Diagnostic Tool Research Articles

Studying Myostatin as a Diagnostic Indicator in Sera of Iraqi Women with Metabolic Syndrome

السمنة في منطقة البطن، ومقاومة الأنسولين ، وارتفاع السكر في الدم ، وعسر شحميات الدم ، وارتفاع ضغط الدم كلها جزء من متلازمة التمثيل الغذائي. تقوم الخلايا العضلية بتصنيع وإطلاق الميوكين المعروف باسمmyostatin ، والذي يعمل على خلايا العضلات لمنعها من النمو. كان الهدف من هذه الدراسة هو مقارنة مستويات الميوستاتين لدى الأشخاص الذين يعانون من متلازمة التمثيل الغذائي والذين لا يعانون منها. صممت الدراسة الحالية لمقارنة بعض المؤشرات الحيوية في امصال مرضى متلازمة الأيض والأشخاص الأصحاء من خلال تقدير الميوستاتين، نسبة السكر في الدم لدى الصائمين (FBS) ، الكوليسترول ، البروتين الدهني عالي الكثافة (HDL) ، الدهون الثلاثية (T.G) ، البروتين الشحمي منخفض الكثافة. (LDL) ، البروتين الدهني منخفض الكثافة (VLDL) ، في امصال المرضى العراقيين المصابين بمتلازمة التمثيل الغذائي والأشخاص الأصحاء. تم تقسيم ما مجموعة ثمانين امرأة إلى مجموعتين: المجموعة الأولى 40 مريض متلازمة ايضية والمجموعة الثانية 40 شخصًا سليما . أظهرت النتائج زيادة معنوية في مستويات الميوستاتين p> 0.001 في مجموعة متلازمة التمثيل الغذائي مقارنة بالمجموعة الضابطة. أظهر منحنى ROC لليموستاتين قيمة فاصلة واضحة (5) عند حسابه في متلازمة التمثيل الغذائي. وخلصت الدراسة إلى أن الميوستاتين هو عامل تشخيصي للميوستاتين.

AI-assisted Analysis to Facilitate Detection of Humeral Lesions on Chest Radiographs.

Purpose To develop an artificial intelligence (AI) system for humeral tumor detection on chest radiographs (CRs) and evaluate the impact on reader performance. Materials and Methods In this retrospective study, 14 709 CRs (January 2000 to December 2021) were collected from 13 468 patients, including CT-proven normal (n = 13 116) and humeral tumor (n = 1593) cases. The data were divided into training and test groups. A novel training method called false-positive activation area reduction (FPAR) was introduced to enhance the diagnostic performance by focusing on the humeral region. The AI program and 10 radiologists were assessed using holdout test set 1, wherein the radiologists were tested twice (with and without AI test results). The performance of the AI system was evaluated using holdout test set 2, comprising 10 497 normal images. Receiver operating characteristic analyses were conducted for evaluating model performance. Results FPAR application in the AI program improved its performance compared with a conventional model based on the area under the receiver operating characteristic curve (0.87 vs 0.82, P = .04). The proposed AI system also demonstrated improved tumor localization accuracy (80% vs 57%, P < .001). In holdout test set 2, the proposed AI system exhibited a false-positive rate of 2%. AI assistance improved the radiologists' sensitivity, specificity, and accuracy by 8.9%, 1.2%, and 3.5%, respectively (P < .05 for all). Conclusion The proposed AI tool incorporating FPAR improved humeral tumor detection on CRs and reduced false-positive results in tumor visualization. It may serve as a supportive diagnostic tool to alert radiologists about humeral abnormalities. Keywords: Artificial Intelligence, Conventional Radiography, Humerus, Machine Learning, Shoulder, Tumor Supplemental material is available for this article. © RSNA, 2024.

Performance analysis of Alexnet for Classification of Knee Osteoarthritis.

