Early Identification Research Articles

Emerging Biohybrids of Aptamer-Based Nano-Biosensing Technologies for Effective Early Cancer Detection.

Cancer is a leading global cause of mortality, which underscores the imperative of early detection for improved patient outcomes. Biorecognition molecules, especially aptamers, have emerged as highly effective tools for early and accurate cancer cell identification. Aptamers, with superior versatility in synthesis and modification, offer enhanced binding specificity and stability compared with conventional antibodies. Hence, this article reviews diagnostic strategies employing aptamer-based biohybrid nano-biosensing technologies, focusing on their utility in detecting cancer biomarkers and abnormal cells. Recent developments include the synthesis of nano-aptamers using diverse nanomaterials, such as metallic nanoparticles, metal oxide nanoparticles, carbon-derived substances, and biohybrid nanostructures. The integration of these nanomaterials with aptamers significantly enhances sensitivity and specificity, promising innovative and efficient approaches for cancer diagnosis. This convergence of nanotechnology with aptamer research holds the potential to revolutionize cancer treatment through rapid, accurate, and non-invasive diagnostic methods.

Association Between Multisensory Impairment and Depression Among Older Adults: A Population-Based Analysis.

In this study, we examine how impairments in vision, hearing, touch, and olfaction relate to depression in older adults, considering both individual and multisensory impairments (MSIs). Analysis of cross-sectional data from a longitudinal investigation involving black and white older adults aged 70 to 79 at enrollment. We studied 1640 black and white participants in the Health ABC study using complete sensory evaluation data from years 3 to 5. Our MSI assessment utilized data obtained for visual acuity, hearing perception, olfactory performance, and tactile function. We performed multivariable logistic regression analyses to examine the associations between the presence of individual and MSIs and depression which was defined as the presence of antidepressants prescribed for depression, or a Center for Epidemiological Studies Depression Scale score of greater than 10. We observed a possible dose-response relationship between the number of sensory impairments and depression. In adjusted models, when compared to no impairments, vision (odds ratio [OR] = 1.45, 95% confidence interval [CI]: 1.09-1.93) and hearing impairments (OR = 1.49, 95% CI: 1.11-1.99) were significantly associated with depression, whereas olfaction (OR = 1.11, 95% CI: 0.83-1.47) and tactile impairments (OR = 1.28, 95% CI: 0.96-1.70) were not. Participants with 3 sensory impairments had a higher rate of depression (OR = 2.05, 95% CI: 1.22-3.54) compared to those without impairments, and this risk increased further for those with 4 sensory impairments (OR = 2.95, 95% CI: 1.48-5.88). The findings suggest that individuals with MSI represent a high-risk population for depression, warranting close monitoring to screen for depression. The study emphasizes the importance of considering multiple sensory impairments in the context of mental health and supports the early identification and monitoring of depression in this population.

Exploration and Validation of Lead Molecules against Yellow Fever through High Throughput Virtual Screening and Molecular Dynamics Simulation

Background: Yellow fever (YF) is a mosquito-borne flaviviral hemorrhagic fever (VHF) that causes severe hepatitis, renal failure, bleeding, and quick terminal events such as shock and multi-organ failure. There are currently no particular anti-viral medications for the management of the YF virus (YFV). Despite the availability of a commercial YFV vaccination, there are roughly 30,000 fatalities globally each year, with instances rising over the previous 20 years. After being translocated into the endoplasmic reticulum lumen, glycosylated NS1 resides as a membrane-associated dimer, where it is required for viral genome replication. The secreted hexamer NS1 has a role in immune evasion and pathogenesis and has been discovered as a possible diagnostic marker for the early identification of viral infections. Objective: The main aim of this study is to analyze the small molecule as a potent drug candidate against the target NS1 protein. Methods: In this study, Computational approaches, including high throughput virtual screening, molecular docking, and dynamics simulation, were carried out against the target NS1 protein using three different chemical libraries Enamine, Asinex, and NCI. The selected lead compounds were validated through HOMO-LUMO analysis, ADME prediction, and Toxicity parameters to analyze the biological and pharmacological properties of the lead small molecules. Results: From the result, it was concluded that the leads possessed the highest docking scores, interacting with the binding residues, and were stable in the simulation period. Conclusion: Overall findings revealed that the lead three small molecules could act as the potential drug candidate for the target NS1 protein to inhibit the diseasing efficacy of Yellow fever.

