Method For Early Diagnosis Research Articles

Diagnostic and prognostic value of serum Cys-C, retinol-binding protein, and ischemia-modified albumin in patients with coronary heart disease: A diagnostic accuracy study.

The use of 3 biomarkers - cystatin-C (Cys-C), retinol-binding protein (RBP), and ischemia-modified albumin (IMA) - for the clinical classification and outcome of coronary heart disease (CHD) has not been adequately evaluated. We explored the serum levels of these 3 markers and evaluated their diagnostic and prognostic values in patients with CHD. This retrospective case-control study, conducted between June 2017 and June 2018, included 201 patients with CHD hospitalized at the Henan Provincial People's Hospital and 127 healthy individuals from Henan Provincial People's Hospital as controls. Cys-C, RBP, IMA levels, and other laboratory parameters in the 2 groups were determined, and patient outcomes were analyzed. Cys-C, RBP, and IMA levels were higher in the case group than in the control group (P < .05). Logistic regression analysis confirmed that these 3 biomarkers were independent risk factors for CHD. Each indicator has clinical significance in the diagnosis and prognosis of CHD, with RBP being the most significant. The AUC value for CHD detection using a combination of the 3 indicators was 0.783, and the sensitivity and specificity values were 78% and 74.6%, respectively. Simultaneous detection of Cys-C, RBP, and IMA could be an optimal method for early diagnosis and prognosis of CHD.

Prognostic algorithm for early diagnosis of subcritical conditions as predictors of sudden cardiac death

Aim. To develop a method for early diagnosis of subcritical homeostasis disorders leading to sudden cardiac death (SCD). The basis is to improve the efficiency of predictive algorithms.Material and methods. This pilot, controlled, open-label, randomized, prospective clinical trial included 220 patients at risk of SCD and 150 patients without risk of SCD. Main and control groups was formed according to the global cardiovascular risk score. Based on the informative features proposed by specialized experts using multivariate statistics methods (discriminant analysis), two condition classes were formed. The conducted exploratory analysis confirmed the significance of diagnostic criteria in relation to SCD manifestation (manifestation of cardiac arrest — MCA), which is an integral assessment of a fatal complication. The development of decision rules was carried out on the basis of soft computing technology.Results. Taking into account the priority of clinical research, namely, the identification of subcritical stages of MCA, a classifier is proposed according to basic severity of patients — the severity of critical condition risk (SCCR). The discriminant function and intersection areas between MCA subclasses in the conditions of early SCD diagnosis determine the transition to soft computing technology. Membership functions for severe MCA are formed, followed by their iteration according to E. Shortliffe. The final decision rule, using a fuzzy classifier, differentiates the MCA into stages with different SCCR. In parallel with standard protocols for the management of severe somatic patients (chronic obstructive pulmonary disease, chronic kidney disease, hepatocellular failure), based on the proposed algorithm with an integral assessment of critical conditions, using the MCA decision rule in the main group in 30,5% of cases, subcritical stage was revealed, followed by targeted treatment and preventive support. In the first group, a subcritical condition was detected in 67 patients (30,5%); a critical condition without SCD — in 3 patients (1,4%). In all noted cases, early prevention of SCD was successfully carried out (these patients were transferred to a class with a lower SCD degree). Using conventional prognostic scores in this group, 46 patients (20,9%) were identified with a subcritical condition and 1 (0,4%) with a critical condition. In the control group, subcritical condition was determined in 35 patients (23,3%), of which 17 patients (11,3%) had a moderate risk of SCD. Using conventional prognostic scores, 23 patients (15,3%) with subcritical condition were identified.Conclusion. In the conditions of intensive care unit, general medicine departments, hemodialysis department, cardiac surgery, and organ transplantation department, an algorithm for early diagnosis and risk stratification of SCD with an integral assessment (MCA) should be used. The fuzzy classifier MCA according to SCCR makes it possible to carry out timely correction of treatment measures in addition to standard protocols.