In recent years, automated grading of knee osteoarthritis (KOA) has focused on determining disease progression. Clinical examinations and radiographic image review are necessary for diagnosis. Timely and accurate diagnosis, along with medical care, can slow down KOA progression. X-rays and MRI are crucial diagnostic tools. KOA diagnosis traditionally relies on radiologists' and clinicians' experience. However, the rapid development of deep learning technology (AI) offers promising solutions for medical applications. The objective of this study was to review and summarize various methods proposed by researchers for the automated grading of KOA. Additionally, this study aimed to evaluate the performance of the AlexNet model in classifying the severity of KOA. The performance of the AlexNet model has been compared to that of other models, and the results have been assessed. A comprehensive review of existing research on automated grading of KOA has been conducted. Various methods proposed by different researchers have been examined and summarized. The AlexNet model has been employed for classifying the severity of KOA, and its performance has been evaluated. A comparative analysis has been carried out to compare the performance of the AlexNet model with that of other models. The results obtained from the evaluation have been assessed to determine the effectiveness of the AlexNet model in the automated grading of KOA. The results of the study indicate that the AlexNet model demonstrates promising performance in classifying the severity of KOA. Comparative analysis reveals that the AlexNet model outperforms other models in terms of accuracy and efficiency. The evaluation of the model's performance provides valuable insights into the effectiveness of deep learning techniques for automated grading of KOA. This study highlights the significance of automated grading in the diagnosis and management of knee osteoarthritis. The utilization of deep learning technology, particularly the AlexNet model, shows promise in accurately classifying the severity of KOA. The findings suggest that automated grading methods can serve as valuable tools for healthcare professionals in assessing the progression of KOA and providing appropriate medical care. Further research and development in this area can contribute to enhancing the efficiency and accuracy of automated grading systems for KOA.

Intrinsic RNA Targeting Triggers Indiscriminate DNase Activity of CRISPR-Cas12a.

The CRISPR-Cas12a system has emerged as a powerful tool for next-generation nucleic acid-based molecular diagnostics. However, it has long been believed to be effective only on DNA targets. Here, we investigate the intrinsic RNA-enabled trans-cleavage activity of AsCas12a and LbCas12a and discover that they can be directly activated by full-size RNA targets, although LbCas12a exhibits weaker trans-cleavage activity than AsCas12a on both single-stranded DNA and RNA substrates. Remarkably, we find that the RNA-activated Cas12a possesses higher specificity in recognizing mutated target sequences compared to DNA activation. Based on these findings, we develop the "Universal Nuclease for Identification of Virus Empowered by RNA-Sensing" (UNIVERSE) assay for nucleic acid testing. We incorporate a T7 transcription step into this assay, thereby eliminating the requirement for a protospacer adjacent motif (PAM) sequence in the target. Additionally, we successfully detect multiple PAM-less targets in HIV clinical samples that are undetectable by the conventional Cas12a assay based on double-stranded DNA activation, demonstrating unrestricted target selection with the UNIVERSE assay. We further validate the clinical utility of the UNIVERSE assay by testing both HIV RNA and HPV 16 DNA in clinical samples. We envision that the intrinsic RNA targeting capability may bring a paradigm shift in Cas12a-based nucleic acid detection and further enhance the understanding of CRISPR-Cas biochemistry.

Особенности актуального состояния обучающихся в регионе с высокой степенью вовлеченности в последствия боевых действий