A Review on the Skin Cancer Detection Using Deep Learning Algorithms and LBP

Abstract: Skin cancer development is one of the conventional deadly disease types, with its cases expanding around the whole world. On the off chance that it's not analyzed in the early stages, it can prompt metastases, bringing about high death rates. Skin disease can be relieved whenever it's identified early. Thus, convenient and exact determination of such malignant growths is a key exploration objective. The early and precise identification of skin cancer has been suggested to be aided by deep-learning algorithms. The paper reviews new research articles based on deep learning methods and feature extraction techniques using LBP for skin cancer classification.

Chronic Kidney Disease Prediction Using Machine Learning

Abstract: Chronic kidney disease (CKD) is a progressive condition in which the kidneys lose their ability to function effectively over time. Individuals with hypertension, diabetes, or a family history of CKD are at increased risk, emphasizing the importance of early detection for effective intervention and management. Recent research has focused on employing machine learning techniques, including Ant Colony Optimization (ACO) and Support Vector Machine (SVM) classifiers, to predict CKD presence using a minimal set of features. This study aims to optimize predictive accuracy through advanced machine learning methodologies, facilitating timely and targeted healthcare interventions for at-risk individuals. By analyzing relevant clinical data, predictive models developed in this research offer promising avenues for early identification of CKD, enabling proactive disease management strategies. The integration of machine learning techniques in CKD prediction not only enhances predictive accuracy but also contributes to advancing personalized healthcare. Early detection allows for the implementation of preventive measures and personalized treatment plans, ultimately improving patient outcomes and reducing the burden on healthcare systems. The proposed methodology seeks to optimize predictive accuracy, aiding in timely and targeted healthcare interventions.

Obstructive Sleep Apnea and Its Cardiac Implications in the United States: An Age-Stratified Analysis Between Young andOlder Adults.

Obstructive sleep apnea (OSA) is a common sleep disorder associated with cardiovascular risks. This study aimed to assess the prevalence of probable OSA and its relationship with cardiovascular risks and diseases focusing on age-stratified young adults (20-40 years) and older (>40 years). The study used a cross-sectional design, analyzing data from the National Health and Nutrition Examination Survey conducted between 2013 and 2018, comprising 9887 community-dwelling adults aged ≥20 years. Probable OSA was determined on the basis of self-report of OSA-related symptoms (eg, snoring, gasping/breath cessation while sleeping). Cardiovascular risk factors, including hypertension, diabetes, hyperlipidemia, and metabolic syndrome, were evaluated according to established guidelines. Cardiovascular diseases (CVDs) included self-reported heart conditions, including congestive heart failure, coronary heart disease, angina, heart attacks, and strokes. Individuals with probable OSA showed a significantly higher prevalence of health conditions, including hypertension (adjusted prevalence ratio [aPR], 1.19; P<0.001), diabetes (aPR, 1.17; P: 0.01), metabolic syndrome (aPR, 1.14; P<0.001), heart attack (aPR, 1.63; P<0.01), stroke (aPR, 1.41; P: 0.03), and any CVD event (aPR, 1.36; P: 0.01) after adjusting for relevant factors. Young adults with probable OSA showed higher prevalence rates of any CVD events (aPR, 3.44; P<0.001), hypertension (aPR, 1.45; P<0.001), metabolic syndrome (aPR, 1.25; P<0.001), and angina (aPR, 10.39; P<0.001). The study suggests early identification and management of OSA in individuals at risk for CVD. While cross-sectional, it emphasizes that health care providers should recognize OSA as significantly associated with CVDs and its precursor risks in young adults, stressing proactive care and screening to reduce CVD risk in this population.