The role of doxorubicin in the formation of cardiotoxicity is a consensus statement. Part II. Cardiotoxicity of doxorubicin unrelated to myocytes and cardioprotection strategy (review)

Introduction. The use of doxorubicin in clinical practice has shown cumulative and dose-dependent toxic effects on cardiomyocytes, leading to an increase of mortality risk among patients with cancer and as a resulting to restrictions in the indications for its use.Text. A dangerous adverse reaction of doxorubicin is cardiomyopathy, leading to congestive heart failure. Cardiotoxicity is based on at least several pathophysiological mechanisms (described in more detail in the first part of the review), leading to damage to cardiomyocytes as a result of oxidative stress with the formation of free radicals, dysfunction of mitochondria, autophagy, release of nitric oxide and inflammatory mediators, as well as changes in gene expression and proteins leading to apoptosis. The current (second) part of the review provides detailed information on the actual understanding of the pathophysiological mechanisms underlying the described cardiotoxicity, the effect of doxorubicin on other heart cells. The use of cardioprotective strategies will reduce the severity and likelihood of developing cardiotoxicity. This article describes strategies based on reducing the maximum cumulative dose, changing the speed of doxorubicin administration, using pegylated liposomal formulations and cardioprotective agents, as well as exercise.Conclusion. Despite the huge number of scientific papers devoted to various aspects of cardiotoxicity of doxorubicin, its prevention and treatment, this issue requires more careful study and development of more advanced methods of early diagnosis, prevention and more effective therapy the complication.

A comparative study on breast cancer classification with stratified shuffle split and K-fold cross validation via ensembled machine learning

In breast cancer, early diagnosis and treatment method hold paramount significance for the augmented survival rates. Through a comprehensive dataset including clinical and genomic information, this study assesses the diverse analytical techniques used in breast cancer classification by the employment of four different machine learning algorithms. There were notable differences in classification findings, emphasizing the necessity of using adept analytical tools to improve the accuracy of breast cancer classification. Among individual algorithms, LGBM has the highest F1 score of 99.2% and a remarkable accuracy of 98.9%. Ensembles comprising AdaBoost, GBM, and RGF outperformed individual techniques with an astonishing 99.5% accuracy. The best ensemble algorithms prioritize features like worst texture, worst concave points, mean concave points, and mean texture, crucial for the classification. The examination of the advantages of ensemble learning methods, which combine predictions from many classifiers to improve classification performance, is at the heart of this the study. In particular, it is revealed how the k-fold and stratified shuffle split cross-validation methods differ in the classification results, providing clinicians a thorough understanding of the clinical ramifications to decipher the complex facets of breast cancer classification and identify crucial tumor traits that can distinguish malignant from benign cases.

Hybrid optimal feature selection-based iterative deep convolution learning for COVID-19 classification system

The COVID-19 pandemic has necessitated the development of innovative and efficient methods for early detection and diagnosis. Integrating Internet of Things (IoT) devices and applications in healthcare has facilitated various functions. This work aims to employ practical artificial intelligence (AI) approaches to extract meaningful information from the vast amount of IoT data to perform disease prediction tasks. However, traditional AI methods need help in feature analysis due to the complexity and scale of IoT data. So, this work implements the optimal iterative COVID-19 classification network (OICC-Net) using machine learning optimization and deep learning approaches. Initially, the preprocessing operation normalizes the dataset with uniform values. Here, random forest infused particle swarm-based black widow optimization (RFI-PS-BWO) algorithm was used to get the disease-specific patterns from SARS-CoV-2 (SC2), and other disease classes, where patterns of the SC2 virus are very similar to those of other virus classes. In addition, an iterative deep convolution learning (IDCL) feature selection method is used to distinguish features from the RFI-PS-BWO data. This iterative process enhances the performance of feature selection by providing improved representation and reducing the dimensionality of the input data. Then, a one-dimensional convolutional neural network (1D-CNN) was employed to classify and identify the extracted features from SC2 with no virus classes. The 1D-CNN model is trained using a large dataset of COVID-19 samples, enabling it to learn intricate patterns and make accurate predictions. It was tested and found that the proposed OICC-Net system is more accurate than current methods, with a score of 99.97 % for F1-score, 100 % for sensitivity, 100 % for specificity, 99.98 % for precision, and 99.99 % for recall.