&lt;p style="text-align: justify;"&gt;&lt;strong&gt;Objective.&lt;/strong&gt; The study is aimed on estimation of students&amp;rsquo; current state in regions with high degrees of involvement in the consequences of hostilities. &lt;br&gt;&lt;strong&gt;Background.&lt;/strong&gt; Research data, mainly foreign ones, indicate a high prevalence of violations of various degrees of severity, from mild adaptation disorders to symptoms of PTSD, in children either living in military conflict zones or having experience related to their involvement into consequences of hostilities. Such students have a great need for targeted psychological assistance. There is still a gap in domestic studies devoted to the analysis of the impact of involvement into consequences of hostilities on minority. Also, specialists are still experiencing deficits in compact tools allowing comprehensive assessment of children current state and their need for psychological assistance. &lt;br&gt;&lt;strong&gt;Study design.&lt;/strong&gt; We studied features of the students&amp;rsquo; current state in regions highly involved into the consequences of hostilities. using specially developed screening tools designed for students of 9&amp;ndash;11 grades.&lt;br&gt;&lt;strong&gt;Participants.&lt;/strong&gt; 646 students in grades 9&amp;ndash;11 (36% male, 64% female) living in regions experiencing the impact of events related to hostilities, mainly from Lugansk People&amp;rsquo;s Republic. &lt;br&gt;&lt;strong&gt;Measurements.&lt;/strong&gt; The study is based on a questionnaire developed to assess the current state in adolescents including items assessing a) manifestations of socio-psychological and mental maladaptation in various spheres of life, b) symptoms of posttraumatic stress disorder, c) psychological (personal and social) resources for coping with stress. This part of questionnaire includes lists if features among which student must choose those which fit his/her current state on the base of multiple choice. The questionnaire also contains items assessing the subjective well-being of children in life areas mentioned above by the 10-point Likert scale. The questionnaires were presented electronically, using the AnketologBox platform. Results were analyzed using IBM SPSS 27.0. &lt;br&gt;&lt;strong&gt;Results.&lt;/strong&gt; Results were obtained regarding the prevalence and severity of signs of socio-psychological maladaptation, including PTSD symptoms, and subjective estimation of well-being and psychological resources for coping with stress in students of 9&amp;ndash;11 grades. Significant differences were found between students according to the criterion of gender: a significantly higher level of vulnerability of girls compared to boys in the psychophysiological, emotional, behavioral spheres, as well as in relationships with loved ones.&lt;br&gt;&lt;strong&gt;С&lt;/strong&gt;&lt;strong&gt;onclusions.&lt;/strong&gt; The data analysis allowed us to identify the main features of the current state of students in the regions highly involved in the consequences of hostilities: the presence of severe distress in some children in the psychophysiological, cognitive, emotional, communicative and behavioral spheres, the tendency to decrease the level of well-being in these areas with age in secondary school students, the existence of special risk groups, which include female students of 9&amp;ndash;11grades. The proposed diagnostic tool appeared to be sensitive to phenomena under study varying from psychological resources of coping with stress to signs of maladaptation in students living in territories involved into the consequences of hostilities.&lt;/p&gt;

Can Optical Coherence Tomography Angiography be a Useful Adjunctive Diagnostic Tool in Patient with Non Arteritic Ischaemic Optic Neuropathy?

Abstract&#x0D; Introduction : Perfusion deficiency of the optic nerve head is presumed to have an immense role in the pathogenesis of non-arteritic ischaemic optic neuropathy (NAION). Optical coherence tomography angiography (OCTA) is a non-invasive method that can assess posterior pole vasculature at multiple levels. Thus, these case series want to compare the OCTA in the acute (before four weeks) and chronic phases (after six weeks) of NAION with their fellow unaffected eyes to give an overview of vasculature changes in NAION.&#x0D; Case Illustration : This case series consists of four patients with NAION. Half of them came with a sudden painless blurry vision, and the remaining presented with an abrupt visual field defect. All of them have diabetes mellitus and hypertension. None of them have superimposed vascular pathology. The first OCTA was taken within seven days of the first symptoms, and the second OCTA was taken more than six weeks after.&#x0D; Discussion : The high percentage of systemic vascular risk factors and the acute onset supported the vascular occlusive hypothesis in the pathogenesis of NAION. Meanwhile, the OCTA provides an actual overview of the vasculature changes. Microvasculature is more likely to expand in the acute phase, and a more significant expansion was found in greater optic disc edema. On the other hand, microvascular tend to attenuate in the chronic phase, representing the pruning of capillaries. A swept-source OCTA with deeper penetration might give a better peripapillary choriocapillaris image in the acute phase.&#x0D; Conclusion : OCTA is a promising adjunctive tool for diagnosing and monitoring progression in patients with NAION.

Recombinase-Aided Loop-Mediated Isothermal Amplification on Human Plasmodium knowlesi.

Loop-mediated isothermal amplification (LAMP) is a nucleic acid amplification technique that can amplify specific nucleic acids at a constant temperature (63-65°C) within a short period (<1 hour). In this study, we report the utilization of recombinase-aided LAMP to specifically amplify the 18S sRNA of Plasmodium knowlesi. The method was built on a conventional LAMP assay by inclusion of an extra enzyme, namely recombinase, into the master mixture. With the addition of recombinase into the LAMP assay, the assay speed was executed within a time frame of less than 28 minutes at 65°C. We screened 55 P. knowlesi samples and 47 non-P. knowlesi samples. No cross-reactivity was observed for non-P. knowlesi samples, and the detection limit for recombinase-aided LAMP was one copy for P. knowlesi after LAMP amplification. It has been reported elsewhere that LAMP can be detected through fluorescent readout systems. Although such systems result in considerable limits of detection, the need for sophisticated equipment limits their use. Hence, we used here a colorimetric detection platform for the evaluation of the LAMP assay's performance. This malachite green-based recombinase-aided LAMP assay enabled visualization of results with the naked eye. Negative samples were observed by a change in color from green to colorless, whereas positive samples remained green. Our results demonstrate that the LAMP assay developed here is a convenient, sensitive, and useful diagnostic tool for the rapid detection of knowlesi malaria parasites. This method is suitable for implementation in remote healthcare settings, where centralized laboratory facilities, funds, and clinicians are in short supply.

