Screening for metabolic dysfunction-associated fatty liver disease: Time to discard the emperor's clothes of normal liver enzymes?

Metabolic dysfunction-associated fatty liver disease (MAFLD) is the most prevalent chronic liver condition worldwide. Current liver enzyme-based screening methods have limitations that may missed diagnoses and treatment delays. Regarding Chen et al, the risk of developing MAFLD remains elevated even when alanine aminotransferase levels fall within the normal range. Therefore, there is an urgent need for advanced diagnostic techniques and updated algorithms to enhance the accuracy of MAFLD diagnosis and enable early intervention. This paper proposes two potential screening methods for identifying individuals who may be at risk of developing MAFLD: Lowering these thresholds and promoting the use of noninvasive liver fibrosis scores.

Identification of key biomarkers for early warning of diabetic retinopathy using BP neural network algorithm and hierarchical clustering analysis.

Diabetic retinopathy is one of the most common microangiopathy in diabetes, essentially caused by abnormal blood glucose metabolism resulting from insufficient insulin secretion or reduced insulin activity. Epidemiological survey results show that about one third of diabetes patients have signs of diabetic retinopathy, and another third may suffer from serious retinopathy that threatens vision. However, the pathogenesis of diabetic retinopathy is still unclear, and there is no systematic method to detect the onset of the disease and effectively predict its occurrence. In this study, we used medical detection data from diabetic retinopathy patients to determine key biomarkers that induce disease onset through back propagation neural network algorithm and hierarchical clustering analysis, ultimately obtaining early warning signals of the disease. The key markers that induce diabetic retinopathy have been detected, which can also be used to explore the induction mechanism of disease occurrence and deliver strong warning signal before disease occurrence. We found that multiple clinical indicators that form key markers, such as glycated hemoglobin, serum uric acid, alanine aminotransferase are closely related to the occurrence of the disease. They respectively induced disease from the aspects of the individual lipid metabolism, cell oxidation reduction, bone metabolism and bone resorption and cell function of blood coagulation. The key markers that induce diabetic retinopathy complications do not act independently, but form a complete module to coordinate and work together before the onset of the disease, and transmit a strong warning signal. The key markers detected by this algorithm are more sensitive and effective in the early warning of disease. Hence, a new method related to key markers is proposed for the study of diabetic microvascular lesions. In clinical prediction and diagnosis, doctors can use key markers to give early warning of individual diseases and make early intervention.

Newborn and infant hearing screening for early detection of hearing loss in Nairobi, Kenya.

Background: Early detection of hearing loss and subsequent intervention leads to better speech, language and educational outcomes giving way to improved social economic prospects in adult life. This can be achieved through establishing newborn and infant hearing screening programs. Objective: To determine the prevalence of hearing loss in newborns and infants in Nairobi, Kenya. Methods: A cross-sectional pilot study was conducted at the National hospital and at a sub county hospital immunization clinic. A total of 9,963 babies aged 0-3 years, were enrolled in the hearing screening program through convenient sampling over a period of nine months. A case history was administered followed by Distortion Product Oto-acoustic emissions (DPOAEs) and automated auditory brainstem response (AABR) hearing screening. Results: The screening coverage rate was 98.6% (9963/10,104). The referral rate for the initial screen was 3.6% (356/ 9,963), the return rate for follow-up rescreening was 72% (258 babies out of 356) with a lost to follow-up rate of 28% (98/356). The referral rate of the second screen was 10% (26/258). All the 26 babies referred from the second screen returned for diagnostic hearing evaluation and were confirmed with hearing loss, yielding a prevalence of 3/1000. Conclusions: Establishing universal newborn and infant hearing screening programs is essential for early detection and intervention for hearing loss. Data management and efficient follow-up systems are an integral part of achieving diagnostic confirmation of hearing loss and early intervention.

Driving under the influence of alcohol and alcohol use disorder: the relevance of early identification from an Italian retrospective outpatient study.

