Development Of Diagnostic Algorithms Research Articles

Heparin-induced thrombocytopenia in extracorporeal membrane oxygenation-supported patients: a systematic review and meta-analysis

BackgroundIn recent years, extracorporeal membrane oxygenation (ECMO) has been increasingly used in critically ill patients with respiratory or cardiac failure. Heparin is usually used as anticoagulation therapy during ECMO support. However, heparin-induced thrombocytopenia (HIT) in ECMO-supported patients, which results in considerable morbidity and mortality, has not yet been well described. This meta-analysis and systematic review aimed to thoroughly report the incidence of HIT on ECMO, as well as the characteristics and outcomes of HIT patients.MethodsWe searched the PubMed, Embase, Cochrane Library, and Scopus databases for studies investigating HIT in adult patients supported by ECMO. All studies conforming to the inclusion criteria were screened from 1975 to August 2023. Nineteen studies from a total of 1,625 abstracts were selected. The primary outcomes were the incidence of HIT and suspected HIT.ResultsThe pooled incidence of HIT in ECMO-supported patients was 4.2% (95% CI: 2.7–5.6; 18 studies). A total of 15.9% (95% CI: 9.0-22.8; 12 studies) of patients on ECMO were suspected of having HIT. Enzyme-linked immunosorbent assay (ELISA) is the most commonly used immunoassay. The median optical density (OD) of the ELISA in HIT-confirmed patients ranged from 1.08 to 2.10. In most studies, the serotonin release assay (SRA) was performed as a HIT-confirming test. According to the subgroup analysis, the pooled incidence of HIT in ECMO patients was 2.7% in studies whose diagnostic mode was functional assays, which is significantly lower than the incidence in studies in which the patients were diagnosed by immunoassay (14.5%). Argatroban was most commonly used as an alternative anticoagulation agent after the withdrawal of heparin. Among confirmed HIT patients, 45.5% (95% CI: 28.8–62.6) experienced thrombotic events, while 50.1% (95% CI: 24.9–75.4) experienced bleeding events. Overall, 46.6% (95% CI: 30.4–63.1) of patients on ECMO with HIT died.ConclusionAccording to our study, the pooled incidence of HIT in ECMO-supported patients is 4.2%, and it contributes to adverse outcomes. Inappropriate diagnostic methods can easily lead to misdiagnosis of HIT. Further research and development of diagnostic algorithms and laboratory assays are warranted.

Development of diagnostic algorithm for Cushing’s syndrome: a tertiary centre experience

PurposeNo consensus exists as the gold standard for Cushing’s Syndrome (CS) screening. This study aimed to evaluate the diagnostic accuracy and utility of late-night salivary cortisol (LNSC) and cortisone (LNSE), overnight dexamethasone suppression test (ODST), and urinary free cortisol (UFC) in developing a screening algorithm for CS.MethodsA retrospective, single-centre analysis on 93 adult patients referred to the Oxford Centre for Diabetes, Endocrinology, and Metabolism for CS evaluation (2017–2022). Data were analysed using binomial logistic regression and area under the receiver-operating curve (AUROC).ResultsFifty-three patients were diagnosed with CS. LNSC (sensitivity 87.5%, specificity 64.9%, AUC 0.76), LNSE (sensitivity 72.4%, specificity 85.7%, AUC 0.79), and ODST (sensitivity 94.7%, specificity 52.1%; AUC 0.74) demonstrated comparable effectiveness for CS diagnosis. Their combined application increased diagnostic accuracy (AUC 0.91). UFC was not statistically significant. Pre-test clinical symptom inclusion improved screening test performance (AUC LNSC: 0.83; LNSE: 0.84; ODST: 0.82). For CD diagnosis, LNSE + LNSC (AUC 0.95) outperformed ODST. Combining these with ACTH levels < 12.6 pmol/L perfectly distinguished MACS (AUC 1.00). ODST (AUC 0.76) exhibited superior performance (sensitivity 100.0%, specificity 52.2%) in MACS detection.ConclusionsLNSC, LNSE, and ODST are robust tools for CS screening, with their combined use offering the highest diagnostic precision. LNSE, especially when used with LNSC, is highly effective for CD diagnosis, exceeding ODST accuracy. ODST is preferable for MACS identification. Integrating ACTH levels markedly improves differentiation between CD and MACS. Conversely, UFC shows limited diagnostic utility.

