Development of a biomarker-based platform for comprehensive skin characterization using minimally invasive skin sampling and quantitative real-time PCR.

BACKGROUND: Classifying diverse skin types is crucial for promoting skin health. However, efficiently identifying and analyzing relevant biomarkers from a vast array of available genetic data is challenging. Therefore, this study aimed to develop a precise and efficient platform for analyzing specific skin biomarkers using quantitative real-time PCR (qRT-PCR) with the minimal invasive skin sampling method (MISSM). MATERIALS AND METHODS: MISSM was used for RNA extraction from skin samples, followed by qRT-PCR analysis to quantify the expression of 20 biomarkers associated with skin characteristics (four biomarkers each for five skin characteristics). Noninvasive measurements from 299 Korean participants were utilized to correlate biomarker expression with skin parameters. Statistical analyses were conducted between biomarker expression levels and noninvasive skin measurements to select the relatively best-performing biomarker for each skin characteristic. RESULTS: Collagen type 1 alpha 1 (COL1A1) and moesin (MSN) were identified as skin aging biomarkers. Krüppel-like factor 4 (KLF4) and serine peptidase inhibitor Kazal type 5 (SPINK5) were identified as skin dryness biomarkers, whereas melan-A (MLANA) was selected as a biomarker for understanding pigmentation dynamics. Myelin protein zero like 3 (MPZL3) and high mobility group box 2 (HMGB2) were identified as markers of oily skin and skin sensitivity, respectively. Statistically significant correlations were found between the biomarker expression levels and noninvasive skin characteristic measurements. CONCLUSION: This study successfully developed a platform for the precise evaluation of individual skin characteristics using MISSM and qRT-PCR biomarker analysis. By selecting biomarkers that correlate with noninvasive measurements of skin characteristics, we demonstrated the platform's efficacy in assessing diverse skin conditions.

Amniotic fluid proteomic analysis identifies IL1RL1, APOE, and NECTIN4 as new biomarkers for preterm birth.

BACKGROUND: Despite extensive research, the identification of effective biomarkers for early prediction of preterm birth (PTB) continues to be a challenging endeavor. This study aims to identify amniotic fluid (AF) protein biomarkers useful for the early diagnosis of PTB. METHODS: We initially identified the protein expression profiles in the AF of women with PTB (n = 22) and full-term birth (FTB, n = 22), from the First People's Hospital of Yunnan Province who underwent amniocentesis from November 2019 to February 2020, using mass spectrometry employing the data-independent acquisition (DIA) technique, and then analyzed differentially expressed proteins (DEPs). Subsequently, the least absolute shrinkage and selection operator (LASSO) and random forest analysis were employed to further screen the key proteins for PTB biomarker identification. The receiver operating characteristic (ROC) analysis, calibration plots, and decision curve analyses (DCA) were utilized to assess the discrimination and calibration of the key biomarkers. RESULTS: A total of 25 DEPs were identified between the PTB and FTB groups, comprising 13 up-regulated and 12 down-regulated proteins. Three key protein biomarkers for early PTB diagnosis were identified: IL1RL1 (interleukin-1 receptor-like 1), APOE (apolipoprotein E), and NECTIN4 (nectin cell adhesion molecule 4). The results of the ROC analysis showed that the area under the curve (AUC) of the three proteins combined as a biomarker for early diagnosis of PTB was 0.913 (95% CI: 0.823-1.000), with a sensitivity of 0.864 and a specificity of 0.955, both superior to those of the individual biomarkers. Bootstrap internal validation revealed a concordance index (C-index) of 0.878, with a sensitivity of 0.812 and a specificity of 0.773, indicating the robust predictive performance of these biomarkers. CONCLUSIONS: We identified three previously unexplored yet potentially useful protein biomarkers in AF for early PTB diagnosis: IL1RL1, APOE, and NECTIN4.