Prospect of large language models and natural language processing for lung cancer diagnosis: A systematic review

AbstractLung cancer, a leading cause of global mortality, demands a combat for its effective prevention, early diagnosis, and advanced treatment methods. Traditional diagnostic methods face limitations in accuracy and efficiency, necessitating innovative solutions. Large Language Models (LLMs) and Natural Language Processing (NLP) offer promising avenues for overcoming these challenges by providing comprehensive insights into medical data and personalizing treatment plans. This systematic review explores the transformative potential of LLMs and NLP in automating lung cancer diagnosis. It evaluates their applications, particularly in medical imaging and the interpretation of complex medical data, and assesses achievements and associated challenges. Emphasizing the critical role of Artificial Intelligence (AI) in medical imaging, the review highlights advancements in lung cancer screening and deep learning approaches. Furthermore, it underscores the importance of on‐going advancements in diagnostic methods and encourages further exploration in this field.

PROGNOSTIC VALUE OF BIOMARKERS IN PATIENTS WITH ISCHEMIC HEART FAILURE

Modern clinical recommendations of medical associations attach great importance to the improvement of methods of early diagnosis, prevention and individualized treatment of heart failure (HF). It is assumed that biological markers that reflect different pathophysiological stages of HF retain their value as a powerful tool for diagnosing acute and chronic HF, stratifying patients into high-risk groups for the occurrence and progression of the specified pathology, and as a probable predictor of treatment effectiveness. Along with this, the levels and prognostic value of serum biomarkers in patients with HF of different etiologies at different stages of treatment remain unclear.The purpose. To study the prognostic value of biomarkers in patients with HF of ischemic origin.Material and methods. 398 were examined with HF aged 45-65 years (54.3±7.2 years. All 398 patients with HF were divided into two groups. The 1st group included 219 (55%) patients who did not have re-hospitalization (RG) due to HF decompensation during the year of observation. The II group consisted of 179 (45%) patients with PG. 226 (56.8%) patients had a permanent form of atrial fibrillation (AF), 102 (25.6%) had type 2 diabetes. Among the examined - 198 (49,7%) women and 200 (50,2%) men. A standardized echocardiographic study was performed to determine the left ventricular ejection fraction (LV EF) and LV dimensions during hospitalization. The levels of thyroid-stimulating hormone, free T3, free T4, blood glucose, glycated hemoglobin, galectin-3, ST-2, BNP, NT-proBNP were determined. The ratios: ST-2 / Galectin-3 (UO), NT-proBNP / BNP (UO), NT-proBNP / ST-2 (UO) and NT-proBNP / Galectin-3 (UO) were calculated. During 1 year, patients were observed, taking into account the presence of repeated hospitalization (RG) due to HF decompensation, mortality. Patients were divided into two groups. The 1st group included 219 (55%) of patients who did not have re-hospitalization (PG) due to HF decompensation. II group - 179 (45%) patients with PG.Results. Compared to patients with a favorable short-term course of the disease, patients with HF of ischemic genesis, who had PG during the year of observation, have HF III and IV PK according to NYHA, diabetes mellitus, and a permanent form of AF more often; higher heart rate, serum levels of galectin-3 (by 11.1%), NT-proBNP (by 31.8%), higher values of NT-proBNP / BNP (by 40.6%) and NT-proBNP / ST- 2 (by 21.1%), but a smaller value of the ST-2 / Galectin-3 ratio (by 9.3%), as well as a higher risk of death during the first year (OR = 3.81). The risk of PG increases when the level of NT-proBNP &gt; 652.6 pg/ml is reached and the ratios: ST-2/Galectin-3 ≤ 17.95 IU, NT-proBNP / BNP &gt; 10.17 IU and NT-proBNP / ST-2 &gt; 13.79 UO. The level of ST-2 &gt; 41.37 ng/ml is prognostically significant for the one-year death of patients within a year.Conclusions. In patients with HF of ischemic genesis, the ratio of NT-proBNP / BNP has a greater prognostic significance regarding the risk of re-hospitalization, compared to NT-proBNP and NT-proBNP / ST-2.

