Clinical Factors Associated with Binge-Eating Episodes or Purging Behaviors in Patients Affected by Eating Disorders: A Cross-Sectional Study.

The aim of the present study was to investigate the potential associations between clinical/socio-demographic variables and the presence of purging/binge-eating episodes in eating disorders (EDs). Clinical/socio-demographic variables and psychometric scores were collected. Groups of patients were identified according to the presence or absence of purging or objective binge-eating episodes (OBEs) and compared through t-test and chi-square tests. Binary logistic regression analyses were run. A sample of 51 ED outpatients was recruited. Patients with purging behaviors had a longer duration of untreated illness (DUI) (t = 1.672; p = 0.019) and smoked a higher number of cigarettes/day (t = 1.061; p = 0.030) compared to their counterparts. A lower BMI was associated with purging (OR = 0.881; p = 0.035), and an older age at onset showed a trend towards statistical significance (OR = 1.153; p = 0.061). Patients with OBEs, compared to their counterparts, were older (t = 0.095; p < 0.001), more frequently presented a diagnosis of bulimia or binge-eating disorder (χ2 = 26.693; p < 0.001), a longer duration of illness (t = 2.162; p = 0.019), a higher number of hospitalizations (t = 1.301; p = 0.012), and more often received a prescription for pharmacological treatment (χ2 = 7.864; OR = 6.000; p = 0.005). A longer duration of the last pharmacological treatment was associated with OBE (OR = 1.569; p = 0.046). In contrast to purging, OBE was associated with a more complicated and severe presentation of ED. A lower BMI and a later age at onset, as well as long-lasting previous pharmacological treatments, may predict the presence of purging/binging. Further research is needed to thoroughly characterize ED features and corroborate our preliminary findings.

Prevalence and clinical markers of herpes simplex virus infection in oral lesions of bullous pemphigoid.

Background: The manifestations of bullous pemphigoid (BP) and herpes simplex virus (HSV) infection are similar in oral mucosa, and the laboratory detection of HSV has some limitations, making it difficult to identify the HSV infection in oral lesions of BP. In addition, the treatments for BP and HSV infection have contradictory aspects. Thus, it is important to identify the HSV infection in BP patients in time. Objective: To identify the prevalence and clinical markers of HSV infection in oral lesions of BP. Methods: This prospective cross-sectional descriptive analytical study was conducted on 42 BP patients with oral lesions. A total of 32 BP patients without oral lesions and 41 healthy individuals were enrolled as control groups. Polymerase chain reaction was used to detect HSV. Clinical and laboratory characteristics of patients with HSV infection were compared with those without infection. Results: A total of 19 (45.2%) BP patients with oral lesions, none (0.0%) BP patients without oral lesions, and four (9.8%) healthy individuals were positive for HSV on oral mucosa. Among BP patients with oral lesions, the inconsistent activity between oral and skin lesions (p=0.001), absence of blister/blood blister in oral lesions (p=0.020), and pain for oral lesions (p=0.014) were more often seen in HSV-positive than HSV-negative BP patients; the dosage of glucocorticoid (p=0.023) and the accumulated glucocorticoid dosage in the last 2 weeks (2-week AGC dosage) (p=0.018) were higher in HSV-positive BP patients. Combining the above five variables as test variable, the AUC was 0.898 (p<0.001) with HSV infection as state variable in ROC analysis. The absence of blister/blood blister in oral lesions (p=0.030) and pain for oral lesions (p=0.038) were found to be independent predictors of HSV infection in multivariable analysis. A total of 14 (73.7%) HSV-positive BP patients were treated with 2-week famciclovir and the oral mucosa BPDAI scores significantly decreased (p<0.001). Conclusion: HSV infection is common in BP oral lesions. The inconsistent activity between oral and skin lesions, absence of blister in oral lesions, pain for oral lesions, higher currently used glucocorticoid dosage, and higher 2-week AGC dosage in BP patients should alert physicians to HSV infection in oral lesions and treat them with 2-week famciclovir in time.