INTRODUCTION: Worldwide, almost 1.2 million people drive under the influence of alcohol. However, early identification of alcohol use disorder (AUD) in subjects driving under the influence (DUI) of alcohol is seldom achieved. AIM: The aim of our retrospective study is to investigate the presence of AUD in a population of DUI subjects who had their driving license suspended, and if they were following a specific rehabilitation program. METHODS AND RESULTS: 750 subjects were retrospectively enrolled from 2018 to 2021. DSM-V to assess AUD was used. Forty-eight (6.4%) subjects presented a diagnosis of AUD, after one month they showed a statistically significant reduction of carbohydrate-deficient transferrin (CDT) (p<0.0001); however, none were following a program for the treatment of AUD. CONCLUSIONS: This outpatient setting may be considered a place of primary and secondary prevention where DUI subjects with a diagnosis of AUD may be entrusted to a Centre in order to follow rehabilitation treatment.

Early detection of low QRS voltage and its association with mortality in patients with sepsis.

Various electrocardiographic changes occur during sepsis, but data on the clinical importance of a low QRS voltage in sepsis are still limited. We aimed to evaluate the association between low QRS voltage identified early in sepsis and mortality in patients with sepsis. Between September 2019 and December 2020, all consecutive adult patients diagnosed with sepsis in the emergency room or general ward at Samsung Medical Center were enrolled. Patients without a 12-lead electrocardiogram recorded within 48 h of recognition of sepsis were excluded. In 432 eligible patients, 12-lead electrocardiogram was recorded within the median of 24 min from the first recognition of sepsis, and low QRS voltage was identified in 115 (26.6%) patients. The low QRS group showed more severe organ dysfunction and had higher levels of N-terminal pro-brain natriuretic peptide. The hospital mortality was significantly higher in the low QRS voltage group than in the normal QRS voltage group (49.6% vs. 28.1%, p < 0.001). Similarly, among the 160 patients who required intensive care unit admission, significantly more patients in the low QRS group died in the intensive care unit (35.9% vs. 18.2%, p = 0.021). Low QRS voltage was associated with increased hospital mortality in patients with sepsis.

Establishment and Diagnostic Value of an Early Prediction Model for Acute Pancreatitis Complicated With Acute Kidney Injury.

OBJECTIVES: To establish an early prediction model for acute pancreatitis (AP) complicated with acute kidney injury (AKI) and evaluate its diagnostic value. METHOD: AP patients were recruited from the Emergency Department at Peking University People's Hospital in 2021 and stratified into AKI and control (no AKI) groups. Their clinical data were analyzed. The risk for AKI development was determined using logistic analyses to establish a risk prediction model, whose diagnostic value was analyzed using a receiver operating characteristic curve. RESULTS: There was no significant difference in the basic renal function between the AKI (n = 79) and control (n = 179) groups. The increased triglyceride glucose index (odds ratio [OR], 2.613; 95% confidence interval [CI], 1.324-5.158; P = 0.006), age (OR, 1.076; 95% CI, 1.016-1.140; P = 0.013), and procalcitonin (OR, 1.377; 95% CI, 1.096-1.730, P = 0.006) were associated with AKI development. A model was established for prediction of AKI (sensitivity 79.75%, specificity 96.65%). The area under the receiver operating characteristic curve was 0.856 which was superior to the Ranson, Bedside Index for Severity in AP, and Acute Physiology and Chronic Health Evaluation II scores (0.856 vs 0.691 vs 0.745 vs 0.705). CONCLUSIONS: The prediction model based on age, triglyceride glucose, and procalcitonin is valuable for the prediction of AP-related AKI.

[The gut, a whistleblower, in the early stages of Parkinson's disease]. / L'intestin, lanceur d'alerte, dans les prémices de la maladie de Parkinson.

The enteric nervous system (ENS), often called the "second brain", plays a crucial role in regulating digestive functions. Dysfunctions of the ENS are associated with several diseases such as Parkinson's disease. Recent studies suggest that early digestive disorders, notably chronic constipation, may be early signs of this neurodegenerative disease. Three-dimensional imaging of the ENS offers new insights into early diagnosis, in particular through the analysis of intestinal biopsies. This new research axis raises questions about the intestinal cause of Parkinson's disease, and opens the door to a better understanding and earlier treatment of this disease.

