an effort to ameliorate the death rate from melanoma, there is a need to improve diagnostic accuracy to decrease misdiagnosis. Artificial intelligence (AI) a new frontier in patient care has the ability to improve the accuracy of melanoma diagnosis. Convolutional neural network (CNN) a form of deep neural network, most commonly applied to analyze visual imagery, has been shown to outperform the human brain in pattern recognition. However, there are noted limitations with the accuracy of the CNN models. Our aim in this study was the optimization of convolutional neural network algorithms for the automated diagnosis of melanoma. We hypothesized that Optimal selection of the momentum and batch hyperparameter increases model accuracy. Our most successful model developed during this study, showed that optimal selection of momentum of 0.25, batch size of 2, led to a superior performance and a faster model training time, with an accuracy of ~ 83% after nine hours of training. We did notice a lack of diversity in the dataset used, with a noted class imbalance favoring lighter vs. darker skin tone. Training set image transformations did not result in a superior model performance in our study.

Keywords: melanoma, convolutional neural network, momentum, batch hyperparameter

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