Elaboración de una escala de puntuación predictiva de infección periamigdalina basada en signos y síntomas / Design of a predictive score table for peritonsillar infection based on signs and symptoms

Introducción La infección periamigdalina (IPA) supone un motivo de consulta urgente entre las molestias de garganta. Un diagnóstico diferido o incorrecto puede comprometer la vía aerodigestiva alta y resultar mortal en su evolución. Nuestro objetivo fue desarrollar un modelo predictivo de presencia de IPA que ayude en su rápida detección. Pacientes y métodos Un estudio observacional retrospectivo de 66 meses desde 2017 fue desarrollado en un hospital comarcal y su centro terciario de referencia, recogiendo datos de todos los pacientes diagnosticados de IPA y un volumen proporcional de sujetos con sintomatología faríngea sin IPA. Recopilación de datos clínicos, exploratorios y demográficos entre participantes. Su mayor riesgo relativo de presencia de IPA los etiquetó como variables a testar. Elaboración de una escala de puntuación de probabilidad de padecerla y análisis de regresión logística, con obtención de la curva ROC que ofreciera mejor correlación diagnóstica. Validación interna y cálculo de los valores predictivos de este modelo. Resultados Sobre 348 casos de IPA, la escala de valoración puntuó la presencia de 6 variables: trismus (3), disfagia-odinofagia unilateral (2), abombamiento velar (2), otalgia refleja (1), faringolalia (1) y edad de 16-46 años (1). Con un rango de 0 a 10, un cut-off≥6 ofreció una sensibilidad del 96,1%, una especificidad del 93,9% y una eficienca del 94,9%. El área bajo la curva ROC fue de 0,979. Conclusiones La validación interna de este modelo basado en signos y síntomas la faculta como herramienta muy útil para detectar precozmente IPA en otorrinolaringología y atención primaria (AU)

Background Peritonsillar infection (PTI) is a reason for urgent consultation due to intense throat discomfort. A delayed or inaccurate diagnosis can jeopardize the upper aerodigestive tract and be fatal in its evolution. Our objective was to develop a predictive model for the presence of IPA helping in its rapid detection. Patients and methods A 66-month retrospective observational study from 2017 was carried out in a county and tertiary referral hospitals, registering data from all patients diagnosed with PTI and a proportional volume of subjects with pharyngeal symptoms without PTI. Collection of clinical, exploratory and demographic data among participants. Their higher relative risk of PTI presence allowed them to be considered as variables to be tested. Development of a scoring scale for the probability of suffering from it and logistic regression analysis, obtaining the ROC curve with the best diagnostic correlation. Internal validation and estimation of the predictive values of the model. Results On 348 cases of PTI, the assessment scale scored the presence of six variables: trismus (3), unilateral dysphagia-odynophagia (2), velar bulging (2), reflex otalgia (1), pharyngolalia (1) and age between 16 and 46 years (1). With a range of 0-10, a cut-off ≥6 offered a sensitivity of 96.1%, a specificity of 93.9%, and an efficiency of 94.9%. The area under the ROC curve was 0.979. Conclusions The internal validation of this model based on signs and symptoms makes it a very useful tool for early detection of PTI in otorhinolaryngology and primary care (AU)

The use of event-related potentials in the investigation of cognitive performance in people with Multiple Sclerosis: Systematic review.

A biomarker of cognition in Multiple Sclerosis (MS) that is independent from the response of people with MS (PwMS) to test questions would provide a more holistic assessment of cognitive decline. One suggested method involves event-related potentials (ERPs). This systematic review tried to answer five questions about the use of ERPs in distinguishing PwMS from controls: which stimulus modality, which experimental paradigm, which electrodes, and which ERP components are most discriminatory, and whether amplitude or latency is a better measure. Our results show larger pooled effect sizes for visual stimuli than auditory stimuli, and larger pooled effect sizes for latency measurements than amplitude measurements. We observed great heterogeneity in methods and suggest that future research would benefit from more uniformity in methods and that results should be reported for the individual subtypes of PwMS. With more standardised methods, ERPs have the potential to be developed into a clinical tool in MS.

Detection of anemic condition in patients from clinical markers and explainable artificial intelligence.

BACKGROUND: Anaemia is a commonly known blood illness worldwide. Red blood cell (RBC) count or oxygen carrying capability being insufficient are two ways to describe anaemia. This disorder has an impact on the quality of life. If anaemia is detected in the initial stage, appropriate care can be taken to prevent further harm. OBJECTIVE: This study proposes a machine learning approach to identify anaemia from clinical markers, which will help further in clinical practice. METHODS: The models are designed with a dataset of 364 samples and 12 blood test attributes. The developed algorithm is expected to provide decision support to the clinicians based on blood markers. Each model is trained and validated on several performance metrics. RESULTS: The accuracy obtained by the random forest, K nearest neighbour, support vector machine, Naive Bayes, xgboost, and catboost are 97%, 98%, 95%, 95%, 98% and 97% respectively. Four explainers such as Shapley Additive Values (SHAP), QLattice, Eli5 and local interpretable model-agnostic explanations (LIME) are explored for interpreting the model predictions. CONCLUSION: The study provides insights into the potential of machine learning algorithms for classification and may help in the development of automated and accurate diagnostic tools for anaemia.

