Method For Diagnosis Research Articles

Diffuse large B-cell lymphoma in the uterus with unexpected manifestations: a case report

BackgroundLymphoid neoplasm is a common disease, arising from lymphoid cells. It is divided into Hodgkin lymphoma and non-Hodgkin lymphoma. Non-Hodgkin lymphoma can be intranodular or extranodular, which happens in 25% of primary cases. The most common locations of extranodular non-Hodgkin lymphoma are the skin and gastrointestinal tract. The genital tract is a rare location; most lymphomas arise from the cervix and vagina, while the uterine corpus is an extremely rare location. In our case, the patient was diagnosed with primary extranodular non-Hodgkin lymphoma in different locations of her genital tract.Case presentationA 48-year-old nonparous Syrian woman complained of diffuse abdominal pain, fatigue, debility, high fever, vomiting, and urinary retention for a week. The last menstrual period of the patient was 5 years previously. The physical examination showed periodic abdominal pain with severe fatigue and increased abdominal size. The laboratory investigations were within normal limits except for a low level of hemoglobin and a high level of cancer antigen 125. The radiological investigations showed a uterine sizable lobulated mass with irregular borders and high and heterogeneous density, extending to the right and left ovaries, enlargement lymph nodes around the abdominal aortic and right iliac vessels, and severe right pleural effusion with right inferior lobe atelectasis. A total hysterectomy and oophorectomy were done. The histopathological examination showed that the patient had non-Hodgkin lymphoma (primary tumor).ConclusionPrimary non-Hodgkin lymphoma in the female genital tract is an extremely rare disease. Fast diagnosis and treatment can improve the outcomes, so this differential diagnosis should be in our minds even in the absence of systematic manifestations of lymphoma. More studies are needed to explain the pathology of this disease and to put guidelines that determine the perfect methods for diagnosis and treatment.

Intelligent Diagnosis Method for Typical Co-frequency Vibration Faults of Rotating Machinery Based on SAE and Ensembled ResNet-SVM

Intelligent fault diagnosis is an important method in rotating machinery fault diagnosis and equipment health management. To deal with co-frequency vibration faults, a type of typical fault in rotating machinery, this paper proposes a fault diagnosis method based on the stacked autoencoder (SAE) and ensembled ResNet-SVM. Furthermore, the time- and frequency-domain features of several co-frequency vibration faults are summarized based on the mechanism analysis and calculated using actual vibration data. To realize and validate the high-precision diagnosis method of rotating equipment with co-frequency faults proposed in this study, the following three criteria are required: First, to improve the effectiveness and robustness of the ensembled model and the sliding window using data augmentation, adding noise, autoencoder (AE) and SAE methods are analyzed in terms of principle and practical effects. Second, ResNet is used as the feature extractor for the ensembled ResNet-SVM model. Feature extraction is carried out twice, and the extracted co-frequency fault features are more comprehensive. Finally, the data augmentation method and ensemble ResNet-SVM are combined for fault diagnosis and compared with other methods. The experimental results show that the accuracy of the proposed method can exceed 99.9%.

Lightweight bearing fault diagnosis method based on cross-scale learning transformer under imbalanced data*

Abstract Due to the limited amount of failure data in rolling bearing faults, traditional fault diagnosis models encounter challenges such as low diagnostic accuracy and efficiency when dealing with imbalanced data. Additionally, many fault diagnosis models are overly complex and demand high computational resources. To address these issues, a lightweight bearing fault diagnosis method based on cross-scale learnable transformer (CSLT) is proposed for imbalanced data. For difficult-to-classify samples, a learnable generalized focal loss function is defined. The learnable parameters are employed to increase its flexibility, it better addresses the issue of bearing fault diagnosis under imbalanced data conditions. Then, a multi-head broadcasted self-attention mechanism is designed by capturing critical local features of the signal through one-dimensional convolution operations, which not only improves feature extraction capability but also reduces computational complexity. Finally, a dynamic label prediction pruning module is developed to trim redundant labels, which helps in lightening the model and enhancing both feature extraction and diagnostic efficiency. The experimental results demonstrate that the proposed diagnosis method exhibits superior diagnostic precision and efficiency by comparing with other methods.