Title: L'intestin, lanceur d'alerte, dans les prémices de la maladie de Parkinson. Abstract: Le système nerveux entérique (SNE), souvent qualifié de « deuxième cerveau ¼, joue un rôle crucial dans la régulation des fonctions digestives. Des dysfonctionnements du SNE sont associés à diverses maladies telles que la maladie de Parkinson. Des études récentes suggèrent que les troubles digestifs précoces, notamment la constipation chronique, pourraient être des signes avant-coureurs de cette maladie neurodégénérative. L'imagerie tridimensionnelle du SNE offre de nouvelles perspectives pour un diagnostic précoce via notamment l'analyse de biopsies intestinales. Ce nouvel axe de recherche soulève des questions sur l'origine intestinale de la maladie de Parkinson et ouvre la porte à une meilleure compréhension et une prise en charge anticipée de cette maladie.

Plasma type IV collagen levels as a potential biomarker for early detection of nephropathy in diabetes mellitus patients.

Diabetic nephropathy represents a microvascular complication related to type 2 diabetes mellitus (T2DM) that ultimately causes end-stage renal disease. Our study aimed to evaluate the association of plasma type IV collagen with albuminuria status and to assess the clinical significance of plasma type IV collagen as a potential biomarker in the early stage of diabetic nephropathy. The study comprised 75 participants diagnosed with T2DM allocated equally (n=25) into three groups: (A) normal albuminuria levels, (B) microalbuminuria, and (C) macroalbuminuria, depending on their urine albumin-to-creatinine ratio. A comparative analysis was conducted between these groups and a control group (D, n=15). The enzyme-linked immunosorbent assay (ELISA) method was employed for measuring plasma type IV collagen levels. The results revealed that plasma type IV collagen levels were significantly higher in T2DM groups than in the control group. Moreover, diabetic patients without albuminuria had significantly higher plasma type IV collagen levels than the control group (p < 0.001). Furthermore, albuminuria levels among diabetic patient groups were significantly increased as albuminuria categories increased (p < 0.001). A significant positive correlation existed between plasma type IV collagen and glycated hemoglobin (HbA1c) levels in the macroalbuminuric diabetic group. Our study employed the receiver operating characteristic (ROC) curve analysis to determine plasma type IV collagen diagnostic utility in macroalbuminuria prediction. The ROC curve analysis revealed that type IV collagen can significantly determine macroalbuminuric patients at a cutoff value of 2.25 with sensitivity, specificity, positive predictive value, and negative predictive value of 68%, 100%, 100%, and 75.8%, respectively (p < 0.001). In conclusion, plasma type IV collagen levels might serve as a valuable predictor of albuminuria onset in patients with T2DM.

Correlation analysis between the early diagnosis and severity of neuropathic pain and the activation level of NLRP3 inflammasome: A retrospective study.

To investigate the correlation between neuropathic pain's early diagnosis, severity, and nucleotide-binding domain, leucine-rich-containing family, pyrin domain-containing-3 (NLRP3) inflammasome activation, we retrospectively evaluated 50 patients with neuropathic pain and 50 healthy individuals. Activation of the NLRP3 inflammasome was measured in blood samples, as well as pain levels and clinical markers. Neuropathic pain patients exhibited elevated NLRP3 inflammasome activation. Pain intensity positively correlated with activation. Correlation was also observed with inflammatory markers and pain-related biomarkers. NLRP3 inflammasome demonstrated high diagnostic sensitivity. In conclusion, NLRP3 inflammasome activation influences neuropathic pain initiation and progression. Measuring activation levels may serve as an early diagnostic indicator and severity gauge for neuropathic pain.

Alanine aminotransferase as a risk marker for new-onset metabolic dysfunction-associated fatty liver disease.

In this editorial, we comment on the article by Chen et al. Metabolic dysfunction-associated fatty liver disease (MAFLD) is a global public health burden whose incidence has risen concurrently with overweight and obesity. Given its detrimental health impact, early identification of at-risk individuals is crucial. MAFLD diagnosis is based on evidence of hepatic steatosis indicated by liver biopsy, imaging, or blood biomarkers, and one of the following conditions: Overweight/ obesity, type 2 diabetes mellitus, or metabolic dysregulation. However, in large-scale epidemiological studies, liver biopsies are not feasible. The application of techniques such as ultrasonography, computed tomography, magnetic resonance imaging, and magnetic resonance spectroscopy is restricted by their limited sensitivity, low effectiveness, high costs, and need for specialized software. Blood biomarkers offer several advantages, particularly in large-scale epidemiological studies or clinical scenarios where traditional imaging techniques are impractical. Analysis of cumulative effects of excess high-normal blood alanine aminotransferase (ALT) levels of blood ALT levels could facilitate identification of at-risk patients who might not be detected through conventional imaging methods. Accordingly, investigating the utility of blood biomarkers in MAFLD should enhance early detection and monitoring, enabling timely intervention and management and improving patient outcomes.