Hepatosis in broiler chickens: The role and efficacy of therapeutic and prophylactic agents containing hepatoprotectors

The preservation of poultry stock can be improved by incorporating therapeutic and preventive agents, including hepatoprotectors, into the diet. This study aimed to evaluate the effect of an experimental drug containing hepatoprotectors on the health of broiler chickens. The research sought to identify the potential of the preparation to enhance liver function and overall poultry stock survival. The study was conducted from 2023 to 2024 at Odesa State Agrarian University. Investigations were carried out to assess the effects of the experimental preparation in the following doses: 1.0 g/kg body weight (therapeutic dose), 2.0 g/kg body weight (double therapeutic dose), and 5.0 g/kg body weight (fivefold therapeutic dose) over 30 days. The experiment was conducted on 500 Cobb 500 broiler chickens aged 1 to 40 days. Additionally, biochemical blood parameters were measured and analysed using a Polish biochemical selective automatic analyser, the Hitachi 902. Studies have established that the optimal dose of the experimental drug for hepatosis in broiler chickens is 1.0 g/kg body weight. After administration of the drug, the serum bilirubin content decreased by 27.3%, lactate dehydrogenase activity decreased by 17.1%, aspartate aminotransferase activity decreased by 16.4%, alanine aminotransferase activity decreased by 19.3%, and vitamin A content increased by 25.6%, while the phagocytic activity of pseudoeosinophils increased by 23.1%. The results of the studies have proven that the experimental drug normalises liver function, positively affects the biochemical composition of blood, promotes growth, provides protection and natural resistance of the broiler organism, and even improves the quality of poultry products. Based on the above, it is necessary to improve methods for early diagnosis of liver function disorders in poultry and apply effective methods for correcting metabolic processes

Application of advanced biosensors in nervous system diseases

AbstractNervous system diseases are among the most common diseases globally, posing a severe threat to patients' quality of life and placing a considerable burden on families and society. With improvements in miniaturization, intelligence, and the safety of biosensors, the combination of machinery and organisms is becoming increasingly common. In neuroscience research, biosensors of different macroscopic dimensions have been uniquely utilized to harness their relevant properties. One‐dimensional (1D) biosensors can achieve in situ real‐time monitoring of neural markers at the subcellular, single‐cell, ex vivo, and in vivo levels, with reduced impacts on organisms. Two‐dimensional (2D) biosensors can monitor the chemical behavior of cells and the neural activity of living animals. They are helpful for objectively identifying the characteristics of cells in response to external stimuli and studying the neural circuits of living animals. Three‐dimensional (3D) biosensors have shown unique advantages in point‐of‐care testing, liquid biopsy, drug screening, and mechanistic research. In clinical practice, brain‐computer interfaces (BCIs) and wearable devices have become important tools for monitoring and treatment. To date, there has been widespread adoption of BCIs in clinical practice. BCIs not only exhibit good efficacy in severe neurological and mental diseases but also provide a method for early diagnosis and treatment of these diseases. Wearable sensor devices can accurately assess the symptoms of movement disorders and play an active role in rehabilitation and treatment. In this review, we summarize the application of advanced biosensors in neuroscience research and clinical practice. The challenges and prospects of biosensors as applied to nervous system diseases under interdisciplinary promotion are also discussed in depth.