Choriocarcinoma of the ovaries: the role of molecular-genetic testing in the differential diagnosis of gestational and non-gestational forms

The treatment approach for gestational and non-gestational ovarian choriocarcinoma has several differences, and their differential diagnosis requires special attention. The implementation of molecular-genetic testing which determines the presence of paternal genetic material in the tumor allows for a reliable determination of the origin of ovarian choriocarcinoma. The presented clinical case demonstrates the importance of this method in the differential diagnosis of gestational and non-gestational forms of ovarian choriocarcinoma.

Unsupervised method for detecting surface defects in steel based on joint optimization of pseudo-labeling and clustering

Abstract Advances in the field of measurement science and technology have improved the detection of defects in industrial production. One of the key challenges in steel plate surface defect detection is the need to quickly detect a small number of defects in an overwhelmingly defect-free sample. Unlike supervised learning, which relies heavily on precise sample labeling, unsupervised learning leverages its inherent learning capabilities for detection. This paper introduces an innovative method for smart steel diagnosis, integrating joint optimization of feature extraction and clustering. The proposed approach merges mini-batch K-Means clustering with a feature extraction network to acquire pseudo-label information for current images. It employs a multi-view transformation strategy, enabling classification through the optimized feedback from pseudo-labels. This method allows the network to self-optimize the distinction of image features through backpropagation. The method exhibits a mere 4% classification failure rate for steel surface images. This significant reduction in additional data processing requirements enhances the inspection system's efficiency and accuracy. Furthermore, the versatility of this method extends beyond steel defect diagnosis. It holds potential for application in various engineering domains, particularly in scenarios characterized by data imbalance.

Application of metagenomic next-generation sequencing in the etiological diagnosis of refractory pneumonia in children

ObjectiveMetagenomic next-generation sequencing (mNGS) was used to analyze the etiological distribution of refractory pneumonia in children. We compared its efficacy in pathogen diagnosis against traditional methods to provide a basis for clinical adjustment and treatment.MethodsA total of 60 children with refractory pneumonia treated at the Department of Respiratory Medicine, Children’s Hospital Affiliated with the Capital Institute of Paediatrics, from September 2019 to December 2021 were enrolled in this study. Clinical data (including sex, age, laboratory tests, complications, and discharge diagnosis) and lower respiratory tract specimens were collected, including bronchoalveolar lavage fluid (BALF), deep sputum, pleural effusion, lung abscess puncture fluid, traditional respiratory pathogens (culture, acid-fast staining, polymerase chain reaction, serological testing, etc.), and mNGS detection methods were used to determine the distribution of pathogens in children with refractory pneumonia and to compare the positive rate and diagnostic efficiency of mNGS and traditional pathogen detection for different types of pathogens.ResultsAmong the 60 children with refractory pneumonia, 43 specimens were positive by mNGS, and 67 strains of pathogens were detected, including 20.90% (14 strains) of which were Mycoplasma pneumoniae, 11.94% (8 strains) were Streptococcus pneumoniae, 7.46% (5 strains) were cytomegalovirus, and 5.97% (4 strains) were Candida albicans. Thirty-nine strains of Mycoplasma pneumoniae (41.03%, 16 strains), Streptococcus pneumoniae (10.26%, 4 strains), Candida albicans (7.69%, 3 strains), and Aspergillus (5.13%, 2 strains) were detected using traditional methods. The positive rate of mNGS detection was 90.48%, and the positive rate of the traditional method was 61.90% (p = 0.050), especially for G+ bacteria. The positive rate of mNGS was greater than that of traditional methods (p < 0.05), but they had no significant difference in detecting G- bacteria, viruses, fungi, or Mycoplasma/Chlamydia. Among the 60 patients, 21 had mixed infections, 25 had single infections, and the other 14 had unknown pathogens. Mycoplasma pneumoniae was most common in both mixed infections and single infections. The sensitivity, specificity, positive predictive value, and negative predictive value of mNGS were 95.45, 37.50, 80.77, and 75.00%, respectively. The sensitivity, specificity, positive predictive value, and negative predictive value of the traditional methods were 72.72, 62.50, 84.21, and 45.45%, respectively. The clinical compliance of mNGS was 80.00%, and that of the traditional method was 70.00%. The sensitivity and negative predictive value of mNGS were high, and the difference in the sensitivity for detecting G+ bacteria was statistically significant (p < 0.05). However, the differences in G- bacteria, fungi, and Mycoplasma/Chlamydia were not statistically significant (p > 0.05). Due to the small sample size, statistical analysis could not be conducted on viral infections.ConclusionmNGS has higher overall efficacy than traditional methods for the etiological diagnosis of refractory pneumonia in children. The application of mNGS can significantly improve the detection rate of pathogens in children with refractory pneumonia. The sensitivity and negative predictive value of mNGS for detecting G+ bacteria are greater than those of other methods, and it can exclude the original suspected pathogenic bacteria. Unnecessary antibiotic use was reduced, but there was no statistically significant difference in G- bacteria, fungi, or Mycoplasma/Chlamydia.