[Design of a predictive score table for peritonsillar infection based on signs and symptoms]. / Elaboración de una escala de puntuación predictiva de infección periamigdalina basada en signos y síntomas.

BACKGROUND: Peritonsillar infection (PTI) is a reason for urgent consultation due to intense throat discomfort. A delayed or inaccurate diagnosis can jeopardize the upper aerodigestive tract and be fatal in its evolution. Our objective was to develop a predictive model for the presence of IPA helping in its rapid detection. PATIENTS AND METHODS: A 66-month retrospective observational study from 2017 was carried out in a county and tertiary referral hospitals, registering data from all patients diagnosed with PTI and a proportional volume of subjects with pharyngeal symptoms without PTI. Collection of clinical, exploratory and demographic data among participants. Their higher relative risk of PTI presence allowed them to be considered as variables to be tested. Development of a scoring scale for the probability of suffering from it and logistic regression analysis, obtaining the ROC curve with the best diagnostic correlation. Internal validation and estimation of the predictive values of the model. RESULTS: On 348 cases of PTI, the assessment scale scored the presence of six variables: trismus (3), unilateral dysphagia-odynophagia (2), velar bulging (2), reflex otalgia (1), pharyngolalia (1) and age between 16 and 46 years (1). With a range of 0-10, a cut-off ≥6 offered a sensitivity of 96.1%, a specificity of 93.9%, and an efficiency of 94.9%. The area under the ROC curve was 0.979. CONCLUSIONS: The internal validation of this model based on signs and symptoms makes it a very useful tool for early detection of PTI in otorhinolaryngology and primary care.

Respiratory system parameters in children with low severity cystic fibrosis: is there early involvement in relation to healthy peers? / Parâmetros do sistema respiratório em crianças com fibrose cística de baixa gravidade: há acometimento precoce em relação a pares saudáveis?

ABSTRACT Objective: To compare and analyze pulmonary function and respiratory mechanics parameters between healthy children and children with cystic fibrosis. Methods: This cross-sectional analytical study included healthy children (HSG) and children with cystic fibrosis (CFG), aged 6-13 years, from teaching institutions and a reference center for cystic fibrosis in Florianópolis/SC, Brazil. The patients were paired by age and sex. Initially, an anthropometric evaluation was undertaken to pair the sample characteristics in both groups; the medical records of CFG were consulted for bacterial colonization, genotype, and disease severity (Schwachman-Doershuk Score — SDS) data. Spirometry and impulse oscillometry were used to assess pulmonary function. Results: In total, 110 children were included, 55 in each group. In the CFG group, 58.2% were classified as excellent by SDS, 49.1% showed the ΔF508 heterozygotic genotype, and 67.3% were colonized by some pathogens. Statistical analysis revealed significant differences between both groups (p<0.05) in most pulmonary function parameters and respiratory mechanics. Conclusions: Children with cystic fibrosis showed obstructive ventilatory disorders and compromised peripheral airways compared with healthy children. These findings reinforce the early changes in pulmonary function and mechanics associated with this disease.