Methods of Early Diagnosis and Treatment of Pathology of the Nose and Nasopharynx in Diseases of the Blood System

Methods of Early Diagnosis and Treatment of Pathology of the Nose and Nasopharynx in Diseases of the Blood System

Radiological segmentation of knee meniscus ultrasound images based on boundary constraints and multi-scale fusion network

Knee osteoarthritis (KOA) is one of the most popular joint diseases endangering human health because of its high incidence, disability, and younger onset. However, there is no suitable method for early diagnosis, evaluation, and treatment of KOA. In recent years, some clinical studies have found that ultrasound can detect early changes in KOA in advance, and the automatic segmentation of ultrasound images can achieve rapid and effective quantitative research on KOA. However, in ultrasound images, the soft tissue boundaries of the lesion are blurred, making lesion segmentation difficult. Although the U-Net family is one of the best networks for image segmentation, they still have defects such as blurred segmentation boundaries, distorted morphology, and insufficient accuracy when segmenting ultrasound images. To address this issue, we added attention, atrous spatial pyramid pooling (ASSP), and edge loss function terms into the Unet3+ network, which improved the contour clarity and accuracy of output images (the improved Unet3+ Dice acc = 78.74%. Then, we extract the key features of the improved Unet3+ for outputting meniscus images: calculating meniscus area and distance, where the average accuracy of the area is: area_ avg_ acc = 91.12%, with an average distance accuracy of distance_ avg_ acc = 91.14%. This thesis creates a new dataset collection from West China Hospital, Sichuan University, and automated measurement of knee meniscus protrusion area has been achieved for the first time. This article is the first to apply deep learning to ultrasound image segmentation of the knee meniscus, helping doctors conduct qualitative and quantitative analysis of the diagnosis and treatment of early KOA. The results indicate that the improved Unet3+ can assist doctors in automatically diagnosing and evaluating KOA based on meniscus ultrasound images, which is beneficial for guiding early clinical intervention.

Automatic Annotation Diagnostic Framework for Nasopharyngeal Carcinoma via Pathology-Fidelity GAN and Prior-Driven Classification.

Non-keratinizing carcinoma is the most common subtype of nasopharyngeal carcinoma (NPC). Its poorly differentiated tumor cells and complex microenvironment present challenges to pathological diagnosis. AI-based pathological models have demonstrated potential in diagnosing NPC, but the reliance on costly manual annotation hinders development. To address the challenges, this paper proposes a deep learning-based framework for diagnosing NPC without manual annotation. The framework includes a novel unpaired generative network and a prior-driven image classification system. With pathology-fidelity constraints, the generative network achieves accurate digital staining from H&E to EBER images. The classification system leverages staining specificity and pathological prior knowledge to annotate training data automatically and to classify images for NPC diagnosis. This work used 232 cases for study. The experimental results show that the classification system reached a 99.59% accuracy in classifying EBER images, which closely matched the diagnostic results of pathologists. Utilizing PF-GAN as the backbone of the framework, the system attained a specificity of 0.8826 in generating EBER images, markedly outperforming that of other GANs (0.6137, 0.5815). Furthermore, the F1-Score of the framework for patch level diagnosis was 0.9143, exceeding those of fully supervised models (0.9103, 0.8777). To further validate its clinical efficacy, the framework was compared with experienced pathologists at the WSI level, showing comparable NPC diagnosis performance. This low-cost and precise diagnostic framework optimizes the early pathological diagnosis method for NPC and provides an innovative strategic direction for AI-based cancer diagnosis.