THEORETICAL AND PHENOMENOLOGICAL APPROACHES TO STUDYING THE CONCEPT OF PERSONALITY TRANSFORMATION OF MILITARY SERVICEMEN AS A CONSEQUENCE OF COMBAT TRAUMA

The purpose of the work – to investigate the personality profile of military personnelwith personality disorders resulting from combat trauma, based on a systematic approachto the study of individual- psychological, pathopsychological, and psychosocial features,in the projection of two different modern diagnostic classifications of mental disorders(ICX-11 and DSM-5) taking into account the pathogenetic and pathoplastic influenceof the premorbid personality structure on the formation of the syndrome complex, itstransformation and the possibility of reverse development.Conclusions. 1. The analysis of modern literature shows that the issue of personalitytransformation in the process of overcoming life crises is quite relevant. It is consideredby scientists in various aspects and manifestations, however, with the increase indevelopment, there is a lack of comprehensive, substantiated theoretical research. Inconnection with the ongoing military operations, as well as the increase in the frequencyof mental disorders as a result of combat trauma, the study of personality transformationin military personnel is of particular relevance. 2. Combat mental trauma is a deformation of the psychological mechanisms of self-regulation and a breakthrough by the combatstress factor of the protection of the psyche of the individual, as a result of which thereis an excess of its adaptive capabilities. 3. In the context of the transition of domesticpsychiatry to the new classification of ICD-11, understanding the transformation ofpersonality under the influence of combat trauma into the development of personalitydisorders appears as one of the most urgent issues of modern theoretical and practicalpsychiatry. Accordingly, there is a task of developing and using additional tools forthe objectification of severity assessment. 4. Taking into account the above, it can beargued that the development of diagnostic algorithms and differential diagnosis of thetransformation of the personality structure into personality disorders in servicemenas a result of combat trauma, as well as the development of recommendations for therehabilitation and resocialization of servicemen with personal mental disorders thatarose as a result of combat trauma, is relevant, necessary and timely.

Physically-based control-oriented modeling for turbocharged stoichiometric spark-ignited engine with cooled EGR and flexible VVT systems

Accurate estimation and prediction of engine gas exchange system and in-cylinder conditions are critical for spark-ignited engine control and diagnostic algorithm development. In this paper, a physically-based, control-oriented model for a 2.8 l turbocharged, variable valve timing (VVT) and low pressure (LP) exhaust gas recirculation (EGR)-utilizing SI engine was developed. The model includes the impact of modulation to any combination of 10 actuators, including the throttle valve, compressor bypass valve, fueling rate, waste-gate, LP EGR valve, number of deactivated cylinders, intake valve open (IVO) timing, intake valve close (IVC) timing, exhaust valve open (EVO) timing and exhaust valve close (EVC) timing. The accuracy of the model in capturing engine dynamics was demonstrated by validating it against high-fidelity engine GT-Power simulation results for various drive cycles, particularly emphasizing elevated loads. In comparison to the open literature, novel contributions of the effort described in this paper includes in-cylinder gas composition modeling and turbine-out pressure estimation.

Pathological changes or technical artefacts? The problem of the heterogenous databases in COVID-19 CXR image analysis

BackgroundWhen the COVID-19 pandemic commenced in 2020, scientists assisted medical specialists with diagnostic algorithm development. One scientific research area related to COVID-19 diagnosis was medical imaging and its potential to support molecular tests. Unfortunately, several systems reported high accuracy in development but did not fare well in clinical application. The reason was poor generalization, a long-standing issue in AI development. Researchers found many causes of this issue and decided to refer to them as confounders, meaning a set of artefacts and methodological errors associated with the method. We aim to contribute to this steed by highlighting an undiscussed confounder related to image resolution. Methods20 216 chest X-ray images (CXR) from worldwide centres were analyzed. The CXRs were bijectively projected into the 2D domain by performing Uniform Manifold Approximation and Projection (UMAP) embedding on the radiomic features (rUMAP) or CNN-based neural features (nUMAP) from the pre-last layer of the pre-trained classification neural network. Additional 44 339 thorax CXRs were used for validation. The comprehensive analysis of the multimodality of the density distribution in rUMAP/nUMAP domains and its relation to the original image properties was used to identify the main confounders. ResultsnUMAP revealed a hidden bias of neural networks towards the image resolution, which the regular up-sampling procedure cannot compensate for. The issue appears regardless of the network architecture and is not observed in a high-resolution dataset. The impact of the resolution heterogeneity can be partially diminished by applying advanced deep-learning-based super-resolution networks. ConclusionsrUMAP and nUMAP are great tools for image homogeneity analysis and bias discovery, as demonstrated by applying them to COVID-19 image data. Nonetheless, nUMAP could be applied to any type of data for which a deep neural network could be constructed. Advanced image super-resolution solutions are needed to reduce the impact of the resolution diversity on the classification network decision.