Patients Journey Before Early Rheumatoid Arthritis Diagnosis Contributes to disease's Activity Level: A Real-Life Study.

INTRODUCTION: The help-seeking process in rheumatoid arthritis (RA) patients is challenging, and its study is limited in Latin America. The study describes the real-life journey before patients' incorporation into an early arthritis clinic (EAC) and its impact on baseline and 1-year cumulative disease activity levels. PATIENTS AND METHODS: The patient's journey was assessed through a questionnaire that captured the patient's path from the first disease-related symptom to the initial assessment in the EAC. A disease activity (28 joints evaluated)-erythrocyte sedimentation rate (DAS28-ESR) score >5.1 defined a high-disease activity level. The mean of individual consecutive DAS28-ESR scores summarized cumulative DAS28-ESR. Multiple logistic regression analysis identified factors associated with a DAS28-ESR score >5.1 at the first assessment. Linear regression analysis assessed the impact of general practitioner (GP)-first consultant and time on disease-modifying antirheumatic drugs (DMARDs) on baseline and cumulative DAS28-ESR scores. RESULTS: Through January 2023, the EAC had 241 RA patients, among whom 209 (86.7%) completed the patients' journey questionnaire (PJQ) and 176 (84.2%) at least 1 year of follow-up. A GP was the first consultant in 76.6% of the patients, and only 12.4% were prescribed DMARDs. Patients had additional evaluations with either rheumatologists (38.6%) or other specialists (31.6%), and half of them were initiated DMARDs. GP-first consultant (adjusted odds ratio: 2.314, 95% confidence interval: 1.190-4.500, p = 0.013) and time on DMARDs (adjusted odds ratio: 0.738, 95% confidence interval: 0.585-0.929, p = 0.010) were associated with baseline DAS28-ESR score >5.1. The B coefficient magnitudes for GP-first consultant and time on DMARDs to predict cumulative DAS28 progressively decreased during the first year of follow-up. CONCLUSIONS: Patients' journey before recent-onset RA diagnosis predicts first-year disease activity levels.

Identification of circulating miRNA biomarkers in leptospirosis for early detection: A promising diagnostic approach.

Leptospirosis is a zoonotic disease of global significance, contributing to morbidity and mortality worldwide. It is endemic to tropical regions, with outbreaks during monsoons. The disease manifestations are similar to that of other febrile illness such as dengue, malaria hence often misdiagnosed and underreported. The zoonoses if undetected, progresses to cause severe life-threatening complications also known as Weil's disease. Routine diagnostic tests are based on the detection of antibodies in patient serum and are not accurate during the initial phase of the infection. Therefore, it is necessary to detect novel biomarkers that can be used in early detection of leptospirosis. Circulating miRNAs are known to be promising biomarkers for various diseases including cancer, tuberculosis, influenza; hence in this study the potential of miRNAs as biomarkers for leptospirosis was evaluated. A total of 30 leptospirosis cases were screened for the differential expression of 10 miRNA by RT-qPCR assay. The differential expression was calculated by relative quantification using healthy individuals as controls. Among the 10 miRNA,3 miRNA, miR-28-5p, miR-302c-3p and miR-302a-3p were reported to exhibit a significant trend of upregulation. Further their role in immune pathways and biological processes was investigated by KEGG analysis and Gene Ontology. The 3 miRNAs were observed to target various immune response pathways, thus confirming their role in host immune response. Based on the results obtained in this study, miR-28-5p, miR-302c-3p and miR-302a-3p can be considered as potential biomarkers for the detection of leptospirosis.

Potentials of artificial intelligence in familial hypercholesterolemia: Advances in screening, diagnosis, and risk stratification for early intervention and treatment.