Identification of Optic Nerve-Related Biomarkers in Primary Open-Angle Glaucoma Based on Comprehensive Bioinformatics and Mendelian Randomization.

Purpose: Glaucoma is the primary cause of permanent vision loss worldwide. However, the pathogenesis of primary open-angle glaucoma (POAG), the main type of glaucoma, has not yet been completely understood. Methods: In our study, the POAG cohorts were obtained from the Gene Expression Omnibus (GEO) database (GSE45570). Biomarkers with diagnostic utility for POAG were identified through combining differentially expressed analysis, enrichment analysis, machine learning algorithms, and receiver operating characteristic (ROC) analysis. The regulatory networks (including a competing endogenous RNA (ceRNA) regulatory network and a small molecule compounds-mRNA network) were created. In addition, the Mendelian randomization (MR) analysis was used to identify exposures causally associated with POAG. Finally, the expression of the biomarkers was validated via real-time quantitative polymerase chain reaction (RT-qPCR). Results: The Gene Ontology (GO) items that the differentially expressed genes (DEGs) between POAG and control groups enriched were relevant to light stimulation and DNA methylation. A total of three light stimulation-related biomarkers (RAB8A, PRG3, and SMAD3) were identified, which had diagnostic value for POAG patients. Besides, the ceRNA regulatory network contained 88 nodes and 93 edges, and a small molecule compounds-mRNA network included 66 nodes and 76 edges. The MR results indicated a causal association between DNA methylation GrimAge acceleration and POAG. Additionally, the results of RT-qPCR revealed that the expression trend of RAB8A was consistent with that of GSE45570. Conclusions: Taken together, this study provides three light stimulation-related biomarkers (RAB8A, PRG3, and SMAD3) for the diagnosis of POAG, providing scientifically valuable insights for further studies of POAG. Translational Relevance: Discovering biomarkers that possess diagnostic significance for POAG has the potential to offer new insights into the pathogenesis of POAG and present novel objectives for clinical intervention.

Co-regulatory mechanisms and potential markers of oxidative stress-related genes in vitiligo and alopecia areata.

BACKGROUND: The specific role of oxidative stress (OS) in vitiligo and alopecia areata (AA) remains unclear. The aim of this study was to analyze and identify the key markers of OS in vitiligo and AA by bioinformatics. METHODS: We obtained vitiligo and AA datasets from gene expression omnibus (GEO) database. The difference-expressed genes of vitiligo and AA were identified by differential analysis, and the functions of difference-expressed genes were identified by gene ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), and Gene Set Enrichment Analysis (GSEA) enrichment analysis. Subsequently, Veen package was used to obtain the intersection genes of OS-related genes with vitiligo and AA. Finally, we used CIBERSORT to assess the infiltration of immune cells in vitiligo and AA. RESULTS: Through enrichment analysis, we found that vitiligo and AA were mainly enriched in cell cycle and cell adhesion molecular channels. We identified KLB and EIF3C as key genes in OS regulation of vitiligo and AA, and found that KLB and EIF3C participate in disease progression by regulating T cells and neutrophils. CONCLUSIONS: According to our findings, KLB and EIF3C play a crucial role in the progression and development of vitiligo and AA, which have been identified as biomarkers and target for early diagnosis of patients.

Identification of molecular targets and small drug candidates for Huntington's disease via bioinformatics and a network-based screening approach.