Prognostic Factors for Non-anastomotic Biliary Strictures Following Adult Liver Transplantation: A Systematic Review and Meta-Analysis.

The development of non-anastomotic biliary strictures (NAS) following orthotopic adult liver transplantation (OLT) is associated with significant morbidity. We performed a systematic review and meta-analysis to identify all prognostic factors for the development of NAS. A systematic review was conducted following preferred reporting items for systematic reviews and meta-analyses (PRISMA) and the meta-analysis of observational studies in epidemiology (MOOSE) guidelines. We used the Newcastle-Ottawa scale to assess the quality of the included studies. Using the random-effects model, we calculated the weighted pooled odds ratios (OR), mean differences (MD), hazard ratios (HR), and 95% confidence intervals (CI) of the risk factors. Based on 19 international studies that included a total of 8269 adult LT patients, we calculated an 8% overall incidence of NAS. In this study, 7 potential prognostic factors were associated with a statistically significant hazard ratio for NAS in pooled analyses including (1) DCD donors compared to DBD donors (2) PSC as an indication for a liver transplant (3) Roux-en-Y bile duct reconstruction compared to duct-to-duct reconstruction (4) hepatic artery thrombosis (5) longer cold ischemia time (6) longer warm ischemia time (7) and total operative times. In this systematic review and meta-analysis, we identified 7 prognostic factors for the development of NAS following OLT. These findings might lay the groundwork for development of diagnostic algorithms to better risk stratify patients at risk for development of NAS.

How to Distinguish Attenuated Forms of Mucopolysaccharidosis and Articular Forms of Juvenile Arthritis: Development of Diagnostic Algorithm Based on the Data from Multicenter Retrospective Study

Background. Differential diagnosis of attenuated forms of mucopolysaccharidosis (MPS) and juvenile idiopathic arthritis (JIA) can be challenging due to their similarities.Objective. The aim of the study is to create simple diagnostic criteria (DScore) that would allow to differentiate MPS from JIA for earlier MPS diagnosis.Methods. The retrospective multicenter study included analysis of clinical (joint, heart, eye involvement, hearing loss, hernias, psychomotor delay, noisy breathing, posture disorders, macrocephaly, hepatomegaly, splenomegaly, and growth delay) and laboratory data (ESR, CRP, hemoglobin, WBC, and platelets) from MPS patients (n = 41) and from rheumatoid factor-negative polyarticular category of JIA patients (n = 255). These variables allowed to differentiate both conditions and were used to create DScore.Results. Patients with MPS had younger onset age, male predominance, height and weight delay, lower inflammation markers (WBC, platelets, and ESR), and usually involved joints, especially cervical spine, upper limbs joints, hip, and small foot joints. The prevalence of eye involvement was similar for both diseases, however, the type of involvement was different. JIA patients had uveitis and its’ complications and MPS patients — corneal opacity and cataract. No differences in CRP levels were revealed in most cases. The major diagnostic criterion of MPS was the presence of more than one extra-articular manifestation associated with polyarticular involvement. DScore has included 5 following criteria: ESR ≤ 11 mm/h (38 points), height ≤ -2.0 SD (20 points), onset age of articular manifestations ≤ 1.1 year (24 points), male gender (15 points), and symmetrical limitation of movements in elbow joints (29 points). The sum &gt; 38 points allowed us to differentiate MPS and JIA with sensitivity of 92.7% and specificity of 91.0%.Conclusion. This DScore can be used for differential diagnosis of mild MPS and JIA alongside with routine diagnostic procedures. DScore allows us to identify a group of patients with joint involvement who require MPS exclusion.