Physical Education Teachers’ Swimming Skill Analysis in 6- to 12-Year-Old Children: Findings From an Online Survey

Purpose: This study aims to investigate how physical education teachers analyze their students’ swimming skills. Particular attention is given to information gathering within the diagnostic process. Methods: Data were collected from a quantitative online survey of German physical education teachers from primary and secondary schools (n = 551). This survey’s questionnaire is based on evaluated statements from a qualitative interview study (n = 10). Findings: Teachers’ diagnostic approaches vary greatly and differ in terms of quality criteria and usability. The predominant method used is movement observation, but 50.3% of the teachers do it rather rarely or without the use of criteria. Many of them (63.8%) would like to be supported by a diagnostic tool for the analysis of swimming skills. Discussion/Conclusion: It has been concluded that an accurate analysis of the students’ swimming skills as a precondition for adaptive lesson structuring is not achieved. It is necessary to determine whether a diagnostic tool could improve this process.

Use of Albumin for Drug Delivery as a Diagnostic and Therapeutic Tool.

Drug delivery is an important topic that has attracted the attention of researchers in recent years. Albumin nanoparticles play a significant role in drug delivery as a carrier due to their unique characteristics. Albumin is non-toxic, biocompatible, and biodegradable. Its structure is such that it can interact with different drugs, which makes the treatment of the disease faster and also reduces the side effects of the drug. Albumin nanoparticles can be used in the diagnosis and treatment of many diseases, including cancer, diabetes, Alzheimer's, etc. These nanoparticles can connect to some compounds, such as metal nanoparticles, antibodies, folate, etc. and create a powerful nanostructure for drug delivery. In this paper, we aim to investigate albumin nanoparticles in carrier format for drug delivery application. In the beginning, different types of albumin and their preparation methods were discussed, and then albumin nanoparticles were discussed in detail in diagnosing and treating various diseases.

Electrocardiogram signal processing algorithm on microcontroller using wavelet transform method

The electrocardiogram (ECG) is an important parameter for analyzing the cardiac system. It serves as the primary diagnostic tool for patients with suspected heart disease, guiding appropriate cardiac investigations according to the disease or condition suspected. However, ECG measurements may generate noise, leading to false diagnoses. The wavelet transform is an effective and widely-used technique for eliminating noise. Typically, analysis and generation algorithms are developed on computer and using software built in. This paper presents a noise elimination algorithm based on the wavelet transform method, designed to operate on resource-limited Node microcontroller unit (MCU). An efficiency study was conducted to determine the optimum mother wavelet implementation of the algorithm, and the results showed that, when considering synthetic ECG signals, db4 was the most suitable for eliminating interference by achieving the highest signal to noise ratio (SNR) and correlation coefficient. In addition, this algorithm prototype can analyze ECG signals using the wavelet transform method processed in a microcontroller and is accurate compared to reliable programs. It has the potential to be further developed into a low-cost portable ECG signal measurement tool for use in remote medicine, healthcare facilities in resource-limited areas, education and training, as well as home monitoring for chronic patients.

Relationship Between Subjective Questionnaires and Videofluoroscopy of Dysphagia Evaluation: A Systematic Review and Meta-Analysis.