RESUMO Objetivo: Comparar e analisar parâmetros de função pulmonar e de mecânica respiratória entre escolares saudáveis e com fibrose cística (FC). Métodos: Estudo transversal que incluiu escolares saudáveis (GES) e com FC (GFC), com idades entre seis e 13 anos, provenientes de instituições de ensino e de um centro de referência da FC em Florianópolis/SC, Brasil, pareados por idade e sexo, respectivamente. Inicialmente, conduziu-se avaliação antropométrica para pareamento e caracterização de ambos os grupos e, no GFC, consultou-se prontuário médico para registro dos dados de colonização bacteriana, genótipo e gravidade da doença (Escore de Schwachman-Doershuk — ESD). Para a avaliação da função pulmonar, realizou-se espirometria e a avaliação da mecânica respiratória foi conduzida por meio do sistema de oscilometria de impulso. Resultados: Participaram 110 escolares, 55 em cada grupo. No GFC, 58,2% foram classificados pelo ESD como excelentes, 49,1% apresentaram genótipo ∆F508 heterozigoto e 67,3% eram colonizados por alguma patógeno. Houve diferença significativa (p<0,05) na maioria dos parâmetros de função pulmonar e de mecânica respiratória entre os grupos. Conclusões: Escolares com FC apresentaram distúrbio ventilatório obstrutivo e com comprometimento de vias aéreas periféricas, em comparação aos escolares hígidos. Esse evento reforça o início precoce da alteração de função pulmonar e de mecânica respiratória nessa enfermidade, evidenciados pelos achados desta investigação.

Prevalence of type 2 diabetes (T2D) in Lebanon: association with inflammatory and infectious clinical markers.

BACKGROUND: Diabetes is a growing health concern in the Middle East, particularly in countries with high rates of obesity and unhealthy lifestyles. Therefore, this study aimed to determine the prevalence of type 2 diabetes (T2D) in Lebanon and its association with clinical markers of inflammation and infection. METHODS: This cross-sectional study examined retrospectively the medical laboratory record of 4093 patients from all Lebanese regions. Prevalence of T2D and its association with age, gender, calcium, vitamin D (VitD), neutrophils-to-lymphocytes ratio (NLR), and C-reactive protein (CRP) were determined. The prevalence of infection in a subpopulation of 712 patients tested from blood, body fluid, sputum, swab, tissue, and urine samples and its etiology was also assessed. RESULTS: Overall, 17% (n = 690) of our participants had T2D, and the mean HbA1c was 5.9% ± 1.2. Age, gender, triglycerides, NLR, and calcemia were significantly associated with T2D. The prevalence of infections in a subgroup of 712 patients was 11.1% (n = 79). Urinary tract infections (UTIs) caused by Escherichia coli (E. coli) were the most common cause of infection, with the highest prevalence in the pre-diabetic group. Serum CRP level was significantly higher in the diabetic group than the pre-diabetic and control groups. Diabetic patients also presented a significantly higher percentage of NLR > 3 compared to the pre-diabetic and control groups. CONCLUSION: The prevalence of T2D is increasing in the Lebanese population compared to prior reports. These results should be considered to guide effective public health preventive strategies.

Leukocyte cell population data as potential markers of COVID-19 disease characterization.

Background: The usefulness of leukocyte cell population data (CPD) is currently being investigated. In COVID-19 pandemic several reports showed the clinical importance of hematological parameters. Our study aimed to assess CPDs in Sars CoV-2 patients as new disease markers. Methods: From February to April 2020 (1st wave) 540 and from September to December 2020 (2nd wave) 2821 patients respectively were enrolled. SARS CoV-2 infection diagnosis was carried out by Multiplex rRT-PCR from nasopharyngeal swabs. CPDs were detected by XN 2000 hematology analyzer (Sysmex Corporation). A comparison between two disease waves was performed. Additionally, C-reactive protein (CRP) and lactate dehydrogenase (LDH) were assayed. Results: CPDs were classified into: cell complextity, DNA/RNA content and abnormal sized cells. We detected parameters increased from the reference population for all cell types for both 1st and 2nd wave (p<0.05). However, in the 2nd vs 1st wave 5 CPDs vs 9 CPDs were found. In addition we observed higher CPD values of the 1st compared to 2nd wave: (NE-SFL) (p<0.001), (LY-Y) (p<0.0001), (LY-Z) (p<0.0001), (MO-X) (p<0.0001), (MO-Y) (p<0.0001). These findings were confirmed by the higher concentrations of CRP and LDH in the 1st vs 2nd wave: 17.3 mg/L (8.5-59.3) vs 6.3 mg/L (2.3-17.6) (p<0.001) and 241.5 IU/L (201-345) vs 195 IU/L (174-228) (p< 0.001) (median, interquartile range) respectively. Conclusions: CPDs showed increased cell activation in 1st wave patients confirmed by clinical and biochemical data, associated with worse clinical conditions. Results highlighted the CPDs as disease characterization markers or useful for a risk model.

COVID-19 diagnosis using clinical markers and multiple explainable artificial intelligence approaches: A case study from Ecuador.