Determining Reference Spectra for Medical Diagnosis Using Clustering Methods

In the paper, we describe the procedure of determining reference spectra for medical diagnosis. In our research, we have used various clustering methods to extract spectra of a group of healthy patients participating in the determination of the final reference spectra. The results presented in the paper can be used in further research concerning the development of diagnostic algorithms based on comparing the spectra of the subjects, suspected by a specific disease unit, with developed reference spectra of healthy people.

Development of diagnostic algorithm using machine learning for distinguishing between active tuberculosis and latent tuberculosis infection

BackgroundThe discrimination between active tuberculosis (ATB) and latent tuberculosis infection (LTBI) remains challenging. The present study aims to investigate the value of diagnostic models established by machine learning based on multiple laboratory data for distinguishing Mycobacterium tuberculosis (Mtb) infection status.MethodsT-SPOT, lymphocyte characteristic detection, and routine laboratory tests were performed on participants. Diagnostic models were built according to various algorithms.ResultsA total of 892 participants (468 ATB and 424 LTBI) and another 263 participants (125 ATB and 138 LTBI), were respectively enrolled at Tongji Hospital (discovery cohort) and Sino-French New City Hospital (validation cohort). Receiver operating characteristic (ROC) curve analysis showed that the value of individual indicator for differentiating ATB from LTBI was limited (area under the ROC curve (AUC) < 0.8). A total of 28 models were successfully established using machine learning. Among them, the AUCs of 25 models were more than 0.9 in test set. It was found that conditional random forests (cforest) model, based on the implementation of the random forest and bagging ensemble algorithms utilizing conditional inference trees as base learners, presented best discriminative power in segregating ATB from LTBI. Specially, cforest model presented an AUC of 0.978, with the sensitivity of 93.39% and the specificity of 91.18%. Mtb-specific response represented by early secreted antigenic target 6 (ESAT-6) and culture filtrate protein 10 (CFP-10) spot-forming cell (SFC) in T-SPOT assay, as well as global adaptive immunity assessed by CD4 cell IFN-γ secretion, CD8 cell IFN-γ secretion, and CD4 cell number, were found to contribute greatly to the cforest model. Superior performance obtained in the discovery cohort was further confirmed in the validation cohort. The sensitivity and specificity of cforest model in validation set were 92.80% and 89.86%, respectively.ConclusionsCforest model developed upon machine learning could serve as a valuable and prospective tool for identifying Mtb infection status. The present study provided a novel and viable idea for realizing the clinical diagnostic application of the combination of machine learning and laboratory findings.

CTEPH: A Kaiser Permanente Northern California Experience

IntroductionChronic thromboembolic pulmonary hypertension (CTEPH) is a rare and life-threatening form of pulmonary hypertension and the only potentially curable form of the World Health Organization Pulmonary Hypertension classes. Thus, the prompt and accurate diagnosis of this condition is imperative. Despite widespread chronic symptoms following acute pulmonary embolism (PE), the condition is rarely considered, and an externally validated inexpensive diagnostic algorithm is lacking. MethodsA long-term, retrospective cohort study was conducted to assess the incidence of CTEPH following acute PE in a real-world study population. Additional data were collected regarding the practice patterns of diagnostic testing and imaging, particularly in patients with persistent or recurrent symptoms. Amongst diagnosed CTEPH patients, previously established risk factors were evaluated for degree of risk and commonly used diagnostic tests (electrocardiogram [ECG] right ventricular hypertrophy [RVH] pattern, B-type natriuretic peptide [BNP] elevations) employed during this period were evaluated and assessed for feasibility as screening tests. The study population was obtained from the MAPLE study cohort, comprised of patients presenting with acute PE in 21 community medical centers across the Kaiser Permanente Northern California system from January 2013 to April 2015. Diagnosis of CTEPH was confirmed via pulmonary vascular imaging (ventilation/perfusion [V/Q] scanning, computed tomography angiography, pulmonary angiography) and diagnostic right heart catheterization (RHC). Probable diagnoses were defined as a combination of suggestive echocardiographic and RHC findings. Additional inclusion criteria included age (≥18 years) with at least 2 years follow up and no previous diagnosis of CTEPH or PE during the prior 30 days. ResultsThere were 1973 patients who met inclusion criteria (mean age 62.4 years). Despite 75 % of patients developing symptoms consistent with CTEPH >3 months following acute PE, only 5.6 % of these symptomatic patients underwent V/Q scanning. There was overall a very low cumulative incidence of CTEPH (2.3 %), which was significantly higher amongst patients with symptoms compared to those without symptoms. When controlled for confounding in the multivariate analysis, only recurrent PE (HR 19.3, P < 0.001) and pulmonary artery systolic pressure >50 mmHg (HR 10.4, P < 0.001) were statistically significant predictors of CTEPH. Of the non-invasive diagnostic tests, ECG criteria for RVH were found to be poorly sensitive (2.6 %), but very specific (98.8 %) for CTEPH. Elevated levels of BNP alone were more sensitive than RVH ECG criteria (76.3 %) but poorly specific (44.4 %). ConclusionsThe diagnosis of CTEPH is uncommonly made following acute PE. Despite the frequency of persistent symptoms consistent with CTEPH following acute PE, the appropriate diagnostic work-up is rarely undertaken as evidenced in this cohort. This suggests that CTEPH is underappreciated and rarely considered, likely underestimating the true incidence in this cohort. Future studies are needed to elucidate the true prevalence of CTEPH and further investigate both the optimal diagnostic tools and timing of appropriate screening. These discoveries may help guide future development of diagnostic algorithms that can effectively rule out and accurately identify this potentially curable disease in a timely manner.