Early detection of dysphagia is important for preventing aspiration pneumonia. Although videofluoroscopy is currently the primary diagnostic tool for dysphagia, access to this tool may be limited because of radiation exposure risk, high cost, and other factors. In this study, a meta-analysis was used to determine the strength of the correlation between dysphagia detection outcomes obtained using subjective questionnaires and videofluoroscopy. The PubMed and Embase databases were searched for original articles up to December 2022. Studies published in English that used cross-sectional designs to assess the correlation between subjective questionnaires and videofluoroscopy were considered eligible for inclusion. The search terms used included "dysphagia," "questionnaire," and "videofluoroscopy." Two reviewers critically appraised and extracted the correlation coefficient r values. In addition, a random-effects meta-analysis was conducted. The Q statistic was used to assess the heterogeneity among the included studies. Publication bias was checked using the funnel plot and Egger's tests. Multilevel analysis was used to determine sensitivity to consider within-study correlations. In addition, subgroup analyses were conducted based on type of questionnaire, head and neck cancer, and English-speaking regions. The meta-analysis included five studies and 856 patients using the Eating Assessment Tool-10 and one study and 27 patients using the Sydney Swallow Questionnaire. The results of the random-effects meta-analysis showed a moderate relationship between the subjective questionnaires and videofluoroscopy ( r = .35, 95% CI [0.20, 0.48]). Similar results were also obtained using multilevel analysis ( r = .34, 95% CI [0.25, 0.42]). No publication bias was found for any of the studies ( p = .88). In the subgroup analyses, a moderate relationship between Eating Assessment Tool-10 and videofluoroscopy ( r = .31, 95% CI [0.19, 0.42]) and an ultrahigh relationship between Sydney Swallow Questionnaire and video-fluoroscopy ( r = .74, 95% CI [0.50, 0.87]) were found. Furthermore, moderate associations were observed within each head and neck cancer and English-speaking regions subgroup. However, no significant differences were found between these two subgroups. These results indicate the subjective questionnaires considered in this study share a moderate relationship with videofluoroscopy. Subjective questionnaires may be used as an auxiliary tool by nurses and homecare givers for the early assessment of dysphagia risk in patients.

The value of multiplex PCR in describing the microbial ecology of respiratory infections during the COVID 19 pandemic

Respiratory infections constitute a frequent reason for consultation and hospitalization and their emergence means that the identification of pathogens is important for the implementation of appropriate treatment for effective patient care. Multiplex PCR is a diagnostic tool developed for the identification of the microbial agent and the detection of resistance mechanisms. The objective of this study was to highlight and identify by multiplex PCR the bacteria and viruses responsible for respiratory infections in patients presenting clinical signs, and in whom biological examinations did not identify germs in cause during the COVID 19 pandemic, to be able to follow the epidemiology and microbial ecology of lower respiratory infections which has undergone changes, due to hygiene rules as well as current measures and methods of controlling this pandemic.

Deep learning-based automatic segmentation of COVID-19 in chest X-ray images using ensemble neural net sentinel algorithm

IntroductionCOVID-19 has affected billions of people worldwide and has serious public health effects. Automatic image segmentation is crucial to improve clinical decision-making for full assessments of illness management as well as monitoring. It plays a crucial part in detecting, diagnosing, and tracking diseases by allowing for the exact or accurate segmentation of diseased regions. Automatic segmentation and classifying medical images remains the most difficult obstacle in medical diagnosis, however, because of the lack of annotated medical images. MethodsIn this article, we propose the Ensemble Neural Net Sentinel Algorithm (ENNSA), and unique deep learning (DL) method for the segmentation and classification of COVID-19. Chest X-ray dataset is first collected from Kaggle and preprocessed. The features in the processed image are retrieved using the Insistent Grey Level Co-occurrence Matrix (IGLCM). ResultsThe suggested ENNSA outperforms current techniques in terms of accuracy (99.25), precision (100), recall (43), and F1 score (61) regardless of unbalanced datasets, indicating its promise as a diagnostic tool for respiratory diseases. ConclusionIn addition, the segmentation approach is used to separate the chosen features for COVID-19 classification from the chest X-ray images. The investigational outcomes demonstrate that our proposed strategy outperforms currently used methods in classifying COVID-19.

Differential diagnosis of gastric low- and high grade dysplasia using C6orf15 protein