Fault Diagnosis Method of Bearings Based on SCSSA-VMD-MCKD

To tackle the issue of detecting early, subtle faults in rolling bearings in the presence of noise interference, the SCSSA-VMD-MCKD method is suggested. This method optimizes the Variational Mode Decomposition (VMD) and Maximum Correlated Kurtosis Deconvolution (MCKD) by integrating the sine-cosine and Cauchy Mutation Sparrow Search Algorithm (SCSSA). The approach aims to effectively diagnose faults in rolling bearings by leveraging the strengths of VMD and MCKD in noise reduction and highlighting fault frequencies. The method utilizes the SCSSA algorithm to autonomously search for parameters in both VMD and MCKD, using the EnvelopeCrest Factor Ec as a fitness function. Firstly, SCSSA is employed to optimize the decomposition mode number K and penalty factor α in the VMD algorithm. Secondly, the parameters in the MCKD algorithm are optimized, and MCKD is used for deconvolution to enhance the impulsive characteristics of the best modal component. Finally, the signal is further analyzed after deconvolution. The results demonstrate that this algorithm can effectively identify subtle fault signals in bearing signals and diagnose fault frequencies in noisy environments. The accuracy of fault diagnosis can reach nearly 99%.

Decoding depression: a comprehensive multi-cohort exploration of blood DNA methylation using machine learning and deep learning approaches

The causes of depression are complex, and the current diagnosis methods rely solely on psychiatric evaluations with no incorporation of laboratory biomarkers in clinical practices. We investigated the stability of blood DNA methylation depression signatures in six different populations using six public and two domestic cohorts (n = 1942) conducting mega-analysis and meta-analysis of the individual studies. We evaluated 12 machine learning and deep learning strategies for depression classification both in cross-validation (CV) and in hold-out tests using merged data from 8 separate batches, constructing models with both biased and unbiased feature selection. We found 1987 CpG sites related to depression in both mega- and meta-analysis at the nominal level, and the associated genes were nominally related to axon guidance and immune pathways based on enrichment analysis and eQTM data. Random forest classifiers achieved the highest performance (AUC 0.73 and 0.76) in CV and hold-out tests respectively on the batch-level processed data. In contrast, the methylation showed low predictive power (all AUCs < 0.57) for all classifiers in CV and no predictive power in hold-out tests when used with harmonized data. All models achieved significantly better performance (>14% gain in AUCs) with pre-selected features (selection bias), with some of the models (joint autoencoder-classifier) reaching AUCs of up to 0.91 in the final testing regardless of data preparation. Different algorithmic feature selection approaches may outperform limma, however, random forest models perform well regardless of the strategy. The results provide an overview over potential future biomarkers for depression and highlight many important methodological aspects for DNA methylation-based depression profiling including the use of machine learning strategies.

A stable and robust fault diagnosis method for bearing using lightweight batch normalization-free residual network

Abstract The conventional deep learning-based bearing fault diagnosis methods tend to utilize denoising modules to improve the fault diagnosis performance in noisy scenes. However, the addition of denoising modules will increase expensive computational costs, leading to a delayed acquisition of fault diagnosis results. This work proposed a lightweight batch normalization (BN)-free residual network without any denoising modules for bearing fault diagnosis which properly rescaled the weights in a standard initialization instead of BN to avoid the exploding gradient problem and vanishing gradient problem at the beginning of training for deep neural networks. Therefore, it prevents the undesirable properties caused by BN. Compared with other methods, the fault diagnosis performance of the proposed method can maintain a high level with different input sizes and batch sizes. Especially in noisy scenes, the testing accuracy of fault diagnosis on different bearing datasets can be improved by 13.54% and 7.74% using fewer parameters and floating point operations on different bearing datasets.