Huntington's disease (HD) is a gradually severe neurodegenerative ailment characterised by an increase of a specific trinucleotide repeat sequence (cytosine-adenine-guanine, CAG). It is passed down as a dominant characteristic that worsens over time, creating a significant risk. Despite being monogenetic, the underlying mechanisms as well as biomarkers remain poorly understood. Furthermore, early detection of HD is challenging, and the available diagnostic procedures have low precision and accuracy. The research was conducted to provide knowledge of the biomarkers, pathways and therapeutic targets involved in the molecular processes of HD using informatic based analysis and applying network-based systems biology approaches. The gene expression profile datasets GSE97100 and GSE74201 relevant to HD were studied. As a consequence, 46 differentially expressed genes (DEGs) were identified. 10 hub genes (TPM1, EIF2S3, CCN2, ACTN1, ACTG2, CCN1, CSRP1, EIF1AX, BEX2 and TCEAL5) were further differentiated in the protein-protein interaction (PPI) network. These hub genes were typically down-regulated. Additionally, DEGs-transcription factors (TFs) connections (e.g. GATA2, YY1 and FOXC1), DEG-microRNA (miRNA) interactions (e.g. hsa-miR-124-3p and has-miR-26b-5p) were also comprehensively forecast. Additionally, related gene ontology concepts (e.g. sequence-specific DNA binding and TF activity) connected to DEGs in HD were identified using gene set enrichment analysis (GSEA). Finally, in silico drug design was employed to find candidate drugs for the treatment HD, and while the possible modest therapeutic compounds (e.g. cortistatin A, 13,16-Epoxy-25-hydroxy-17-cheilanthen-19,25-olide, Hecogenin) against HD were expected. Consequently, the results from this study may give researchers useful resources for the experimental validation of Huntington's diagnosis and therapeutic approaches.

Fluorescence Lifetime Multiplexing (FLEX) for simultaneous high dimensional spatial biology in 3D.

Immunohistochemistry is a crucial method for detecting specific proteins within tissue samples, yet constrained to one biomarker per tissue section. Multiplexed immunofluorescence, while allowing simultaneous visualization of multiple proteins, faces limitations in the number of simultaneous fluorescent labels due to spectral overlap. Although cyclic immunofluorescence techniques have successfully broadened antibody staining capacities in a single tissue sample, they are plagued by time-consuming and labor-intensive procedures, sample degradation risks, and inability to scale beyond thin sections. In this study, we introduce the use of 3D confocal Fluorescence Lifetime Imaging Microscopy as a high-throughput, multiplexed immunofluorescence platform that can differentiate 11 or more biomarkers in 3D tissue volumes. Leveraging both spectral and lifetime information, this approach allows for practical spatial biology in thin sections that can readily scale to larger volumes of tissue. We believe that this highly multiplexed and versatile biomarker imaging platform will significantly expedite cancer research and enable new translational approaches in the future.

Dissection of potential anti-osteoporosis mechanism of isopsoralen - a quality control marker in Psoraleae Fructus - by metabolite profiling and network pharmacology.

RATIONALE: Isopsoralen (ISO), a quality control marker (Q-marker) in Psoraleae Fructus, is proven to present an obvious anti-osteoporosis effect. Until now, the metabolism and anti-osteoporosis mechanisms of ISO have not been fully elucidated, greatly restricting its drug development. METHODS: The metabolites of ISO in rats were profiled by using ultrahigh-performance liquid chromatography coupled with time-of-flight mass spectrometry. The potential anti-osteoporosis mechanism of ISO in vivo was predicted by using network pharmacology. RESULTS: A total of 15 metabolites were characterized in rats after ingestion of ISO (20 mg/kg/day, by gavage), including 2 in plasma, 12 in urine, 6 in feces, 1 in heart, 3 in liver, 1 in spleen, 1 in lung, 3 in kidney, and 2 in brain. The pharmacology network results showed that ISO and its metabolites could regulate AKT1, SRC, NFKB1, EGFR, MAPK3, etc., involved in the prolactin signaling pathway, ErbB signaling pathway, thyroid hormone pathway, and PI3K-Akt signaling pathway. CONCLUSIONS: This is the first time for revealing the in vivo metabolism features and potential anti-osteoporosis mechanism of ISO by metabolite profiling and network pharmacology, providing data for further verification of pharmacological mechanism.

Multiple Transcriptomic Analyses Explore Potential Synaptic Biomarker Rabphilin-3A for Alzheimer's Disease.