Reliability of Self-Report Data on Age of Onset in Essential Tremor: Data from a Prospective, Longitudinal Cohort

Background: Essential tremor (ET) is a highly prevalent neurological disease. Age of onset can occur anytime between childhood and advanced age. Tremor generally starts insidiously. Age of onset is a particularly important data item in clinical and epidemiological research. In general, these data are self-reported by ET cases. A fundamental question is whether ET cases reliably report their age of onset. Methods: In this prospective, epidemiological study of 125 ET cases, self-reported age of onset data were collected at regular 18 months intervals over four time points. Results: The correlation between self-reported age of onset was high – intra-class correlation coefficient = 0.972 (95% confidence interval = 0.962–0.980, p < 0.001). However, agreement was not perfect. Approximately 20–25% of participant’s reports at different time intervals differed by as much as 10 years, and approximately 10% of participant’s reports differed by as much as 20 years. Conclusions: There was a robust correlation between self-reports of age of onset. Yet in a not-insignificant number of cases, there were considerable differences, some of which were substantial. These findings have broad implications for development of diagnostic algorithms, data stratification schemes, and analyses that assess correlations between biomarker data and clinical features (e.g., disease duration).

Optimization of Approaches to Early Diagnosis of Hearing Impairment in Primary Care

Almost 500 million people worldwide suffer from hearing loss or impairment. The causes of hearing loss are very diverse. Hearing loss can be caused by hereditary and non-hereditary genetic factors, certain complications during pregnancy and delivery, infectious and chronic diseases, head or ear trauma, etc. A common problem today is hearing impairment. Development of diagnostic algorithm for hearing disorders in primary care will improve the quality of diagnosis, optimize the patient’s management and ensure the earliest possible treatment and prevention measures, which will positively effect the results of treatment and patients quality of life. Primary care physicians use the ICPC-2 classification in their practice. They collect complaints, anamnesis, and test hearing ability. There are various methods to examine auditory function, most of which are based on the patient’s subjective data about perception of the heard sounds. These include perception of whispered and spoken speech, tuning fork experiments (accumetry), audiometry. Hearing is considered normal if a whispered speech is perceived at a distance of 6 m. The whispered speech degree of perception is the greatest distance from which the patient correctly repeats the words spoken by the doctor fro three times. Primary care physicians conduct a tuning fork examination of the patient using the Weber test (W), Rinne test (R), Schwabach test (Sch). Timely hearing examination with the help of modern diagnostic methods in primary care and the correct assessment of the potential capabilities of the auditory system of patients allows correct evaluation of its condition. The normal functioning of the hearing organ is one of the crucial factors for the overall development and healthy human life. After all, thanks to the auditory analyzer, a person receives information about the surrounding world.