Cardiovascular risk factors among older persons with cognitive frailty in middle income country

BACKGROUND Cognitive frailty, characterized by the coexistence of cognitive impairment and physical frailty, represents a multifaceted challenge in the aging population. The role of cardiovascular risk factors in this complex interplay is not yet fully understood. AIM To investigate the relationships between cardiovascular risk factors and older persons with cognitive frailty by pooling data from two cohorts of studies in Malaysia. METHODS A comprehensive approach was employed, with a total of 512 community-dwelling older persons aged 60 years and above, involving two cohorts of older persons from previous studies. Datasets related to cardiovascular risks, namely sociodemographic factors, and cardiovascular risk factors, including hypertension, diabetes, hypercholesterolemia, anthropometric characteristics and biochemical profiles, were pooled for analysis. Cognitive frailty was defined based on the Clinical Dementia Rating scale and Fried frailty score. Cardiovascular risk was determined using Framingham risk score. Statistical analyses were conducted using SPSS version 21. RESULTS Of the study participants, 46.3% exhibited cognitive frailty. Cardiovascular risk factors including hypertension (OR:1.60; 95%CI: 1.12-2.30), low fat-free mass (OR:0.96; 95%CI: 0.94-0.98), high percentage body fat (OR:1.04; 95%CI: 1.02-1.06), high waist circumference (OR:1.02; 95%CI: 1.01-1.04), high fasting blood glucose (OR:1.64; 95%CI: 1.11-2.43), high Framingham risk score (OR:1.65; 95%CI: 1.17-2.31), together with sociodemographic factors, i.e. , being single (OR 3.38; 95%CI: 2.26-5.05) and low household income (OR 2.18; 95%CI: 1.44-3.30) were found to be associated with cognitive frailty. CONCLUSION Cardiovascular-risk specific risk factors and sociodemographic factors were associated with risk of cognitive frailty, a prodromal stage of dementia. Early identification and management of cardiovascular risk factors, particularly among specific group of the population might mitigate the risk of cognitive frailty, hence preventing dementia.

Network Hyperexcitability in Early-Stage Alzheimer's Disease: Evaluation of Functional Connectivity Biomarkers in a Computational Disease Model.

There is increasing evidence from animal and clinical studies that network hyperexcitability (NH) may be an important pathophysiological process and potential target for treatment in early Alzheimer's disease (AD). Measures of functional connectivity (FC) have been proposed as promising biomarkers for NH, but it is unknown which measure has the highest sensitivity for early-stage changes in the excitation/inhibition balance. We aim to test the performance of different FC measures in detecting NH at the earliest stage using a computational approach. We use a whole brain computational model of activity dependent degeneration to simulate progressive AD pathology and NH. We investigate if and at what stage four measures of FC (amplitude envelope correlation corrected [AECc], phase lag index [PLI], joint permutation entropy [JPE] and a new measure: phase lag time [PLT]) can detect early-stage AD pathophysiology. The activity dependent degeneration model replicates spectral changes in line with clinical data and demonstrates increasing NH. Compared to relative theta power as a gold standard the AECc and PLI are shown to be less sensitive in detecting early-stage NH and AD-related neurophysiological abnormalities, while the JPE and the PLT show more sensitivity with excellent test characteristics. Novel FC measures, which are better in detecting rapid fluctuations in neural activity and connectivity, may be superior to well-known measures such as the AECc and PLI in detecting early phase neurophysiological abnormalities and in particular NH in AD. These markers could improve early diagnosis and treatment target identification.

Clinical and genetic analysis of three children with Hyperekplexia

To explore the clinical and genetic characteristics of three children with Hyperekplexia. Three children who were diagnosed with Hyperekplexia at the Third Affiliated Hospital of Zhengzhou University between June 2018 and March 2020 were selected as the study subjects. Clinical data of the three children were collected. All children were subjected to whole exome sequencing. Pathogenicity of candidate variants were verified by Sanger sequencing and bioinformatic analysis. The three children were all males, and had presented exaggerated startle reflexes and generalized stiffness in response to unexpected auditory or tactile stimulation, or had frequent traumatic falls following exaggerated startle. All children had shown positive nose-tapping reflex, though EEG and cranial MRI exams were all negative. Whole exome sequencing revealed that two children had harbored homozygous variants of the GLRB gene, of which the c.1017_c.1018insAG (p.G340Rfs*14) was unreported previously. The third child had harbored compound heterozygous variants of the GLRA1 gene, among which the c.1262T>A (p.IIe421Asn) variant showed an unreported autosomal recessive inheritance. All children had responded well to clonazepam treatment. Patients with Hyperekplexia have typical clinical manifestations. Early clinical identification and genetic analysis can facilitate their diagnosis.