Alzheimer's disease (AD) is a prevalent neurodegenerative disorder afflicting the elderly population worldwide. The identification of potential gene candidates for AD holds promises for diagnostic biomarkers and therapeutic targets. Employing a comprehensive strategy, this study integrated transcriptomic data from diverse data sources, including microarray and single-cell datasets from blood and tissue samples, enabling a detailed exploration of gene expression dynamics. Through this thorough investigation, 19 notable candidate genes were found with consistent expression changes across both blood and tissue datasets, suggesting their potential as biomarkers for AD. In addition, single cell sequencing analysis further highlighted their specific expression in excitatory and inhibitory neurons, the primary functional units in the brain, underscoring their relevance to AD pathology. Moreover, the functional enrichment analysis revealed that three of the candidate genes were downregulated in synaptic signaling pathway. Further validation experiments significantly showed reduced levels of rabphilin-3A (RPH3A) in 3xTg-AD model mice, implying its role in disease pathogenesis. Given its role in neurotransmitter exocytosis and synaptic function, further investigation into RPH3A and its interactions with neurotrophic proteins may provide valuable insights into the complex molecular mechanisms underlying synaptic dysfunction in AD.

Preeclampsia: A comprehensive review.

Preeclampsia (PE) is a life-threatening disease of pregnancy and a prominent cause of neonatal and maternal mortality and morbidity. PE affects approximately 5-10% of pregnancies worldwide, posing significant risks to perinatal and maternal health. It is characterized by a variety of interconnected pathological cascades contributing to the stimulation of intravascular inflammation, oxidative stress (OS), endothelial cell activation, and syncytiotrophoblast stress that converge on a common pathway, ultimately resulting in disease progression. The present study was designed and executed to review the existing scientific literature, specifically focusing on the etiology (gestational diabetes mellitus and maternal obesity, insulin resistance, metabolic syndrome, maternal infection, periodontal disease, altered microbiome, and genetics), clinical presentations (hypertension, blood disorders, proteinuria, hepatic dysfunction, renal dysfunction, pulmonary edema, cardiac dysfunction, fetal growth restrictions, and eclampsia), therapeutic clinical biomarkers (creatinine, albuminuria, and cystatin C) along with their associations and mechanisms in PE. In addition, this study provides insights into the potential of nanomedicines for targeting these mechanisms for PE management and treatment. Inflammation, OS, proteinuria, and an altered microbiome are prominent biomarkers associated with progression and PE-related pathogenesis. Understanding the molecular mechanisms, exploring suitable markers, targeted interventions, comprehensive screening, and holistic strategies are critical to decreasing the incidence of PE and promoting maternal-fetal well-being. The present study comprehensively reviewed the etiology, clinical presentations, therapeutic biomarkers, and preventive potential of nanomedicines in the treatment and management of PE.

Oxidative and Antioxidative Biomarker Profiles in Neonatal Hypoxic-Ischemic Encephalopathy: Insights for Pathophysiology and Treatment Strategies.

BACKGROUND Neonatal hypoxic-ischemic encephalopathy (HIE) is a significant cause of perinatal and postnatal morbidity and mortality worldwide. Catalase (CAT) activity detection is used to determine levels of inflammation and oxidative stress. Glutathione (GSH) is the most critical non-enzymatic endogenous antioxidant. Lipid peroxidation levels marked after hypoxia can be detected based on the level of malondialdehyde (MDA). Ischemia-modified albumin (IMA) is considered a biomarker for cardiac ischemia and is known to increase in the liver, brain, and kidney in states of insufficient oxygenation. We aimed to explain the results and relations between the oxidant and antioxidants to detail oxidant-antioxidant balance and cellular mechanisms. MATERIAL AND METHODS Serum levels of IMA and MDA, as an oxidative stress marker, and CAT and GSH, as antioxidant enzymes, were measured in first blood samples of 59 neonates diagnosed with HIE, with pH <7, base excess >12, and APGAR scores. RESULTS Neonates who were ≥37 weeks of gestation and had hypoxia were included. Compared with healthy newborns (n=32), CAT was statistically significantly lower in the hypoxia group (P=0.0001), while MDA serum levels were significantly higher in neonates with hypoxia (P=0.01). There was no difference between hypoxic and healthy neonates in GSH and IMA measurements (P=0.054, P=0.19 respectively). CONCLUSIONS HIE pathophysiology involves oxidative stress and mitochondrial energy production failure. Explaining the pathways between oxidant-antioxidant balance and cell death, which explains the pathophysiology of HIE, is essential to develop treatment strategies that will minimize the effects of oxygen deprivation on other body organs, especially the brain.