Blood-Derived Extracellular Vesicles as a Promising Liquid Biopsy Diagnostic Tool for Early Cancer Detection

Cancer poses a significant public health challenge worldwide, and timely screening has the potential to mitigate cancer progression and reduce mortality rates. Currently, early identification of most tumors relies on imaging techniques and tissue biopsies. However, the use of low-cost, highly sensitive, non-invasive detection methods for early cancer screening has become more attractive. Extracellular Vesicles (EVs) released by all living cells contain distinctive biological components, such as nucleic acids, proteins, and lipids. These vesicles play crucial roles in the tumor microenvironment and intercellular communication during tumor progression, rendering liquid biopsy a particularly suitable method for diagnosis. Nevertheless, challenges related to purification methods and validation of efficacy currently hinder its widespread clinical implementation. These limitations underscore the importance of refining isolation techniques and conducting comprehensive investigations on EVs. This study seeks to evaluate the potential of liquid biopsy utilizing blood-derived EVs as a practical, cost-effective, and secure approach for early cancer detection.

Spatial-temporal graph feature learning driven by time–frequency similarity assessment for robust fault diagnosis of rotating machinery

Noises widely exist in the actual working environment of rotating machinery, which makes it difficult to extract high-quality fault features. Recently, the superiority of graph neural network (GNN) in the aspect of fault feature representation has been confirmed. By converting one-dimensional signals into graph structural data, hidden structural information inside the signals can be revealed. In this study, a novel graph-level fault diagnosis method based on spatial–temporal graph feature learning is developed. It mainly contains the following steps: First, Short-Time Fourier Transform (STFT) is used to construct time–frequency energy heatmaps, which can reflect the distribution of frequencies over time windows and the temporal variation characteristics. Second, a time–frequency similarity assessment method based on dynamic frequency warping is presented. On this basis, a time–frequency similarity graph (TFSG) is constructed to describe the frequency relationship over different time windows, which can bring extra structural information for the domain of time–frequency to support the subsequent classification task. Then, with the input of TFSG, a graph-level fault diagnosis model based on an improved GraphSAGE network (IGN) is developed. Finally, the fault labels for all TFSGs are recognized with a SoftMax classifier. Two experimental cases based on axial flow pumps and a public gearbox dataset verified the effectiveness of the proposed fault diagnosis method.

Patient experiences with blood pressure measurement methods for hypertension diagnosis: Qualitative findings from the BP-CHECK study.

Out-of-office blood pressure (BP) measurement is recommended when making a new hypertension diagnosis. In practice, however, hypertension is primarily diagnosed using clinic BP. The study objective was to understand patient attitudes about accuracy and patient-centeredness regarding hypertension diagnostic methods. Qualitative study within a randomized controlled diagnostic study conducted between May 2017 and March 2019 comparing the accuracy and acceptability of BP measurement methods among patients in an integrated healthcare delivery system. All participants completed 24-hour ambulatory blood pressure monitoring (ABPM), plus either clinic BP, home BP monitoring (HBPM), or kiosk BP diagnostic testing. Qualitative interviewees (aged 31-76 years, n=35) were recruited from the main study. Participants who completed HBPM found it to be comfortable and low burden, and believed it produced accurate results. Participants in the clinic arm described clinic measurements as inconvenient. Participants in the kiosk arm overall did not favor kiosks due to concerns about accuracy and privacy. Participants described ABPM as the most accurate method due to repeated measurements over the 24-hour period in real-world contexts, but many found it uncomfortable and disruptive. Participants also noted methods that involved repeated measures such as HBPM and ABPM particularly influenced their understanding of whether or not they had hypertension. Hypertension diagnostic methods that include more BP measurements help patients gain a deeper understanding of BP variability and the lower reliability of infrequent measurements in clinic. These findings warrant implementing strategies to enhance out-office BP diagnostic testing in primary care.

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.

Signal enhancement method for gearboxes fault diagnosis in robotic flexible joint

Abstract Motor Current Signal Analysis (MCSA) provides a non-intrusive approach to fault diagnosis. However, the fault impact reacting in the current is reduced due to the presence of flexible structures in the transmission path from the fault source to the motor. Therefore, this paper proposes a method to enhance the frequency domain of the current signal of a single mechanical fault through a transfer model between motor torque and link vibration. First, the joint system dynamics model was developed based on a three-inertia simplified model. The transfer model of motor torque and link vibration was defined based on the system dynamics. The link vibration is then estimated based on the transfer model and electromagnetic torque. Link vibration signal is considered as an enhancement of the torque signal. Finally, the link vibration signature analysis is performed instead of MCSA. The experimental results show that the method is effective in enhancing the features of individual mechanical faults and improving the fault diagnosis performance.