Multiple primary tumors in children: a clinicopathological analysis of four cases

Objective: To investigate the clinicopathological features of children with metachronous or synchronous primary tumors and to identify related genetic tumor syndromes. Methods: The clinicopathological data of 4 children with multiple primary tumors diagnosed in the Xinhua Hospital, Shanghai Jiaotong University School of Medicine, Shanghai, China from 2011 to 2023 were collected. The histological, immunophenotypic and molecular characteristics were examined using H&E staining, immunohistochemical staining, PCR, Sanger sequencing and next-generation sequencing (NGS). The patients were followed up. Results: Case 1 was an 8-year-old boy with the adrenal cortical carcinoma, and 5 years later a poorly differentiated gastric adenocarcinoma was detected. Case 2 was a 2-year-old boy, presented with a left ventricular choroid plexus carcinoma, and a hepatoblastoma was detected 8 months later. Case 3 was a 9-month-old girl, diagnosed with renal rhabdoid tumor first and intracranial atypical teratoid/rhabdoid tumor (AT/RT) 3 months later. Case 4 was a 7-year-old boy and had a sigmoid colon adenocarcinoma 3 years after the diagnosis of a glioblastoma. The morphology and immunohistochemical features of the metachronous or synchronous primary tumors in the 4 cases were similar to the corresponding symptom-presenting/first-diagnosed tumors. No characteristic germ line mutations were detected in cases 1 and 2 by relevant molecular detection, and the rhabdoid tumor predisposition syndrome was confirmed in case 3 using NGS. Case 4 was clearly related to constitutional mismatch repair deficiency as shown by the molecular testing and clinical features. Conclusions: Childhood multiple primary tumors are a rare disease with histological morphology and immunophenotype similar to the symptom-presenting tumors. They are either sporadic or associated with a genetic (tumor) syndrome. The development of both tumors can occur simultaneously (synchronously) or at different times (metachronously). Early identification of the children associated with genetic tumor syndromes can facilitate routine tumor screening and early treatment.

Columbia-Suicide Severity Rating Scale (C-SSRS) Reveals High Rates of Suicidality in 602 Patients With Cleft and Craniofacial Conditions.

Little is known about rates of suicidal ideation and behavior among youth with cleft lip and/or palate (CLP) and other craniofacial conditions. Records of patients ages 6 and older who were administered the Columbia-Suicide Severity Rating Scale (C-SSRS) Lifetime Version during routine multidisciplinary cleft or craniofacial team visits between 2019 and 2023 were examined. Demographics information, C-SSRS data, and diagnoses were assessed with statistics including t tests, the Fisher exact test, and odds ratios. A total of 1140 C-SSRS questionnaires across 602 (433 CLP and 169 craniofacial) patients with an average age of 11.2±3.7 years were included. Eighty-four (13.6%) patients endorsed lifetime suicidal ideation, 9 (1.5%) had at least one instance of suicidal behavior, 30 (5.0%) endorsed nonsuicidal self-injury, and 2 (0.3%) engaged in self-injurious behavior. Compared with CLP, those with other craniofacial conditions had similar odds of endorsing suicidal ideation and behavior (P≥0.05). Compared with those with isolated cleft palates, CLP had greater odds of endorsing suicidal ideation and behavior, though those differences were not significant (P≥0.05). Incidence of suicidality was unchanged before, during, and after the COVID-19 pandemic (P≥0.05). Dividing patients by sex or insurance type revealed no difference in suicidality (P≥0.05). Patients with CLP and craniofacial conditions have a high incidence of suicidal ideation and behavior, though levels are similar between these groups. Suicidality in these patients was not negatively impacted by the COVID-19 pandemic. Early identification of safety risks and psychosocial challenges through regular screening can facilitate connection with appropriate clinical interventions.