Изменения ОКТ-сигнала роговицы при поражениях глаз газовым оружием самообороны

In the conditions of the modern world order, studies on gas weapons are increasingly appearing in medical periodicals. To date, self-defense gas weapons and special means, with various modifications of the delivery methods of gas weapon components, which are in service with law enforcement agencies, are allowed to be used. Despite the increasing interest in this issue, the development of diagnostic algorithms for the examination of these patients is not found in the articles. In the presented article, we consider the question of the informativeness of optical coherence tomography for the diagnosis of lesions of the rabbit cornea with gas self-defense weapons in experimental conditions. We have identified changes in the reflexivity of the signal from the corneal tissues that correlate with the clinical manifestations of eye lesions and their dynamics during therapy. Changes in the structure of the cornea have been confirmed by pathomorphological studies. We have shown that optical coherence tomography is a highly sensitive method of examining the cornea in cases of damage to the organ of vision with a gas self-defense weapon, which allows tracking the development of the lesion in dynamics. Based on the results of the work carried out, it can be concluded that the use of optical coherence tomography for the diagnosis and evaluation of treatment in affected laboratory animals is quite acceptable and effective. Keywords: the eye trauma, gas weapon of self-defense, chemical eye burn, the ocular trauma, non-lethal weapon, optical coherence tomography, oleoresin capsoicin, ortochlorbenziloidenmalonodinitrile, cornea burn.

Deep learning-based patient re-identification is able to exploit the biometric nature of medical chest X-ray data

With the rise and ever-increasing potential of deep learning techniques in recent years, publicly available medical datasets became a key factor to enable reproducible development of diagnostic algorithms in the medical domain. Medical data contains sensitive patient-related information and is therefore usually anonymized by removing patient identifiers, e.g., patient names before publication. To the best of our knowledge, we are the first to show that a well-trained deep learning system is able to recover the patient identity from chest X-ray data. We demonstrate this using the publicly available large-scale ChestX-ray14 dataset, a collection of 112,120 frontal-view chest X-ray images from 30,805 unique patients. Our verification system is able to identify whether two frontal chest X-ray images are from the same person with an AUC of 0.9940 and a classification accuracy of 95.55%. We further highlight that the proposed system is able to reveal the same person even ten and more years after the initial scan. When pursuing a retrieval approach, we observe an mAP@R of 0.9748 and a precision@1 of 0.9963. Furthermore, we achieve an AUC of up to 0.9870 and a precision@1 of up to 0.9444 when evaluating our trained networks on external datasets such as CheXpert and the COVID-19 Image Data Collection. Based on this high identification rate, a potential attacker may leak patient-related information and additionally cross-reference images to obtain more information. Thus, there is a great risk of sensitive content falling into unauthorized hands or being disseminated against the will of the concerned patients. Especially during the COVID-19 pandemic, numerous chest X-ray datasets have been published to advance research. Therefore, such data may be vulnerable to potential attacks by deep learning-based re-identification algorithms.

Применение искусственного интеллекта в диагностике и хирургии кератоконуса: систематический обзор

Introduction. Artificial Intelligence is new theoretical approaches, methods, technologies and applied systems for modeling and extending human intelligence. In ophthalmology, artificial intelligence is one of the tools that help improve the efficiency of the treatment process through more accurate diagnostics, search for new biomarkers of diseases, automation of decision-making processes and assistance in other aspects of the physician‘s daily activities. The purpose of this review is to describe the currently available developments for the diagnosis and surgery of keratoconus in the field of artificial intelligence. Material and methods. Databases that were used for literature search included: Google and Google Scholar, PubMed, Embase, MEDLINE and Web of Science. Results. As a result of a search across all selected databases, as well as a selection of relevant studies, 75 articles were analyzed. Most of the studies that were selected for full-text analysis were the development of diagnostic algorithms. The most common classical machine learning methods were support vector machines method and random forest method. The most commonly used type of neural network is the convolutional neural network. 4 studies out of 75 reported the creation of a graphical interface for using the developed algorithm in a clinical environment. Conclusion. The accuracy of the algorithms that were obtained in the analyzed researches was basically more than 90%. It indicates the ability of machine learning models to solve complex clinical problems. Key words: artificial intelligence, machine learning, keratoconus, diagnostics

P129 Hepcidin and ferritin index can help to differentiate between different types of anaemia: an exploratory study