Novel biomarkers in the saliva of healthy young males and females in a randomized crossover study on sedentary time: An exploratory analysis.

Several known biomarkers have been used to understand the physiological responses of humans to various short and long-term interventions such as exercise or dietary interventions. However, little exploratory work has been conducted to identify novel biomarkers in human saliva that could enable non-invasive physiological research to understand acute responses to interventions such as reducing sedentary time. The purpose of this study was to identify novel biomarkers in the saliva (cytokines, growth factors and vascular factors) that respond to prolonged (4 hours) and interrupted sitting (4 hours of sitting interrupted by 3 minutes of walking at 60% of maximal heart rate every 27 minutes) in young, healthy males and females. We also sought to determine whether responsive biomarkers would differ by sex. Participants (n = 24, 21.2 ± 2.2 years, 50% female) completed a prolonged sitting (PS) session and an interrupted sitting (IS) session in random order. Individual saliva samples were pooled into a male sample and a female sample to identify responsive biomarkers using a human cytokine antibody membrane array (42 targets). Several novel biomarkers were responsive in both sexes (e.g., IL-8, Angiogenin, VEGF, and EGF), in females only (e.g., TNF-α and IL-13), and in males only (e.g., IL-3, RANTES, and IL-12p40/p70). Importantly, several biomarkers appear to be responsive to the 4-hour prolonged and interrupted sitting sessions (e.g., TNF-α, IL-8, IL-3, RANTES, EGF, Angiogenin, and VEGF). This work highlights new directions for researchers aiming to investigate the effect of short-term or acute interventions on different physiological pathways using non-invasive methods. Our work clearly indicates that human saliva samples can provide a wealth of insight into physiological responses, and that a number of biomarkers can be used to understand changes induced by acute interventions such as interrupting prolonged sitting.

Machine learning-causal inference based on multi-omics data reveals the association of altered gut bacteria and bile acid metabolism with neonatal jaundice.

Early identification of neonatal jaundice (NJ) appears to be essential to avoid bilirubin encephalopathy and neurological sequelae. The interaction between gut microbiota and metabolites plays an important role in early life. It is unclear whether the composition of the gut microbiota and metabolites can be used as an early indicator of NJ or to aid clinical decision-making. This study involved a total of 196 neonates and conducted two rounds of "discovery-validation" research on the gut microbiome-metabolome. It utilized methods of machine learning, causal inference, and clinical prediction model evaluation to assess the significance of gut microbiota and metabolites in classifying neonatal jaundice (NJ), as well as the potential causal relationships between corresponding clinical variables and NJ. In the discovery stage, NJ-associated gut microbiota, network modules, and metabolite composition were identified by gut microbiome-metabolome association analysis. The NJ-associated gut microbiota was closely related to bile acid metabolites. By Lasso machine learning assessment, we found that the gut bacteria were associated with abnormal bile acid metabolism. The machine learning-causal inference approach revealed that gut bacteria affected serum total bilirubin and NJ by influencing bile acid metabolism. NJ-associated gut bile acids are potential biomarkers of NJ, and clinical prediction models constructed based on these biomarkers have some clinical effects and the model may be used for disease risk prediction. In the validation stage, it was found that intestinal metabolites can predict NJ, and the machine learning-causal inference approach revealed that bile acid metabolites affected NJ itself by affecting the total bilirubin content. Intestinal bile acid metabolites are potential biomarkers of NJ. By applying machine learning-causal inference methods to gut microbiome-metabolome association studies, we found NJ-associated intestinal bacteria and their network modules and bile acid metabolite composition. The important role of intestinal bacteria and bile acid metabolites in NJ was determined, which can predict the risk of NJ.