The COVID-19 pandemic erupted at the beginning of 2020 and proved fatal, causing many casualties worldwide. Immediate and precise screening of affected patients is critical for disease control. COVID-19 is often confused with various other respiratory disorders since the symptoms are similar. As of today, the reverse transcription-polymerase chain reaction (RT-PCR) test is utilized for diagnosing COVID-19. However, this approach is sometimes prone to producing erroneous and false negative results. Hence, finding a reliable diagnostic method that can validate the RT-PCR test results is crucial. Artificial intelligence (AI) and machine learning (ML) applications in COVID-19 diagnosis has proven to be beneficial. Hence, clinical markers have been utilized for COVID-19 diagnosis with the help of several classifiers in this study. Further, five different explainable artificial intelligence techniques have been utilized to interpret the predictions. Among all the algorithms, the k-nearest neighbor obtained the best performance with an accuracy, precision, recall and f1-score of 84%, 85%, 84% and 84%. According to this study, the combination of clinical markers such as eosinophils, lymphocytes, red blood cells and leukocytes was significant in differentiating COVID-19. The classifiers can be utilized synchronously with the standard RT-PCR procedure making diagnosis more reliable and efficient.

Artificial intelligence for diagnosis of mild-moderate COVID-19 using haematological markers.

OBJECTIVE: The persistent spread of SARS-CoV-2 makes diagnosis challenging because COVID-19 symptoms are hard to differentiate from those of other respiratory illnesses. The reverse transcription-polymerase chain reaction test is the current golden standard for diagnosing various respiratory diseases, including COVID-19. However, this standard diagnostic method is prone to erroneous and false negative results (10% -15%). Therefore, finding an alternative technique to validate the RT-PCR test is paramount. Artificial intelligence (AI) and machine learning (ML) applications are extensively used in medical research. Hence, this study focused on developing a decision support system using AI to diagnose mild-moderate COVID-19 from other similar diseases using demographic and clinical markers. Severe COVID-19 cases were not considered in this study since fatality rates have dropped considerably after introducing COVID-19 vaccines. METHODS: A custom stacked ensemble model consisting of various heterogeneous algorithms has been utilized for prediction. Four deep learning algorithms have also been tested and compared, such as one-dimensional convolutional neural networks, long short-term memory networks, deep neural networks and Residual Multi-Layer Perceptron. Five explainers, namely, Shapley Additive Values, Eli5, QLattice, Anchor and Local Interpretable Model-agnostic Explanations, have been utilized to interpret the predictions made by the classifiers. RESULTS: After using Pearson's correlation and particle swarm optimization feature selection, the final stack obtained a maximum accuracy of 89%. The most important markers which were useful in COVID-19 diagnosis are Eosinophil, Albumin, T. Bilirubin, ALP, ALT, AST, HbA1c and TWBC. CONCLUSION: The promising results suggest using this decision support system to diagnose COVID-19 from other similar respiratory illnesses.

Deep Analysis of Clinical Parameters and Temporal Evolution of Glycemic Parameters Based on CGM Data for the Characterization of Severe Hypoglycemia in a Cohort of Children and Adolescents with Type 1 Diabetes.

This study aims to evaluate the determinants and clinical markers of patients at risk for severe hypoglycemia (SH) in children and adolescents with type 1 diabetes. In the EPI-GLUREDIA study, clinical parameters and continuous glucose monitoring metrics from children and adolescents with type 1 diabetes were retrospectively analyzed between July 2017 and June 2022. Their clinical parameters were collected during traditional and quarterly medical consultations according to whether they experienced severe hypoglycemia or not. Then, continuous glucose monitoring metrics were analyzed on days surrounding SH during specific periods. According to the glycemic parameters, glycemic hemoglobin and glycemic mean were significantly lower in the three months preceding a SH compared with during three normal months (p < 0.05). Moreover, the time spent in hypoglycemia(time below the range, TBR<3.3) and its strong correlation (R = 0.9, p < 0.001) with the frequency of SH represent a sensitive and specific clinical parameter to predict SH (cut-off: 9%, sensitivity: 71%, specificity: 63%). The second finding of the GLUREDIA study is that SH is not an isolated event in the glycemic follow-up of our T1DM patients. Indeed, most of the glycemic parameters (i.e., glycemic mean, glycemic variability, frequency of hypoglycemia, and glycemic targets) vary considerably in the month preceding an SH (all p < 0.05), whereas most of these studied glycemic parameters remain stable in the absence of a severe acute complication (all p > 0.05). Furthermore, the use of ROC curves allowed us to determine for each glycemic parameter a sensitive or specific threshold capable of more accurately predicting SH. For example, a 10% increase in the frequency of hypoglycemia predicts a risk of near SH with good combination of sensitivity and specificity (sensitivity: 80%, specificity: 60%). The GLUREDIA study aimed to target clinical and glycemic parameters to predict patients at risk for SH. First, we identified TBR<3.3 < 9% as a sensitive and specific tool to reduce the frequency of SH. In addition, SH was not an isolated event but rather it was accompanied by glycemic disturbances in the 30 days before SH.