Familial hypercholesterolemia (FH) poses a global health challenge due to high incidence rates and underdiagnosis, leading to increased risks of early-onset atherosclerosis and cardiovascular diseases. Early detection and treatment of FH is critical in reducing the risk of cardiovascular events and improving the long-term outcomes and quality of life for affected individuals and their families. Traditional therapeutic approaches revolve around lipid-lowering interventions, yet challenges persist, particularly in accurate and timely diagnosis. The current diagnostic landscape heavily relies on genetic testing of specific LDL-C metabolism genes, often limited to specialized centers. This constraint has led to the adoption of alternative clinical scores for FH diagnosis. However, the rapid advancements in artificial intelligence (AI) and machine learning (ML) present promising solutions to these diagnostic challenges. This review explores the intricacies of FH, highlighting the challenges that are encountered in the diagnosis and management of the disorder. The revolutionary potential of ML, particularly in large-scale population screening, is highlighted. Applications of ML in FH screening, diagnosis, and risk stratification are discussed, showcasing its ability to outperform traditional criteria. However, challenges and ethical considerations, including algorithmic stability, data quality, privacy, and consent issues, are crucial areas that require attention. The review concludes by emphasizing the significant promise of AI and ML in FH management while underscoring the need for ethical and practical vigilance to ensure responsible and effective integration into healthcare practices.

Artificial intelligence in Parkinson's disease: Early detection and diagnostic advancements.

Parkinson's disease (PD) is the second most common neurodegenerative disorder, globally affecting men and women at an exponentially growing rate, with currently no cure. Disease progression starts when dopaminergic neurons begin to die. In PD, the loss of neurotransmitter, dopamine is responsible for the overall communication of neural cells throughout the body. Clinical symptoms of PD are slowness of movement, involuntary muscular contractions, speech & writing changes, lessened automatic movement, and chronic tremors in the body. PD occurs in both familial and sporadic forms and modifiable and non-modifiable risk factors and socioeconomic conditions cause PD. Early detectable diagnostics and treatments have been developed in the last several decades. However, we still do not have precise early detectable biomarkers and therapeutic agents/drugs that prevent and/or delay the disease process. Recently, artificial intelligence (AI) science and machine learning tools have been promising in identifying early detectable markers with a greater rate of accuracy compared to past forms of treatment and diagnostic processes. Artificial intelligence refers to the intelligence exhibited by machines or software, distinct from the intelligence observed in humans that is based on neural networks in a form and can be used to diagnose the longevity and disease severity of disease. The term Machine Learning or Neural Networks is a blanket term used to identify an emerging technology that is created to work in the way of a "human brain" using many intertwined neurons to achieve the same level of raw intelligence as that of a brain. These processes have been used for neurodegenerative diseases such as Parkinson's disease and Alzheimer's disease, to assess the severity of the patient's condition. In the current article, we discuss the prevalence and incidence of PD, and currently available diagnostic biomarkers and therapeutic strategies. We also highlighted currently available artificial intelligence science and machine learning tools and their applications to detect disease and develop therapeutic interventions.

Early detection of cardiorespiratory complications and training monitoring using wearable ECG sensors and CNN.

This research study demonstrates an efficient scheme for early detection of cardiorespiratory complications in pandemics by Utilizing Wearable Electrocardiogram (ECG) sensors for pattern generation and Convolution Neural Networks (CNN) for decision analytics. In health-related outbreaks, timely and early diagnosis of such complications is conclusive in reducing mortality rates and alleviating the burden on healthcare facilities. Existing methods rely on clinical assessments, medical history reviews, and hospital-based monitoring, which are valuable but have limitations in terms of accessibility, scalability, and timeliness, particularly during pandemics. The proposed scheme commences by deploying wearable ECG sensors on the patient's body. These sensors collect data by continuously monitoring the cardiac activity and respiratory patterns of the patient. The collected raw data is then transmitted securely in a wireless manner to a centralized server and stored in a database. Subsequently, the stored data is assessed using a preprocessing process which extracts relevant and important features like heart rate variability and respiratory rate. The preprocessed data is then used as input into the CNN model for the classification of normal and abnormal cardiorespiratory patterns. To achieve high accuracy in abnormality detection the CNN model is trained on labeled data with optimized parameters. The performance of the proposed scheme is evaluated and gauged using different scenarios, which shows a robust performance in detecting abnormal cardiorespiratory patterns with a sensitivity of 95% and specificity of 92%. Prominent observations, which highlight the potential for early interventions include subtle changes in heart rate variability and preceding respiratory distress. These findings show the significance of wearable ECG technology in improving pandemic management strategies and informing public health policies, which enhances preparedness and resilience in the face of emerging health threats.