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.

Rolling bearing fault diagnosis method based on PE-DCM and ViT

Abstract Considering the issue of capturing the local and global contextual information and enhancing the parallel capability of bearing fault diagnosis in variable load and noise environments, a fault diagnosis method of rolling bearing based on PE-DCM and ViT is proposed. Firstly, the one-dimensional vibration signal is converted into a two-dimensional time-frequency diagram by continuous wavelet transform in the data processing module, and the model can understand the characteristics of the vibration signal more comprehensively. Secondly, a pyramid exponential expansion convolution module is established to extract the local features of fault information. Then, the global features of the fault information are learnt through the ViT (Vision Transformer) network, and the adaptive multi-attention is used to dynamically adjust the attention weights according to the features of the input data so as to inhibit noise or unimportant information. Finally, the experimental verification is carried out by using Case Western Reserve University and self-made MFS-bearing data set. The experimental results show that the method can better reflect the powerful image classification ability of the ViT network and has better noise resistance and generalization compared with other fault diagnosis methods.&amp;#xD;

A Method for Ocular Disease Diagnosis through Visual Prediction Explainability

Ocular diseases can range in severity, with some being more serious than others. As a matter of fact, there are several common and severe eye diseases, for instance, glaucoma, i.e., a group of eye conditions that damage the optic nerve, often associated with elevated intraocular pressure. Effective management and prevention strategies require a multifaceted approach, involving healthcare providers, public health officials and community education. Regular screenings and early interventions are crucial in reducing the impact of eye diseases on individuals and populations. In this paper, we propose a method aimed to detect the presence of ocular disease from the automatic analysis of eye fundus photographs. We consider deep learning; in detail, we adopt several convolutional neural networks aimed to train several models to be able to discriminate between different eye diseases. Furthermore, to boost the application of deep learning in real-world everyday medical practice, we adopt a method to understand which areas of the images are of interest from the model’s point of view; this allows us to detect disease by providing in this way disease localization by explainability. In the experimental analysis, we provide a set of four different experiments: in the first one, we propose a model to discern between age-related macular degenerations and normal fundus (obtaining an accuracy of 0.91); in the second one, the model is able to discriminate between cataract and normal fundus (obtaining an accuracy of 0.92); the third experiment is related to a model aimed to discriminate between glaucoma and normal ocular fundus (obtaining an accuracy of 0.88); and the last experiment is related to a model aimed to discern between pathological myopia and normal ocular fundus (obtaining an accuracy of 0.95). Thus, the experimental analysis confirms the effectiveness of the proposed method from a quantitative point of view (analysis aimed at understanding whether the model is able to correctly identify the disease) but also from a qualitative one, with a detailed and reasoned analysis aimed at understanding whether the model is able to correctly localize the disease.

Obtaining and Characterization of Experimental Diagnostic Brucellosis Rabbit Sera for the Development of a National Standard for Brucellosis Serum

Relevance. The production of brucellosis diagnosticums is currently being established in the medical and veterinary practice of the Republic of Uzbekistan. However, due to the lack of a national standard serum in the country and the high cost of analog standard serums on the market, problems may arise in the production of diagnostic tools. In addition, the lack of national standard serums causes problems in quality control of commercial and local antigens, hinders the development of local drugs – brucellosis diagnostics, which reduces the effectiveness of serological diagnosis of brucellosis in humans and animals. Given the above, it becomes necessary to develop and produce a standard serum with a high level of safety, which will improve the methods of laboratory diagnosis of various forms of brucellosis in patients and animals to identify hidden foci of infection in Uzbekistan. The aim of the study is to obtain and characterize experimental standard diagnostic brucellosis rabbit sera for the development of national and industry standard serum with appropriate titers. Materials and Methods. The experiments were carried out in full compliance with the European Council Directive 2010/63/EU on compliance with ethical principles in working with laboratory animals and the experiments were carried out on the basis of the methodological manual «Methods and rules of work» approved by the Ministry of Health of the Republic of Uzbekistan in 2016 with laboratory animals in experimental microbiological and immunological studies. To obtain positive standard sera against brucellosis pathogens, 12 rabbits weighing from 2.2 kg to 4.6 kg, aged 6 to 12 months, were used. The animals were kept under identical conditions on a standard vivarium diet. For the experiment, animals adapted to the conditions of the study were selected and kept in quarantine for 21 days. The maintenance and conduct of the studies followed international rules for the humane treatment of animals. Results and Discussion. During the evaluation of the heterologic specificity of the obtained sera for 23 collection and hospital strains, a weakly positive response (+) was observed in 16.7% of cases. Rabbit sera positive for heterologic specificity were adsorbed on suspensions of inactivated microorganisms (Citrobacter spp. and Enterobacteriaceae spp.) - no significant drop in titer was observed, and cross-agglutination was eliminated. A significant increase in the total protein level was noted after the first and fourth immunizations. Before the first immunization, the total protein level was 59.57 g/l, and after the fourth immunization, it increased threefold to 157.17 g/l. There was also an increase in albumin and globulin (33.2 g/L and 26.37 g/L, respectively) to 79.37 g/L and 77.43 g/L, respectively. IgM levels remained almost unchanged, increasing 3.5-fold after the second immunization and decreasing to 0.24 mg/ml after the fourth immunization. An increase in the level of IgG after the second and third immunizations was revealed, followed by a decrease after the fourth immunization. Conclusion. The obtained national and industry standard diagnostic polyvalent brucellosis sera, as well as the developed methods of laboratory control of its quality and efficiency testing will make it possible to produce candidate standard serum samples for agglutination reaction test and detection of Brucella strains. The obtained standard serum samples will be further used for control of the produced immunobiological diagnostic preparations and provision of unified requirements to their quality. The goal was achieved – improving the serological diagnosis of brucellosis, laying the foundation for further study and standardization of serum.