Relationship between chemical composition and VOCs emission potential of similar paving grade road bitumen

ABSTRACT Exposure to bitumen fumes of workers during road pavement is a subject of interest in the field of industrial hygiene. Improvements have been made to technical means of prevention, but recent studies have shown that exposures need to be reduced. The development of early diagnosis method able to assess the volatile organic compounds (VOCs) emission potential would therefore be useful. The objective of this study was to outline a possible relationship between the chemical composition of bitumen and VOCs emission during heating using thirteen 35/50 paving-grade bitumen samples used in France. The samples are similar regarding their physical properties, elemental and SARA compositions. Molecular analysis investigations show that they contain the same class of compounds but in different proportions. Thermal analyses revealed different capacities of volatiles release potential. The combination of these data allowed to establish a relationship between chemical composition and volatile emission, leading to the identification of two groups of samples. We suggest three parameters based on molecular weight distribution, thermogravimetric profile and hydrocarbon release capacity that may distinguish bitumen by their emission potential prior to road pavement application. They may contribute to a methodology aiming to prevent workers exposure to bitumen fumes.

Plasma-based near-infrared spectroscopy for early diagnosis of lung cancer

Lung cancer (LC) continues to be a leading death cause in China, primarily due to late diagnosis. This study aimed to evaluate the effectiveness of using plasma-based near-infrared spectroscopy (NIRS) for LC early diagnosis. A total of 171 plasma samples were collected, including 73 healthy controls (HC), 73 LC, and 25 benign lung tumors (B). NIRS was utilized to measure the spectra of samples. Pre-processing methods, including centering and scaling, standard normal variate, multiplicative scatter correction, Savitzky-Golay smoothing, Savitzky-Golay first derivative, and baseline correction were applied. Subsequently, 4 machine learning (ML) algorithms, including partial least squares (PLS), support vector machines (SVM), gradient boosting machine, and random forest, were utilized to develop diagnostic models using train set data. Then, the predictive performance of each model was evaluated using test set samples. The study was conducted in 5 comparisons as follows: LC and HC, LC and B, B and HC, the diseased group (D) and HC, as well as LC, B and HC. Among the 5 comparisons, SVM consistently generated the best performance with a certain pre-processing method, achieving overall accuracy of 1.0 (kappa: 1.0) in the comparisons of LC and HC, B and HC, as well as D and HC. Pre-processing was identified as a crucial step in developing ML models. Interestingly, PLS demonstrated remarkable stability and relatively high predictive performance across the 5 comparisons, even though it did not achieve the top results like SVM. However, none of these algorithms were able to effectively distinguish B from LC. These findings indicate that the combination of plasma-based NIRS with ML algorithms is a rapid, non-invasive, effective, and economical method for LC early diagnosis.

Primary Cutaneous Diffuse Large B‑Cell Lymphoma, Leg Type: A Clinical Case

Introduction. Primary Cutaneous Diffuse Large B-Cell Lymphoma, Leg Type it is a rare variant of skin lymphoma that has an aggressive course and a high risk of extracutaneous manifestations.The purpose of this work is to familiarize a wide range of specialists with the clinical manifestations of a rare disease — primary cutaneous diffuse large B-cell lymphoma.Materials and methods. An interesting clinical case is presented and the medical history of a patient with B-cell lymphoma from the skin is analyzed.Results. A differential diagnosis of the disease was made. Based on the study of histological material, the final clinical diagnosis was established and further treatment tactics were determined.Discussion. The patient had spots and plaques on the lower extremities, the sole visible signs of the disease. These symptoms may suggest a positive prognosis and a gradual progression. Diagnostic measures should include gathering medical history, assessing the local condition, and carrying out an excision biopsy early in the examination process for an accurate and prompt diagnosisConclusion. Due to the rarity of this subtype of lymphoma and the limited number of domestic publications on the topic, a more detailed study and description of individual clinical cases can lead to a better understanding of the characteristics of the symptoms and the processes of pathological changes in cells, tissues, and organs. This could potentially lead to the development of more efficient methods for early diagnosis and timely treatment of the condition.

Fluorescent DNA tetrahedral probe with catalytic hairpin self-assembly reaction for imaging of miR-21 and miR-155 in living cells.