Diagnostic approaches for dengue infection.

INTRODUCTION: Every year, a significant rise in dengue incidence observed is responsible for 10% of fever episodes in children and adolescents in endemic countries. Considering that the symptoms of dengue are similar to those of many other viruses, early diagnosis of the disease has long been difficult, and lack of sensitive diagnostic tools may be another factor contributing to a rise in dengue incidence. AREAS COVERED: This review will highlight dengue diagnostics strategies and discuss other possible targets for dengue diagnosis. Understanding the dynamics of the immune response and how it affects viral infection has enabled informed diagnosis. As more technologies emerge, precise assays that include some clinical markers need to be included. EXPERT OPINION: Future diagnostic strategies will require the use both viral and clinical markers in a serial manner with the use of artificial intelligence technology to determine from the first point of illness to better determine severity status and management. A definitive endpoint is not in the horizon as the disease as well as the virus is constantly evolving and hence many developed assays need to be constantly changing some of their reagents periodically as newer genotypes and probably too serotypes emerge.

The relative area score for sublingual varices reliability measurement: a diagnostic study.

BACKGROUND: Sublingual varices (SV) and their predictive potential for other clinical parameters is a much studied topic in oral medicine. SVs have been well studied as predictive markers for many common diseases such as arterial hypertension, cardiovascular disease, smoking, type 2 diabetes mellitus and age. Despite many prevalence studies, it is still unclear how the reliability of SV inspection affects its predictive power. The aim of this study was to quantify the inspection reliability of SV. METHODS: In a diagnostic study, the clinical inspection of 78 patients by 23 clinicians was examined for the diagnosis of SV. Digital images of the underside of the tongue were taken from each patient. The physicians were then asked to rate them for the presence of sublingual varices (0/1) in an online inspection experiment. Statistical analysis for inter-item and inter-rater reliability was performed in a τ-equivalent measurement model with Cronbach's [Formula: see text] and Fleiss κ. RESULTS: The interrater reliability for sublingual varices was relatively low with κ = 0.397. The internal consistency of image findings for SV was relatively high with α≈ 0.937. This shows that although SV inspection is possible in principle, it has a low reliability R. This means that the inspection finding (0/1) of individual images often cannot be reproduced stably. Therefore, SV inspection is a difficult task of clinical investigation. The reliability R of SV inspection also limits the maximum linear correlation [Formula: see text] of SV with an arbitrary other parameter Y. The reliability of SV inspection R = 0.847 limits the maximum correlation to [Formula: see text] (SV, Y) = 0,920-a 100% correlation was a priori not achievable in our sample. To overcome the problem of low reliability in SV inspection, we propose the RA (relative area) score as a continuous classification system for SV, which normalises the area of visible sublingual veins to the square of the length of the tongue, providing a dimensionless measure of SV. CONCLUSIONS: The reliability of the SV inspection is relatively low. This limits the maximum possible correlation of SV with other (clinical) parameters. SV inspection reliability is an important indicator for the quality of SV as a predictive marker. This should be taken into account when interpreting previous studies on SV and has implications for future studies. The RA score could help to objectify the SV examination and thus increase its reliability.

Novel oxidized LDL-based clinical markers in peritoneal dialysis patients for atherosclerosis risk assessment.