Correlation between newborn weight and serum BCAAs in pregnant women with diabetes

BackgroundBranched-chain amino acids (BCAAs), including leucine, isoleucine, and valine, are essential amino acids for mammals. Maternal BCAAs during pregnancy have been associated with newborn development. Meanwhile, BCAAs have been tightly linked with insulin resistance and diabetes in recent years. Diabetes in pregnancy is a common metabolic disorder. The current study aims to assess the circulating BCAA levels in pregnant women with diabetes and their relationship with neonatal development.MethodsThe serum concentrations of BCAAs and their corresponding branched-chain α-keto acids (BCKAs) catabolites in 33 pregnant women with normal glucose tolerance, 16 pregnant women with type 2 diabetes before pregnancy (PDGM), and 15 pregnant women with gestational diabetes mellitus (GDM) were determined using a liquid chromatography system coupled to a mass spectrometer. The data were tested for normal distribution and homogeneity of variance before statistical analysis. Correlations were computed with the Pearson correlation coefficient.ResultsThe maternal serum BCAAs and BCKAs levels during late pregnancy were higher in women with PGDM than those in healthy women. Meanwhile, the circulating BCAAs and BCKAs showed no significant changes in women with GDM compared with those in healthy pregnant women. Furthermore, the circulating BCAA and BCKA levels in women with PGDM were positively correlated with the weight of the newborn. The circulating leucine level in women with GDM was positively correlated with the weight of the newborn. BCAA and BCKA levels in healthy pregnant women showed no correlation with newborn weight.ConclusionsThe serum BCAAs in pregnant women with diabetes, which was elevated in PGDM but not GDM, were positively correlated with newborn weight. These findings highlight potential approaches for early identification of high-risk individuals and interventions to reduce the risk of adverse pregnancy outcomes.

Outcomes of Combined Opioids and Benzodiazepines Consumption in Elderly Trauma: A Retrospective Cohort Study.

Acute substance intoxication is associated with traumatic injury and worse hospital outcomes. The objective of this study was to evaluate the association between simultaneous opioids and benzodiazepines (OB) use and hospital outcomes in elderly trauma patients. We performed a retrospective analysis using the American College of Surgeons Trauma Quality Improvement Program (ACS-TQIP) 2017 database. We included trauma patients (age ≥ 65years) examined by urine toxicology within 24hours of presentation. The primary outcome was in-hospital mortality. Secondary outcomes included hospital and ICU lengths of stay (HLOS AND ICULOS), in-hospital complications (eg, ventilator-associated pneumonia), unplanned intubation, and duration of mechanical ventilation. Patients were stratified being both positive for opioids and benzodiazepines (OB+) or not (OB-) based on having positive or negative drug screen for both drugs, respectively. A 1:1 propensity score matching was performed controlling for demographics (eg, age and sex), comorbidities (eg, alcoholism), and injury characteristics. Of 77,311 tested patients, 849 OB+ were matched to OB- patients. Compared to OB- group, OB+ patients were more likely to have unplanned intubation (26 [3.1%] vs 8 [0.9%], P = 0.002) and had prolonged HLOS (≥2days: 683 [84.0%] vs 625 [77.8%], P = 0.002). There were no differences in all other outcomes (P > 0.05). The OB intake is associated with higher incidence of unplanned intubation and longer HLOS in elderly trauma patients. Early identification of elderly trauma patient with OB+ can help provide necessary pharmacologic and behavioral interventions to treat their substance use and potentially improve outcomes.

The role of biomarkers in early identification of acute kidney injury among non-critically ill patients.