ACHA-based fluorescent DNA tetrahedral probe (FDTp) has been designedto detect the microRNAsmiR-21 and miR-155 sensitively and specifically in living cells. The design consisted of functional elements (H1, H2, and Protector) connected to a DNA tetrahedron modified with two pairs of fluorophores and quenching groups. In the presence of miR-21, the chain displacement effect was triggered and Cy3 fluorescence was emitted. In the presence of miR-155, the signal of the catalytic hairpin assembly (CHA) between H1 and H2 on FDTp was amplified, making the fluorescence of FAM sensitive to miR-155. Using this method, the detection limit for miR-155 was 5pM. The FDTp successfully imaged miR-21 and miR-155 in living cells and distinguished a variety of cell lines based on their expression levels of miR-21 and miR-155. The detection and imaging of dual targets in this design ensured the accuracy of tumor diagnosis and provided a new method for early tumor diagnosis.

Course of Niemann – Pick disease type A/B in the context of hematopoietic stem cell transplantation

Niemann – Pick disease type A/B (NPAB) is a rare severe inherited disease from the group of accumulation diseases with a defect in the acid sphingomyelinase gene (Niemann-Pick disease types B, A/B) (ASMD). Symptoms of damage to the nervous system and internal organs manifest in infancy, leading to disability, fatalities in childhood. NPAB is so far incurable. Optimal management of the disease requires a multidisciplinary team of physicians, specialists. The basis of therapy is the elimination of existing/forming complications, symptomatic treatment. Enzyme replacement therapy as a means of modifying the course of this disease is expected to slow down the progression of pathologic manifestations of the disease not related to the central nervous system lesions. Single cases of hematopoietic stem cell transplantation (HSCT) have been described in the treatment of ASMD, which is one of the new methods aimed at normalizing the level of acid sphingomyelinase, blood parameters, as well as reducing the severity of pathological visceral manifestations. However, the development of complications during HSCT, absence of positive therapeutic effect in severe CNS lesions does not allow to widely implement this method. Taking into account the contradictory data on the efficacy of HSCT in ASMD, further clinical studies are required. Analysis of 2 clinical cases of NPAB in children from the same family allowed us to reveal differences in the course and outcomes of the disease at verification of the diagnosis at birth followed by HSCT. Difficulties in diagnosing this extremely rare pathology, which requires a multidisciplinary approach, justify the need to improve methods of early diagnosis, including the organization of genetic risk determination, introduction of prenatal genetic testing before pregnancy.

Method of Diagnosing Sarcopenia in Morbidly Obese Patients

Introduction. Sarcopenia refers to a clinical syndrome characterized by progressive skeletal muscle disease, loss of muscle mass and strength resulting in impaired physical performance. The prevalence of sarcopenia in the population ranges from 5% to 13% in people aged 43.0 ± 11.1 years and reaches 50% in those over 70. Various methods have been used to diagnose sarcopenia, which, however, have their limitations. Currently, CT scanning is considered the most accurate method for the detection of sarcopenia. Therefore, the development of new methods for early diagnosis of sarcopenia appears relevant. Aim. To investigate different methods of diagnosing sarcopenia in morbidly obese patients. Materials and methods. The study presents an analysis of various methods for diagnostics of sarcopenia in morbidly obese patients, including a patented method based on the research conducted in the BSMU Clinic (Utility Patent No. 2815157 dated 11.03.2024). Results and discussion. Doctors used the suggested method of diagnosis, defined the value of sarcopenia index equal or less than 15, and diagnosed sarcopenia. Conclusion. The prevalence and severity of sarcopenia vary depending on the classification criteria and thresholds used. Since a unified diagnostic method is yet to be developed, it is necessary to continue research into this issue, and introduce sarcopenia detection, prevention, and correction techniques into widespread clinical practice. Indices of sarcopenia and obesity, obtained during an additional preoperative study, provide significant prognostic information superior to body mass index, thereby enabling the direction and scope of surgical interventions to be determined and survival outcomes to be improved.