Maintenance peritoneal dialysis (PD) is commonly associated with cardiovascular diseases (CVDs), whose risk is assessed via LDL-C. Nonetheless, oxidized LDL (oxLDL), as being a key component of atherosclerotic lesions, could be also associated with atherosclerosis and related CVDs. However, its predictive value for CVDs risk assessment is subject of research studies due to the lack of specific methods to measure oxLDL status from its individual lipid/protein components. In the present study, six novel oxLDL markers, representative of certain oxidative modifications on the LDL protein and lipid components, are measured in atherosclerosis-prone PD patients (39) versus those in chronic kidney disease patients (61) under hemodialysis (HD) and healthy controls (40). LDL from serum of PD, HD and control subjects were isolated and fractionated into cholesteryl esters, triglycerides, free cholesterol, phospholipids and apolipoprotein B100 (apoB100). Subsequently the oxLDL markers cholesteryl ester hydroperoxides (-OOH), triglyceride-OOH, free cholesterol-OOH, phospholipid-OOH, apoB100 malondialdehyde and apoB100 dityrosines were measured. LDL carotenoid levels and LDL particle serum concentration were also measured. The levels of all oxLDL lipid-OOH markers were significantly elevated in PD patients versus control, while the levels of cholesteryl ester-/triglyceride-/free cholesterol-OOH were significantly elevated in PD versus HD patients, regardless of patients' underlying medical conditions, sex, age, PD type, clinical biochemical markers and medication. It should be noted that all fractionated lipid-OOH levels were inversely correlated with LDL-P concentration, while LDL-P concentration was not correlated with LDL-C in PD patients. Moreover, LDL carotenoids were significantly lower in PD patients versus control. The increased levels of oxLDL status specific markers in both PD and HD patients (compared to control), support a potential prognostic value of oxLDL regarding CVD risk assessment in both patient groups. Lastly, the study introduces the oxLDL peroxidation markers free cholesterol-OOH and cholesteryl ester-OOH as complementary to LDL-P number, and as possible alternatives to LDL-C.

A Decision Support System for Diagnosis of COVID-19 from Non-COVID-19 Influenza-like Illness Using Explainable Artificial Intelligence.

The coronavirus pandemic emerged in early 2020 and turned out to be deadly, killing a vast number of people all around the world. Fortunately, vaccines have been discovered, and they seem effectual in controlling the severe prognosis induced by the virus. The reverse transcription-polymerase chain reaction (RT-PCR) test is the current golden standard for diagnosing different infectious diseases, including COVID-19; however, it is not always accurate. Therefore, it is extremely crucial to find an alternative diagnosis method which can support the results of the standard RT-PCR test. Hence, a decision support system has been proposed in this study that uses machine learning and deep learning techniques to predict the COVID-19 diagnosis of a patient using clinical, demographic and blood markers. The patient data used in this research were collected from two Manipal hospitals in India and a custom-made, stacked, multi-level ensemble classifier has been used to predict the COVID-19 diagnosis. Deep learning techniques such as deep neural networks (DNN) and one-dimensional convolutional networks (1D-CNN) have also been utilized. Further, explainable artificial techniques (XAI) such as Shapley additive values (SHAP), ELI5, local interpretable model explainer (LIME), and QLattice have been used to make the models more precise and understandable. Among all of the algorithms, the multi-level stacked model obtained an excellent accuracy of 96%. The precision, recall, f1-score and AUC obtained were 94%, 95%, 94% and 98% respectively. The models can be used as a decision support system for the initial screening of coronavirus patients and can also help ease the existing burden on medical infrastructure.

A Machine Learning Approach for Highlighting microRNAs as Biomarkers Linked to Amyotrophic Lateral Sclerosis Diagnosis and Progression.

Amyotrophic Lateral Sclerosis (ALS) is a fatal neurodegenerative disease characterized by the progressive loss of motor neurons in the brain and spinal cord. The early diagnosis of ALS can be challenging, as it usually depends on clinical examination and the exclusion of other possible causes. In this regard, the analysis of miRNA expression profiles in biofluids makes miRNAs promising non-invasive clinical biomarkers. Due to the increasing amount of scientific literature that often provides controversial results, this work aims to deepen the understanding of the current state of the art on this topic using a machine-learning-based approach. A systematic literature search was conducted to analyze a set of 308 scientific articles using the MySLR digital platform and the Latent Dirichlet Allocation (LDA) algorithm. Two relevant topics were identified, and the articles clustered in each of them were analyzed and discussed in terms of biomolecular mechanisms, as well as in translational and clinical settings. Several miRNAs detected in the tissues and biofluids of ALS patients, including blood and cerebrospinal fluid (CSF), have been linked to ALS diagnosis and progression. Some of them may represent promising non-invasive clinical biomarkers. In this context, future scientific priorities and goals have been proposed.