Prediction and/or early identification of acute kidney injury (AKI) and individuals at greater risk remains of great interest in clinical medicine. Acute kidney injury continues to be a common complication among hospitalized patients, with an incidence ranging from 6 to 58%, depending on the setting.Aim of this study was to determine the performance of Insulin-like growth factor binding protein-7 (IGFBP7), tissue metallopeptidase inhibitor 2 (TIMP2), and urinary neutrophil gelatinase-associated lipocalin (uNGAL) in early detection of AKI among non-critically ill patients. In this prospective observational study at Mayo Clinic Hospitals in Rochester, Minnesota, USA, non-critically ill patients admitted from the emergency department between October 31st, 2016 and May 1st, 2018, who had an acute kidney injury (AKI) probability of 5% or higher were included. Biomarkers were measured in residual urine samples collected in the emergency department. The primary outcome was biomarker performance in predicting AKI development within the first 72h. Among 368 included patients, the mean age was 79 ± 12years, and 160 (43%) were male. Acute kidney injury occurred in 62 (17%) patients; 11.5% stage 1, 2.5% stage 2, and 3% stage 3. Twelve patients (3%) died during hospitalization and 102 (28%) within nine months after admission. The median uNGAL and IGFBP7-TIMP2 were 57 [20-236ng/ml], and 0.3 [0.1-0.8], respectively. The C-statistic of uNGAL and IGFBP7-TIMP2 of > 0.3 and > 2.0 for AKI prediction were 0.56, 0.54, and 0.53, respectively. In a model where one point is assigned to each marker of AKI (elevated serum creatinine, IGFBP7-TIMP2 > 0.3, and uNGAL), a higher score correlated with higher nine-month mortality [OR of 1.32 per point (95% CI 1.02-1.71)]. Among non-critically ill hospitalized patients, the performance of uNGAL and IGFBP7-TIMP2 for AKI prediction within 72h of admission was modest. This suggests a limited role for these biomarkers in AKI risk stratification among non-critically ill patients. Key learning points What was known Acute kidney injury (AKI) is a common complication among hospitalized patients. It is associated with increased morbidity and mortality. Various clinical prediction models and biomarkers have been developed to identify patients in special populations (such as ICU and cardiac surgery) who are at risk of AKI and diagnose AKI early. This study adds The performance of the biomarkers uNGAL, TIMP-2, and IGFBP-7 in predicting AKI within 72h of admission in non-critically ill patients was modest. However, these biomarkers were found to have a prognostic value for predicting 9-month mortality. One potential application of these biomarkers is identifying patients at higher AKI risk before exposing them to nephrotoxic agents. Potential impact This study provides evidence regarding the real-world performance of current FDA-approved biomarkers (uNGAL, TIMP-2, and IGFBP-7) for predicting acute kidney injury (AKI) within 72h of hospital admission among noncritically ill patients. While the performance of these biomarkers for predicting short-term AKI was modest, they may have a prognostic value for predicting 9-month mortality.

Early identification of Parkinson's disease with anxiety based on combined clinical and MRI features.

To identify cortical and subcortical volume, thickness and cortical area features and the networks they constituted related to anxiety in Parkinson's disease (PD) using structural magnetic resonance imaging (sMRI), and to integrate multimodal features based on machine learning to identify PD-related anxiety. A total of 219 patients with PD were retrospectively enrolled in the study. 291 sMRI features including cortical volume, subcortical volume, cortical thickness, and cortical area, as well as 17 clinical features, were extracted. Graph theory analysis was used to explore structural networks. A support vector machine (SVM) combination model, which used both sMRI and clinical features to identify participants with PD-related anxiety, was developed and evaluated. The performance of SVM models were evaluated. The mean impact value (MIV) of the feature importance evaluation algorithm was used to rank the relative importance of sMRI features and clinical features within the model. 17 significant sMRI variables associated with PD-related anxiety was used to build a brain structural network. And seven sMRI and 5 clinical features with statistically significant differences were incorporated into the SVM model. The comprehensive model achieved higher performance than clinical features or sMRI features did alone, with an accuracy of 0.88, a precision of 0.86, a sensitivity of 0.81, an F1-Score of 0.83, a macro-average of 0.85, a weighted-average of 0.92, an AUC of 0.88, and a result of 10-fold cross-validation of 0.91 in test set. The sMRI feature right medialorbitofrontal thickness had the highest impact on the prediction model. We identified the brain structural features and networks related to anxiety in PD, and developed and internally validated a comprehensive model with multimodal features in identifying.