Association analysis of the intestinal microbiome-metabolome found that neonatal jaundice (NJ)-related intestinal microbiota, network modules and metabolite composition, and the intestinal microbiota are closely related to bile acid metabolites.Gut bacteria were found to affect serum total bilirubin (TBIL) and NJ by influencing bile acid metabolism through a machine learning-causal inference approach, and bile acid metabolites affected NJ itself by affecting the TBIL content.NJ-associated gut bacteria and bile acids are potential biomarkers of NJ, and clinical decision-making models based on these biomarkers have some clinical effects for disease risk prediction.

Machine learning and experimental validation of novel biomarkers for hypertrophic cardiomyopathy and cancers.

Hypertrophic cardiomyopathy (HCM) is a hereditary cardiac disorder marked by anomalous thickening of the myocardium, representing a significant contributor to mortality. While the involvement of immune inflammation in the development of cardiac ailments is well-documented, its specific impact on HCM pathogenesis remains uncertain. Five distinct machine learning algorithms, namely LASSO, SVM, RF, Boruta and XGBoost, were utilized to discover new biomarkers associated with HCM. A unique nomogram was developed using two newly identified biomarkers and subsequently validated. Furthermore, samples of HCM and normal heart tissues were gathered from our institution to confirm the variance in expression levels and prognostic significance of GATM and MGST1. Five novel biomarkers (DARS2, GATM, MGST1, SDSL and ARG2) associated with HCM were identified. Subsequent validation revealed that GATM and MGST1 exhibited significant diagnostic utility for HCM in both the training and test cohorts, with all AUC values exceeding 0.8. Furthermore, a novel risk assessment model for HCM patients based on the expression levels of GATM and MGST1 demonstrated favourable performance in both the training (AUC = 0.88) and test cohorts (AUC = 0.9). Furthermore, our study revealed that GATM and MGST1 exhibited elevated expression levels in HCM tissues, demonstrating strong discriminatory ability between HCM and normal cardiac tissues (AUC of GATM = 0.79; MGST1 = 0.86). Our findings suggest that two specific cell types, monocytes and multipotent progenitors (MPP), may play crucial roles in the pathogenesis of HCM. Notably, GATM and MGST1 were found to be highly expressed in various tumours and showed significant prognostic implications. Functionally, GATM and MGST1 are likely involved in xenobiotic metabolism and epithelial mesenchymal transition in a wide range of cancer types. GATM and MGST1 have been identified as novel biomarkers implicated in the progression of both HCM and cancer. Additionally, monocytes and MPP may also play a role in facilitating the progression of HCM.

Early asymptomatic prediction of potato soft rot disease using phytohormone-induced volatile biomarkers.