A nomogram model based on clinical markers for predicting malignancy of ovarian tumors.

Objective: The aim of this study was to build a nomogram based on clinical markers for predicting the malignancy of ovarian tumors (OTs). Method: A total of 1,268 patients diagnosed with OTs that were surgically removed between October 2017 and May 2019 were enrolled. Clinical markers such as post-menopausal status, body mass index (BMI), serum human epididymis protein 4 (HE4) value, cancer antigen 125 (CA125) value, Risk of Ovarian Malignancy Algorithm (ROMA) index, course of disease, patient-generated subjective global assessment (PG-SGA) score, ascites, and locations and features of masses were recorded and analyzed (p 0.05). Significant variables were further selected using multivariate logistic regression analysis and were included in the decision curve analysis (DCA) used to assess the value of the nomogram model for predicting OT malignancy. Result: The significant variables included post-menopausal status, BMI, HE4 value, CA125 value, ROMA index, course of disease, PG-SGA score, ascites, and features and locations of masses (p 0.05). The ROMA index, BMI (≥ 26), unclear/blurred mass boundary (on magnetic resonance imaging [MRI]/computed tomography [CT]), mass detection (on MRI/CT), and mass size and features (on type B ultrasound [BUS]) were screened out for multivariate logistic regression analysis to assess the value of the nomogram model for predicting OT malignant risk (p 0.05). The DCA revealed that the net benefit of the nomogram's calculation model was superior to that of the CA125 value, HE4 value, and ROMA index for predicting OT malignancy. Conclusion: We successfully tailored a nomogram model based on selected clinical markers which showed superior prognostic predictive accuracy compared with the use of the CA125, HE4, or ROMA index (that combines both HE and CA125 values) for predicting the malignancy of OT patients.

The comprehension and production of Wh- questions among Malay children with developmental language disorders: Climbing the syntactic tree.

This study is an investigation of both comprehension and production of Wh- questions in Malay-speaking children with a developmental language disorder (DLD). A total of 15 Malay children with DLD (ages 7;0-9;11 years) were tested on a set of Wh- questions (who subject and object, which subject and object), comparing their performance with two control groups [15 age-matched typically developing (TD) children and 15 younger TD language-matched children]. Malay children with DLD showed a clear asymmetry in comprehension of Wh- questions, with a selective impairment for which NP questions compared with who questions. Age-matched controls performed at ceiling in all Wh- questions, while the language-matched group reported a subject/object asymmetry selective for the which NP, as reported in other languages. In production, both children with DLD and younger children showed a preference for questions with in situ Wh- elements, a structure that is allowed in colloquial Malay, but which is not produced by the age-matched TD group. Several non-adult-like strategies were adopted particularly by the children with DLD to avoid complex sentences, including substitution with yes/no echo questions, production of the wrong Wh- question, and use of a generic Wh- element. The study provides an insight on the mastery of Wh- questions in both typical Malay children and children with DLD. Implications for the definition of a clinical marker for DLD in a free word order language with Wh- in situ option will be discussed.

Clinical Markers of 6 Pre-dominant Coping Behaviors in Living With Parkinson Disease: A Convergent Mixed Methods Study.

People with Parkinson's disease (PwP) experience a variety of symptoms and fluctuations in these, which they have to cope with every day. In tailoring a person-centered treatment to PwP there is a lack of knowledge about the association between pre-dominant coping behaviors and clinical markers among PwP. To describe and compare specific clinical markers between 6 suggested coping behaviors. Thirty-four PwP, who previously had been classified into 6 different pre-dominant coping behaviors, were included in this mixed methods study. Six primary variables were included in the descriptive analysis; motor function (UPDRS-III), non-motor symptoms score (NMS-Quest), change in bradykinesia score, apathy score (LARS), personality traits (NEO-FFI), and cognitive status (evaluated by a neuropsychologist). The merged results of this mixed methods study indicate that clinical markers as apathy, burden of non-motor symptoms, cognitive impairments and personality traits, have the potential to impact the coping behavior in PwP. In a clinical setting the markers; NMS-burden, degree of apathy, cognition, and personality traits may indicate specific coping behavior. Three of the six suggested typologies of coping behaviors differed from the other groups when comparing descriptive data. In order to improve patient care and guide the development of person-centered therapies, each PwP should be approached based on those typologies.