ObjectiveTo investigate the expression of C6orf15 protein in gastric endoscopic biopsy specimens and its usage as an ancillary diagnostic biomarker in determining the grade of gastric dysplasia. MethodsWe selected 102 patients with gastric endoscopic biopsy specimens from Jinling Hospital. These were divided into four groups: 22 cases of gastric mucosal benign lesions, 28 with low-grade dysplasia (LGD, intestinal-type: 21 cases，foveolar-type: 7cases), 28 with high-grade dysplasia (HGD, intestinal-type: 20 cases，foveolar-type: 8 cases), and 24 cases of gastric adenocarcinoma. We examined the expressions of C6orf15, P53, and Ki67 in 102 gastric endoscopic biopsy specimens, including 47 cases with accompanying endoscopic submucosal dissection (ESD) specimens, using immunohistochemistry. ResultsIn gastric HGD and gastric adenocarcinoma, the c6orf15 protein exhibits diffuse and strong cytoplasmic expression in tumor cells. Conversely, in gastric LGD and benign gastric mucosal lesions, the c6orf15 protein shows negative or faint yellow cytoplasmic staining. The expression rate of C6orf15 in high-grade gastric dysplasia (HGD, 93 %) and gastric adenocarcinoma (100 %) was significantly higher than in the gastric mucosal benign lesion group (0 %) and the low-grade dysplasia (LGD, 7 %) group (P < 0.001). ConclusionThe detection of C6orf15 protein expression could serve as a valuable adjunctive diagnostic tool for distinguishing between gastric HGD, LGD, and benign lesions. The combined assessment of C6orf15, P53, and Ki67 expressions may be beneficial in determining the grade of gastric dysplasia and evaluating the risk of progression in gastric mucosal lesions in clinical practice.

Rapid and non-invasive detection of malaria parasites using near-infrared spectroscopy and machine learning.

Novel and highly sensitive point-of-care malaria diagnostic and surveillance tools that are rapid and affordable are urgently needed to support malaria control and elimination. We demonstrated the potential of near-infrared spectroscopy (NIRS) technique to detect malaria parasites both, in vitro, using dilutions of infected red blood cells obtained from Plasmodium falciparum cultures and in vivo, in mice infected with P. berghei using blood spotted on slides and non-invasively, by simply scanning various body areas (e.g., feet, groin and ears). The spectra were analysed using machine learning to develop predictive models for infection. Using NIRS spectra of in vitro cultures and machine learning algorithms, we successfully detected low densities (<10-7 parasites/μL) of P. falciparum parasites with a sensitivity of 96% (n = 1041), a specificity of 93% (n = 130) and an accuracy of 96% (n = 1171) and differentiated ring, trophozoite and schizont stages with an accuracy of 98% (n = 820). Furthermore, when the feet of mice infected with P. berghei with parasitaemia ≥3% were scanned non-invasively, the sensitivity and specificity of NIRS were 94% (n = 66) and 86% (n = 342), respectively. These data highlights the potential of NIRS technique as rapid, non-invasive and affordable tool for surveillance of malaria cases. Further work to determine the potential of NIRS to detect malaria in symptomatic and asymptomatic malaria cases in the field is recommended including its capacity to guide current malaria elimination strategies.

Transforming Healthcare: A Comprehensive Approach to Mitigating Bias and Fostering Empathy through AI-Driven Augmented Reality

The integration of Artificial Intelligence (AI) into Augmented Reality (AR) for medical applications is propelled by the aim to address evident healthcare disparities. Certain communities have encountered disparities in medical diagnoses, exemplified by Black individuals exhibiting a 2.4 times higher likelihood of schizophrenia diagnosis compared to their white counterparts (Faber et al., 2023). These disparities often arise from structured interview assessments overlooking cultural nuances, resulting in increased misdiagnosis rates. This study leverages AI and AR to develop unbiased diagnostic tools and enhance empathy in healthcare professionals' training. Uniquely prioritizing the reduction of biased language and the fostering of empathy through AI-driven Natural Language Processing (NLP) and AI-driven virtual patients, the research aims to enhance diagnostic accuracy while promoting cultural sensitivity among healthcare professionals. Aligned with broader goals of achieving equitable healthcare and reducing disparities, the evaluation involves pre- and post-training assessments to measure language improvements and empathy enhancements. Successful implementation could lead to a more equitable healthcare landscape, fostering trust in AI-driven systems and ensuring fairer medical care for diverse communities.