Synergizing Chemical Structures and Bioassay Descriptions for Enhanced Molecular Property Prediction in Drug Discovery.

The precise prediction of molecular properties can greatly accelerate the development of new drugs. However, in silico molecular property prediction approaches have been limited so far to assays for which large amounts of data are available. In this study, we develop a new computational approach leveraging both the textual description of the assay of interest and the chemical structure of target compounds. By combining these two sources of information via self-supervised learning, our tool can provide accurate predictions for assays where no measurements are available. Remarkably, our approach achieves state-of-the-art performance on the FS-Mol benchmark for zero-shot prediction, outperforming a wide variety of deep learning approaches. Additionally, we demonstrate how our tool can be used for tailoring screening libraries for the assay of interest, showing promising performance in a retrospective case study on a high-throughput screening campaign. By accelerating the early identification of active molecules in drug discovery and development, this method has the potential to streamline the identification of novel therapeutics.

Stitched vision transformer for age-related macular degeneration detection using retinal optical coherence tomography images.

Age-related macular degeneration (AMD) is an eye disease that leads to the deterioration of the central vision area of the eye and can gradually result in vision loss in elderly individuals. Early identification of this disease can significantly impact patient treatment outcomes. Furthermore, given the increasing elderly population globally, the importance of automated methods for rapidly monitoring at-risk individuals and accurately diagnosing AMD is growing daily. One standard method for diagnosing AMD is using optical coherence tomography (OCT) images as a non-invasive imaging technology. In recent years, numerous deep neural networks have been proposed for the classification of OCT images. Utilizing pre-trained neural networks can speed up model deployment in related tasks without compromising accuracy. However, most previous methods overlook the feasibility of leveraging pre-existing trained networks to search for an optimal architecture for AMD staging on a new target dataset. In this study, our objective was to achieve an optimal architecture in the efficiency-accuracy trade-off for classifying retinal OCT images. To this end, we employed pre-trained medical vision transformer (MedViT) models. MedViT combines convolutional and transformer neural networks, explicitly designed for medical image classification. Our approach involved pre-training two distinct MedViT models on a source dataset with labels identical to those in the target dataset. This pre-training was conducted in a supervised manner. Subsequently, we evaluated the performance of the pre-trained MedViT models for classifying retinal OCT images from the target Noor Eye Hospital (NEH) dataset into the normal, drusen, and choroidal neovascularization (CNV) classes in zero-shot settings and through five-fold cross-validation. Then, we proposed a stitching approach to search for an optimal model from two MedViT family models. The proposed stitching method is an efficient architecture search algorithm known as stitchable neural networks. Stitchable neural networks create a candidate model in search space for each pair of stitchable layers by inserting a linear layer between them. A pair of stitchable layers consists of layers, each selected from one input model. While stitchable neural networks had previously been tested on more extensive and general datasets, this study demonstrated that stitching networks could also be helpful in smaller medical datasets. The results of this approach indicate that when pre-trained models were available for OCT images from another dataset, it was possible to achieve a model in 100 epochs with an accuracy of over 94.9% in classifying images from the NEH dataset. The results of this study demonstrate the efficacy of stitchable neural networks as a fine-tuning method for OCT image classification. This approach not only leads to higher accuracy but also considers architecture optimization at a reasonable computational cost.

Patterns of kidney function and risk assessment in a nationwide laboratory database: the Brazilian CHECK-CKD study