Potatoes (Solanum tuberosum L.) are one of the world's major staple crops. In stored potatoes, Pectobacterium carotovorum subsp carotovorum causes soft rot. As a result of the rapid spread of the disease during post-harvest storage, potato production suffers huge losses. By detecting disease early and controlling it promptly, losses can be minimized. The profile of volatiles of plants can be altered by phytopathogens. Identifying unique volatile organic compounds (VOCs) as biomarkers for early disease detection has attracted considerable research attention. This study compared the VOC profiles of healthy and soft rot inoculated potatoes (cv. "Kufri Pukhraj") over a time course using gas chromatography-mass spectrometry (GC-MS). It was found that there was a differential emission of 27 VOCs between healthy non-inoculated potatoes and soft rot inoculated potatoes. Among 27 VOCs, only five (1-octen-3-ol, 2-methylisoborneol, 3-octanone, 1,4-dimethyladamantane, and 2-methyl-2-bornene) were found exclusively in soft rot inoculated potatoes, suggesting them potential biomarker for non-destructive prediction of soft rot disease in potatoes. Reactive oxygen species (H2O2) and phytohormone methyl-jasmonate (MeJa) levels increased transiently on infection with soft rot. The analysis of the primary metabolism of soft rot infected tubers at three different stages suggests metabolic reprogramming that occurs at the early stage of infection, possibly leading to biomarker volatile emission. Based on these results, it appears that the initial potato-soft rot bacteria interaction initiates metabolic reprogramming mainly through H2O2 and the MeJa signalling pathway. In asymptomatic potatoes, these biomarkers may be promising candidates for non-destructive detection of soft rot at an early stage. These biomarkers can be used to develop an e-nose sensor to predict soft rot in the future.

Urine Metabolites Changes in Acute Myocardial Infarction Rats via Metabolomic Analysis.

OBJECTIVES: To screen biomarkers for forensic identification of acute myocardial infarction (AMI) by non-targeted metabolomic studies on changes of urine metabolites in rats with AMI. METHODS: The rat models of the sham surgery group, AMI group and hyperlipidemia + acute myocardial infarction (HAMI) group were established. Ultra-high performance liquid chromatography-mass spectrometry (UPLC-MS) was used to analyze the changes of urine metabolic spectrometry in AMI rats. Principal component analysis, partial least squares-discriminant analysis, and orthogonal partial least squares-discriminant analysis were used to screen differential metabolites. The MetaboAnalyst database was used to analyze the metabolic pathway enrichment and access the predictive ability of differential metabolites. RESULTS: A total of 40 and 61 differential metabolites associated with AMI and HAMI were screened, respectively. Among them, 22 metabolites were common in both rat models. These small metabolites were mainly concentrated in the niacin and nicotinamide metabolic pathways. Within the 95% confidence interval, the area under the curve (AUC) values of receiver operator characteristic curve for N8-acetylspermidine, 3-methylhistamine, and thymine were greater than 0.95. CONCLUSIONS: N8-acetylspermidine, 3-methylhistamine, and thymine can be used as potential biomarkers for AMI diagnosis, and abnormal metabolism in niacin and nicotinamide may be the main causes of AMI. This study can provide reference for the mechanism and causes of AMI identification.

Tear α-synuclein as a biomarker for Parkinson's disease: A systematic review and meta-analysis.