Novel dynamic light scattering immunosensor for prostate specific antigens based upon dual-tyramine signal amplification strategy

The sensitive detection of biomarkers is crucial for the early diagnosis, treatment, and improvement of the quality of life of patients. A novel dynamic light scattering-dual tyramine signal amplification strategy (DLS-dTSA) was constructed to detect prostate-specific antigen (PSA). The Oligo 1 was first immobilized in a 96-well plate for PSA capture. Afterwards, horseradish peroxidase (HRP) and Oligo 2 functionalized AuNPs (HRP-Au NPs-Oligo 2) bound to Oligo 1 through DNA hybridization. In the presence of hydrogen peroxide (H2O2), tyramine could be converted into a transient radical intermediate by HRP, leading to the aggregation of tyramine and HRP-labeled AuNPs (tyramine-AuNPs-HRP) near HRP-AuNPs-Oligo 2, triggering the first TSA process. Later, the PSA was added into the 96-well plate, to specifically recognize aptamer and thus depart the Au-aggregate from the 96-well plate. Then H2O2 was added to the Au-aggregate solution, triggering the free Au-aggregate to gather again, realizing the second TSA amplification. The average diameter of Au NPs increased linearly with the concentration of PSA during the range of 0.0001 – 1000 ng mL−1, and the limit of detection was 0.033 pg mL−1. Moreover, this strategy has been used to analyze PSA in human serum samples successfully, demonstrating the potential application of the proposed method for rapid clinical and early clinical diagnosis of prostate cancer in vitro.

Problems of oxaliplatin-induced neuropathy in patients with colorectal cancer

Aim. To investigate the methods of early diagnosis of oxaliplatin-induced peripheral neuropathy (PN).Materials and methods. The study included 52 patients with diagnosis of metastatic colorectal cancer receiving 1st line polychemotherapy per the mFOLFOX6 scheme ± targeted therapy who prior to each cycle filled out the European Organization for Research and Treatment of Cancer Quality of Life Questionnaire Chemotherapy-Induced Peripheral Neuropathy (EORTC QLQ CIPN 20) and were subject to neurological examination using the Neuropathy Disability Scale (NDS).Results. The analysis included 28 patients who completed polychemotherapy: 16 (57,2 %) men and 12 (42,8 %) women, mean age 60.0 ± 10.0 years. Fridman’s two-way analysis of variance by ranks showed significant changes in the scores (sensory, motor, autonomic) of the EORTC QLQ CIPN 20 questionnaire with the increase in the number of chemotherapy cycles (р = 0.001). Significant increase in the sensory and motor scores was observed after the 3rd cycle, in the autonomic – after the 6th cycle. Significant increase in NDS score was observed starting at the 3rd cycle, and vibration sensitivity on the medial ankle or at the base of the 1st toe significantly decreased after the 1stcycle. Median number of cycles at which oxaliplatin dose was reduced or the drug was discontinued due to clinically significant symptoms of PN irrespective of questionnaire results and neurological examination was 6.Conclusion. According to the results of the study, the following conclusions were made: 1) sensory scale of the 3-part EORTC QLQ CIPN 20 questionnaire (9 questions of 20) or motor scale (7 questions of 20), vibration sensitivity can be considered as stand-alone informative tests; 2) in a large patient cohort, NDS score increase to 5, EORTC QLQ CIPN 20 sensory scale score increase to 12 and higher or decreased vibration sensitivity on the medial ankle or at the base of the 1st toe to 4.5 arbitrary units should be considered early manifestations of PN; 3) for a routine algorithm, it is acceptable to consider a combination of 9 questions of the sensory or 7 questions of the motor scales of the EORTC QLQ CIPN 20 questionnaire and evaluation of vibration sensitivity on the medial ankle or at the base of the 1st toe to decide on oxaliplatin dose reduction or measures for prevention of peripheral neuropathy symptom worsening.