Improving prevention and early detection of sepsis among patient groups at risk: Introducing a model for a multimodal information campaign-The SepWiss study protocol.

BACKGROUND: Sepsis is a life-threatening organ dysfunction due to a dysregulated host response to infection. Annually, sepsis leads to approx. 90.000 deaths in Germany. Risk factors include amongst others older age (>60), innate or acquired dysfunction of the immune system, and underlying chronic diseases of the lung, heart, liver, or kidneys. The manifestation of sepsis is a medical emergency, and patient outcomes depend on timely diagnosis and immediate treatment. In addition, vaccinations e.g., against pneumococci or influenza virus, are a highly effective public health tool to prevent the most common underlying infections that may lead to sepsis. However, a lack of public awareness for the relevance of vaccination and detecting sepsis as an emergency underlines the need for public health interventions that address these issues. SepWiss aims to evaluate the effects of a multimodal information campaign designed to address this lack of awareness among the risk population in Germany. METHODS: SepWiss is an intervention at state level, consisting of a multimodal information campaign targeting risk groups in the German federal states of Berlin and Brandenburg (intervention region). Based on available evidence, various information formats were developed and implemented by outdoor advertising, social media, educational formats and through stakeholders' platforms, starting in August 2021. The control region comprises of the remaining 14 German federal states. We will analyze vaccination coverage (primary outcome), and sepsis knowledge, the ability to detect sepsis as an emergency, and attitude towards vaccination (secondary outcomes) amongst the risk population in a controlled before-after comparison. The implementation is accompanied by a mixed-method process evaluation. DISCUSSION: SepWiss is the first project of its kind to evaluate a complex multi-faceted evidence-based information campaign with regards to the topics of vaccination coverage, and the importance of sepsis detection and prevention for the most vulnerable populations in Germany. Results will be valuable for informing further nationwide campaigns. TRIAL REGISTRATION: German Registry for Clinical Trials: DRKS00024475. Registered February 24th, 2021.

Left atrial stiffness index - an early marker of left ventricular diastolic dysfunction in patients with coronary heart disease.

AIMS: To evaluate the correlation between left atrial stiffness index (LASI) and left ventricular diastolic function in patients with coronary heart disease (CHD) by Autostrain LA technique. METHODS: This was a retrospective analysis that included a total of 82 CHD patients who had suitable image quality for left atrial strain measurement. According to the 2016 ASE/EACVI guidelines for the echocardiographic assessment of diastolic dysfunction, the patients were divided into three groups: normal left ventricular diastolic function group (n = 26), indeterminate left ventricular diastolic function (n = 36), and left ventricular diastolic dysfunction (LVDD) (n = 20). The left atrial conduit strain (LAScd), Left atrial contractile strain (LASct), left atrial reservoir strain (LASr) and its derived parameters, including LASI and left atrial filling index (LAFI), were compared among the three groups. Furthermore, we conduct a correlation analysis between LASI and left ventricular diastolic function in patients with CHD. RESULTS: LASr and LAScd in normal group were higher than those in indeterminate group, LASr and LAScd in indeterminate group were higher than those in LVDD group, LASI in normal group was lower than that in indeterminate group, and LASI in indeterminate group was lower than that in LVDD group (P < 0.001). LASct in both normal and indeterminate groups was higher than that in LVDD group (P < 0.05). The LAFI of normal group was lower than that of indeterminate group and LVDD group (P < 0.001). LASI was positively correlated with E/e'(r = 0.822) (P < 0.001). LASr and E/e' were negatively correlated (r = -0.637) (P < 0.001). CONCLUSION: LASI is closely related to the changes of left ventricular diastolic function in CHD patients.