Abstract Background Anaemia is frequent in patients with IBD. Iron deficiency anaemia [IDA] and anaemia of chronic disease [ACD] are the most common subtypes and warrant a different treatment approach. The ESPGHAN/ NASHGAN position paper on Anaemia in Children with IBD describes cut-off values on ferritin, transferrin saturation [TSAT] and ferritin index (soluble transferrin receptor/log10(ferritin)) [sTfR-F], to distinguish between IDA and ACD. However, cut-off values for sTfR-F and hepcidin are based on adult data. In this exploratory study we used a data driven approach to define different anaemia groups, using hepcidin and sTfR-F and studied differences between groups and their response to iron suppletion therapy. Insights contribute to a better description of ACD and IDA in a pediatric population. Methods Data from the multi-centre prospective POPEYE study (NTR4487) consisting of a paediatric IBD population was used. Subjects with anaemia (haemoglobin [Hb] &amp;lt; 2 standard deviations [SD] below the mean of a healthy age reference group) with baseline ferritin, TSAT, hepcidin, sTfR-F and erythrocyte sedimentation rate [ESR] values were selected. Summary statistics of these biomarkers were generated for the data-driven groups and compared with existing characteristics for IDA and ACD groups. At baseline subjects received oral or intravenous iron repletion therapy, Hb was determined at baseline and 1 month after iron therapy. Kmeans was used, an unsupervised clustering algorithm using Euclidean distance, to divide data points into k clusters based on sTfR-F and hepcidin. Results Mean age was 13.6 (2.5 SD) years, and mean standardized Hb value -3.2 at baseline for 47 subjects. Data driven groupings using Kmeans (k=3) were based on baseline hepcidin and sTfR-F (figure 1). Group 1 shows characteristics indicative for IDA, with mean ferritin &amp;lt;15µg/l, TSAT &amp;lt; 20% and sTfR-F &amp;gt; 2. Group 2 shows signs suggestive for inflammation since ferritin, hepcidin and ESR were increased (mean ESR 28,5 mm/h [23,2 SD]); this group could be characterized as ACD. Group 3 could not be related to a specific anaemia type. Mean standardized Hb increase 1 month after iron repletion for 37 patients, was 2,9 (SD 0,8), 0,8 (0,7 SD), 1,3 (1,1 SD), for group 1, 2 and 3 respectively. Conclusion Explorative data-driven analysis of a real-world data set showed that hepcidin and sTfR-F can be used to characterize groups that relate to ACD and IDA in a paediatric population. Children with high sTfR-F and low hepcidin possibly benefit more from iron repletion therapy (group 1), compared to when hepcidin is high (group 2). If validated in a larger patient population this determination could contribute to the development of diagnostic algorithms that support targeted therapy.

Cardiac Amyloidosis Screening at Trigger Finger Release Surgery

Cardiac amyloidosis is often preceded by orthopedic manifestations such as carpal tunnel syndrome, and 10% of patients who underwent idiopathic carpal tunnel release surgery will have biopsy-confirmed amyloid deposits in the tenosynovial sheath. Trigger finger is also commonly reported in patients with amyloidosis and involves the same tendon sheath as carpal tunnel syndrome, but the prevalence of amyloid deposition is unclear. This prospective cross-sectional study enrolled 100 patients aged ≥50 years at the time of surgery for idiopathic trigger finger. Patients underwent release surgery, and a sample of the tenosynovium of the affected finger was excised, stained with Congo red, and subtyped with mass spectrometry if amyloid was demonstrated. Further cardiac evaluation was performed in patients with amyloid deposition. Of the 100 patients (mean age 65.5 ± 8.1years) enrolled, only 2 demonstrated amyloid deposits on Congo red staining. One patient with previous proteinuric kidney disease had fibrinogen A α-chain amyloidosis, and the other patient had untyped amyloidosis. Neither patient had cardiac involvement. A total of 13 of the 100 patients underwent concomitant carpal tunnel release surgery, and 2 of these patients had amyloid deposits in the carpal tunnel with "false-negative" samples from the trigger finger tenosynovium. In conclusion, biopsy during trigger finger release surgery demonstrated a 2% yield for amyloidosis, which is significantly lower than the previously published yield of 10% during carpal tunnel release surgery. This observation has important implications for the development of diagnostic algorithms to screen patients for amyloidosis during orthopedic operations.