BackgroundChronic kidney disease (CKD) is a global health problem with rising prevalence, morbidity, mortality, and associated costs. Early identification and risk stratification are key to preventing progression to kidney failure. However, there is a paucity of data on practice patterns of kidney function assessment to guide the development of improvement strategies, particularly in lower-income countries.MethodsA retrospective observational analysis was conducted in a nationwide laboratory database in Brazil. We included all adult patients with at least one serum creatinine assessment between June 2018 and May 2021. Our primary objective was to determine the proportion of patients with estimated glomerular filtration rate (eGFR) evaluations accompanied by predicted levels of urinary albumin-to-creatinine ratio (pACR) assessments within 12 months.ResultsOut of 4,5323,332 serum creatinine measurements, 42% lacked pACR measurements within 12 months. Approximately 10.8% of tests suggested CKD, mostly at stage 3a. The proportion of serum creatinine exams paired with pACR assessment varied according to the CKD stage. Internal Medicine, Cardiology, and Obstetrics/Gynecology were the specialties requesting most of the creatinine tests. Nephrology contributed with only 1.1% of serum creatinine requests for testing.ConclusionOur findings reveal that a significant proportion of individuals with a creatinine test lack an accompanying urinary albuminuria measurement in Brazil, contrary to the recommendations of the international guidelines. Non-Nephrologists perform most kidney function evaluations, even among patients with presumable advanced CKD. This highlights the urge to incorporate in clinical practice the early detection of CKD and to encourage more collaborative multidisciplinary care to improve CKD management.

Magnetic Resonance Imaging of Central Nervous System Manifestations of Type 1 Neurofibromatosis: Pictorial Review and Retrospective Study of Their Frequency in a Cohort of Patients.

Background: type 1 neurofibromatosis (NF1) is the most common neurocutaneous disorder, and it is an inherited condition that causes a tumour predisposition. Central nervous system (CNS) manifestations are a significant cause of morbidity and mortality in NF1. We provide a pictorial review of neuroradiological features of NF1, with emphasis on magnetic resonance imaging (MRI), and we assess the frequency of those features on a cohort of NF1 patients. Methods: we retrospectively evaluated all patients with a diagnosis of NF1 who underwent MRI of the spine and brain in our centre over a period of almost 5 years. A total of 74 patients were enrolled, 28 males and 46 females, with a mean age of 21 ± 12.67 years. The frequency of CNS manifestations encountered in our cohort of NF1 patients was assessed and compared with the data found in other studies published in the literature. Results: many of our findings were in line with the literature, and possible interpretations for those that turned out to be different were suggested in the discussion. Conclusion: imaging plays a central role in the diagnosis and management of NF1, and the knowledge of CNS manifestations could be critical for its early detection and identification, such as for treatment planning and prognostic implications.

Serum proteomic analysis uncovers novel serum biomarkers for depression.

The identification of depression primarily relies on the clinical symptoms and psychiatric evaluation of the patient, in the absence of objective and quantifiable biomarkers within clinical settings. This study aimed to explore potential serum biomarkers associated with depression. Serum samples from a training group comprising 48 depression patients and 48 healthy controls underwent proteomic analysis. Magnetic bead-based weak cation exchange (MB-WCX) and MALDI-TOF-MS were used in combination. To screen the differential peaks, ClinProTools software was employed. The proteins were identified using LC-MS/MS. ELISA was employed to confirm the expression of entire protein in the serum of the verification cohort, which encompassed 48 individuals who had been diagnosed with Depression and 48 healthy controls who were collected prospectively. Subsequently, logistic regression analysis was conducted to determine the diagnostic efficacy of the aforementioned predictors. Five potential biomarker peaks indicating depression were identified in serum samples (peak 1, m/z: 1868.21; peak 2, m/z: 1062.35; peak 3, m/z: 1452.12; peak 4, m/z: 1208.72; peak 5, m/z: 1619.58). All of these peaks had higher expression in the pre-therapy group and were confirmed to be Tubulin beta chain (TUBB), Inter-alpha-trypsin inhibitor heavy chain H4 (ITIH4), Complement component 3 (C3), and Complement C4A precursor (C4A) by ELISA validation. Multivariate logistic regression analysis revealed that serum levels of TUBB, ITIH4, C3, and C4A were significant independent risk factors for the development of depression. Depression is a prevalent psychiatric condition. Timely detection is challenging, resulting in poor prognoses for patients. Our study on plasma proteomics for depression demonstrated that TUBB, ITIH4, C3, and C4A differentiate between depression patients and healthy controls. The proteins that were identified could potentially function as biomarkers for the diagnosis of depression. Pinpointing these biomarkers could enable early identification of depression, which would advance precise treatment.