Abstract 7289: PancreaAlert: Intelligent nanoengineered biosensor for pancreatic cancer

Abstract Introduction: Pancreatic cancer accounts for 3% of cancer cases and 7% of cancer deaths in the United States. It is currently in the third position with respect to cancer-related deaths, and it is expected to move to second place as of 2040. This is mainly due to its diagnosis in the later stage. If detected in the first or second stage surgery can be performed thus increasing the patient’s survival rate. The current methods of diagnosis cannot do early stage or asymptomatic detection. So, detecting cancer-specific biomarkers can aid in the prognosis. Serum protein-based biomarkers carbohydrate antigen CA242, CA19-9, and carcinoembryonic antigen CEA are specific to pancreatic cancer. Thus, in this preliminary study, we aim to develop a nanoengineered electrochemical biosensor combined with machine learning to detect pancreatic cancer-specific biomarkers CA242, CA19-9, and CEA. Methodology: Nanoengineering the biosensor: The gold working electrode of the gold- biosensor (DRP-250AT) was coated with graphene oxide nano colloidal solution and kept in UV overnight for the graphene oxide nanosheet to crosslink with the working electrode. Detection of biomarkers using an electrochemical biosensor: First, a Crosslinker (DSP) was added to ethanol cleaned biosensor to bind the analytes. Following that antibody specific to each biomarker were added to the biosensors. The addition of TBS superblock prevented non-specific binding. Then different concentrations of biomarkers were added, and the corresponding sensor was subjected to various electrochemical analyses such as electrochemical impedance spectroscopy and voltammetry. SEM-EDAX analysis: The antigen-antibody binding on the biosensor's surface was confirmed. Automation and machine learning: Python code was developed to automate the analysis process and machine learning models (support vector machine and neural network) were used to classify the concentrations into various risk levels. The sensitivity of the developed tool was high when compared with conventional methods like ELISA and confocal microscopy. Results: From the electrochemical studies we found that with increasing concentration of biomarkers, there was a trend in output parameters: capacitance, resistance, impedance and area under the curve. By analyzing this trend, the concentrations of biomarkers can be detected. Using this data as input the machine learning model (accuracy greater than 80%) classified the concentrations into risk levels (normal, low risk, and high risk). Significance: Nanoengineering with graphene oxide nanosheets enhances the sensitivity of electrochemical biosensors. Automation and machine learning models help in the analysis of large datasets produced by this sensor. Overall, this device aids in the prognosis of pancreatic cancer. Our preliminary results are encouraging but more thorough research is required to make this diagnostic tool a working application. Citation Format: Meenal Karunanidhi, Hemalatha Kanniyappan, Edith Zhan, Ruth Mathew, Yani Sun, Junyi Wu, Yan Yan, Gnanasekar Munirathinum, Mathew T. Mathew. PancreaAlert: Intelligent nanoengineered biosensor for pancreatic cancer [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 7289.

Liquid biopsy using non-coding RNAs and extracellular vesicles for breast cancer management.

This article examines liquid biopsy using non-coding RNAs and extracellular vesicles in detail. Liquid biopsy is emerging as a prominent non-invasive diagnostic tool in the treatment of breast cancer. We will elucidate the roles of these molecules in early detection, monitoring treatment effectiveness, and prognostic assessment of breast cancer. Additionally, the clinical significance of these molecules will be discussed. We aim to delve into the distinct characteristics of these molecules and their possible roles in breast cancer management, with an anticipation of their contribution to future diagnostic and therapeutic advancements.

Monitoring the psychological component of higher education quality using ICT tools

This study examines the use of information and communication technologies (ICT) to monitor the psychological component of higher education quality. A literature review covers the concepts of higher education quality and its monitoring, the psychological factors involved, and the potential of ICT tools, including Google services. The study details a monitoring procedure using Google Forms to assess indicators like relationships between participants, satisfaction levels, and organisational climate. The computer program “Personnel – Ψ” is also presented as a tool for comprehensive psychological diagnostics of teachers. The advantages of using ICT tools for efficient data collection, analysis and management are discussed. The results demonstrate the feasibility and effectiveness of using ICT to improve monitoring of the psychological aspects of educational quality and make evidence-based decisions to enhance the educational environment. The use of user-friendly tools like Google Forms facilitates regular assessment of quality indicators and aids institutions in ensuring participant satisfaction and optimal conditions for learning and growth.

A three month old infant with severe respiratory distress

This case report discusses the diagnostic challenge of congenital lobar emphysema (CLE) in a three-month old infant with severe respiratory distress. The infant was initially misdiagnosed and managed as a case of pneumothorax. This case highlights the importance of CT scans as a diagnostic tool for early diagnosis and lifesaving management of CLE. It also signifies the need for adequate funds and infrastructure in the health care system especially in rural areas of developing countries like Pakistan. Keywords: Congenital lobar emphysema, respiratory distress, CT scan.