CORRECTION OF MENSTRUAL FUNCTION DISORDERS IN GIRLS WITH OBESITY

Based on the literature datа and the results of our studies, menstrual dysfunction in teen girls leads to disorders of the reproductive system in fertile age. Establishment of normal menstrual function in teen girls is an eff ective measure for the prevention of future reproductive health disorders, which leads to improved methods of menstrual disorders diagnostics in overweight girls by analyzing clinical and anamnestic data, biochemical markers, development of diagnostic algorithm and pathogenetically based correction of menstrual disorders. To gain this aim, it was proposed to include to the complex of conventional therapy, diet therapy and drugs myo-inositol (inofolic softgel) and metformin. Evaluation of the treatment eff ectiveness was performed on the regression dynamics of the disease main clinical symptom and the normalization of hormonal status indicators. The result of treatment was considered satisfactory when the patient had regular menstruation for 12 months on the background of the therapy, and the maintenance of irregular menstruation with a cycle duration of more than 38 days was unsatisfactory. As a result of treatment the body mass index has decreased in all the patients which received our complex of therapeutic measures, especially for obesity of the second and third degree what proves the eff ectiveness of complex treatment. The appointment of proposed complex of treatment measures for patients is pathogenetically based, because after treatment, previously detected insulin resistance decreased signifi cantly in all groups, but most and reliably in groups with obesity of the second and third degree and became almost within normal limits.

Acute and chronic neurological disorders in COVID-19: potential mechanisms of disease.

Coronavirus disease 2019 (COVID-19) is a global pandemic caused by SARS-CoV-2 infection and is associated with both acute and chronic disorders affecting the nervous system. Acute neurological disorders affecting patients with COVID-19 range widely from anosmia, stroke, encephalopathy/encephalitis, and seizures to Guillain–Barré syndrome. Chronic neurological sequelae are less well defined although exercise intolerance, dysautonomia, pain, as well as neurocognitive and psychiatric dysfunctions are commonly reported. Molecular analyses of CSF and neuropathological studies highlight both vascular and immunologic perturbations. Low levels of viral RNA have been detected in the brains of few acutely ill individuals. Potential pathogenic mechanisms in the acute phase include coagulopathies with associated cerebral hypoxic-ischaemic injury, blood–brain barrier abnormalities with endotheliopathy and possibly viral neuroinvasion accompanied by neuro-immune responses. Established diagnostic tools are limited by a lack of clearly defined COVID-19 specific neurological syndromes. Future interventions will require delineation of specific neurological syndromes, diagnostic algorithm development and uncovering the underlying disease mechanisms that will guide effective therapies.

Protein glycation – biomarkers of metabolic dysfunction and early-stage decline in health in the era of precision medicine

Protein glycation provides a biomarker in widespread clinical use, glycated hemoglobin HbA1c (A1C). It is a biomarker for diagnosis of diabetes and prediabetes and of medium-term glycemic control in patients with established diabetes. A1C is an early-stage glycation adduct of hemoglobin with glucose; a fructosamine derivative. Glucose is an amino group-directed glycating agent, modifying N-terminal and lysine sidechain amino groups. A similar fructosamine derivative of serum albumin, glycated albumin (GA), finds use as a biomarker of glycemic control, particularly where there is interference in use of A1C. Later stage adducts, advanced glycation endproducts (AGEs), are formed by the degradation of fructosamines and by the reaction of reactive dicarbonyl metabolites, such as methylglyoxal. Dicarbonyls are arginine-directed glycating agents forming mainly hydroimidazolone AGEs. Glucosepane and pentosidine, an intense fluorophore, are AGE covalent crosslinks. Cellular proteolysis of glycated proteins forms glycated amino acids, which are released into plasma and excreted in urine. Development of diagnostic algorithms by artificial intelligence machine learning is enhancing the applications of glycation biomarkers. Investigational glycation biomarkers are in development for: (i) healthy aging; (ii) risk prediction of vascular complications of diabetes; (iii) diagnosis of autism; and (iv) diagnosis and classification of early-stage arthritis. Protein glycation biomarkers are influenced by heritability, aging, decline in metabolic, vascular, renal and skeletal health, and other factors. They are applicable to populations of differing ethnicities, bridging the gap between genotype and phenotype. They are thereby likely to find continued and expanding clinical use, including in the current era of developing precision medicine, reporting on multiple pathogenic processes and supporting a precision medicine approach.