Expert consensus on the terminology, diagnostics and management of persisting symptoms after concussion with a focus on mental health, postural stability, electroencephalogram and balance testing: A cross-sectional Delphi-like survey

Background: Persisting symptoms after concussion (PSaC) are a pathological manifestation of head injuries that present with symptoms after the acute phase of head trauma has subsided. Insufficient research about PSaC has led to gaps in knowledge and incorrect terminology being applied. Furthermore, gaps exist in standardised assessment protocols and understanding of mental health symptoms associated with sports. Objectives: The study aimed to; 1) Determine expert consensus on appropriate terminology for symptoms lasting &gt;4 weeks, 2) Investigate associations with mental health and postural stability symptoms, 3) Evaluate experts’ views on quantitative balance and electroencephalogram (EEG) testing. Methods: A Delphi-like survey was designed in REDCap and sent to identified experts in the field of sports-related concussions (SRC). Expert consensus was defined as ≥ 75% agreement. Results: Expert consensus identified the following mood and motor control symptoms being associated with PSaC: increases in emotional state (80%), irritability (87%), nervousness (87%), sadness (80%), balance impairment (80%), dizziness (87%) and feeling slow (80%). Numbness and tingling were not considered longer-term effects (80%). Additionally, 93% of respondents acknowledged mental health symptoms as potential longer-term effects, with 80% agreeing on inadequate current management. Respondents indicated PSaC are only somewhat adequately managed (73%) or not managed well enough (27%). The use of EEG and quantitative balance testing remains open for debate. The survey response rate was 21%. Conclusion: Improving mental health management for athletes with PSaC and standardising terminology is crucial. Future research is required to establish effective diagnosis and treatment methods. Addressing these issues may result in better care and safer return to play for athletes.

Nutritional Diagnosis of Potato Crops Using the Multivariate Method

The compositional nutrient diagnosis (CND) method considers the multiple relationships among nutrients and has been proposed to evaluate the nutritional status of plants in place of the univariate and bivariate methods. As it is mathematically based and considers the interactions among all nutrients at the same time, it avoids the errors and trends observed in the calculations of other methods estimating nutritional status, enabling a greater relationship with productivity. The objective of this study was to obtain the CND norms for high-yielding populations of potato crops. For this, 587 samples were used from 21 experimental areas in the state of São Paulo, Brazil to correlate the leaf nutrient contents and the yields of potato crops. Crops with yields higher than 48,993.24 kg ha−1 were considered to have high yields, and the Mahalanobis distance separated the balanced samples from the nutritionally unbalanced ones. Thus, the CND-ilr method generated the norms and classified the 587 samples as nutritionally balanced with a high yield (5% of the total), nutritionally unbalanced with a low yield (92%), nutritionally unbalanced with a high yield (0.3%), or nutritionally balanced with a low yield (2.7%), with accuracy, sensitivity, specificity, NPV, and PPV scores of 96.9, 97.1, 93.6, 64.4, and 99.6%, respectively.