BACKGROUND: Parkinson's disease symptoms mostly manifest after significant and irreversible neuropathology. Hence, there is a need to identify biomarkers that can provide indications of disease before significant neuronal degeneration occurs. OBJECTIVE: To estimate the difference in the concentration of α-synuclein protein in tears between individuals with Parkinson's disease and healthy controls. DATA SOURCES: PubMed, Scopus, and Web of Science. The last database search was on December 20, 2023. STUDY ELIGIBILITY CRITERIA: Primary prospective studies in humans measuring the level of α-synuclein in tears and clinical outcomes reported using mean or median. PARTICIPANTS AND INTERVENTIONS: Individuals with Parkinson's disease and healthy controls. STUDY APPRAISAL AND SYNTHESIS METHODS: The risk of bias was assessed using the Newcastle-Ottawa Scale. The I2 statistic was used to estimate heterogeneity. The outcome measure was the difference in tear total and oligomeric α-synuclein. Mean difference (MD) was used to assess the outcome. The certainty of evidence was rated following the Grading of Recommendations Assessment and Development and Evaluation (GRADE) system. RESULTS: Three hundred twenty-seven Parkinson's disease and 312 healthy control subjects from five studies and 177 Parkinson's disease and 166 healthy control subjects from two studies were included in total α-synuclein levels and oligomeric α-synuclein levels analysis, respectively. Total α-synuclein level was not different between Parkinson's disease and healthy controls (MD = 0.02 ng/mL [95% confidence interval {CI}: 0.00 to 0.05 ng/mL; I2 = 90%; Z = 1.79; p=0.07; number of studies = 5; GRADE rating = very low]). Stratifying the data based on disease duration, total α-synuclein was higher in subjects with Parkinson's disease duration ≥7 years compared with healthy controls (MD = 0.04 ng/mL [95% CI: 0.03 to 0.05 ng/mL; I2 = 0%; Z = 8.24, p<0.00001; number of studies = 2; GRADE rating = low]) but not different between the two groups (MD = -0.12 ng/mL (95% CI: -0.38 to 0.15 ng/mL; I2 = 93%; Z = 0.84, p=0.40; number of studies = 3; GRADE rating = very low]). Oligomeric α-synuclein level was higher in Parkinson's disease compared with controls (MD = 6.50 ng/mL [95% CI: 2.79 to 10.20 ng/mL; I2 = 94%; Z = 3.44; p=0.0006; number of studies = 2; GRADE rating = very low]). LIMITATIONS: High heterogeneity between studies. Potential sources of heterogeneity could not be explored due to the limited number of studies. CONCLUSIONS AND IMPLICATIONS OF KEY FINDINGS: Tear α-synuclein has the potential to be a noninvasive biomarker for Parkinson's disease. Studies are, however, needed to increase certainty in the biomarker and establish how the protein's changes in tears correlate with Parkinson's disease progression and severity.

Microfluidic device: A versatile biosensor platform to multiplex aptamer-based detection of malaria biomarkers.

Plasmodium falciparum malaria remains a dominant infectious disease that affects Africa than the rest of the world, considering its associated cases and death rates. It's a febrile illness that produces several reliable biomarkers, for example, P. falciparum lactate dehydrogenase (PfLDH), P. falciparum Plasmodium glutamate dehydrogenase (PfGDH), and P. falciparum histidine-rich proteins (HRP-II) in blood circulatory system that can easily be employed as targets in rapid diagnostic tests (RDTs). In recent times, several DNA aptamers have been developed via SELEX technology to detect some specific malaria biomarkers (PfLDH, PvLDH, HRP-II, PfGDH) in a biosensor mode with good binding affinity properties to overcome the trend of cross-reactivity, limited sensitivity and stability problems that have been observed with immunodiagnostics. In this review, we summarized existing diagnostic methods and relevant biomarkers to suggest promising approaches to develop sensitive and species-specific multiplexed diagnostic devices enabling effective detection of malaria in complex biological matrices and surveillance in the endemic region.

Clinical Characteristic and Tear Film Biomarkers After Myopic FS-LASIK: 1-Year Prospective Follow-up.

PURPOSE: To assess the long-term (1-year) effect of myopic femtosecond laser-assisted in situ keratomileusis (FSLASIK) on clinical characteristics and tear film biomarkers. METHODS: Eighty eyes from 80 patients who underwent FSLASIK were evaluated. Ocular surface symptoms and signs were evaluated using specific questionnaires and tests. The corneal nerves and dendritic cells were examined using in vivo confocal microscopy. Corneal sensitivity was evaluated using a Cochet-Bonnet esthesiometer. Tear inflammatory cytokines and neuropeptides were evaluated using Luminex immunoassay. These examinations were performed preoperatively and at 1, 3, 6, and 12 months postoperatively. RESULTS: Seventy-three participants completed all follow-up visits. Following FS-LASIK, ocular symptoms and signs (except Schirmer I test) worsened at 1 month but corneal and conjunctival stainings improved by 3 months. The numbers of dendritic cells and activated dendritic cells increased at the 3-month postoperative visit and recovered to preoperative levels by the 6-month visit. Ocular symptoms and corneal sensitivity recovered to preoperative levels at the 12-month visit. Tear break-up time and corneal nerve morphology were not recovered to preoperative status at the 12-month visit. Interleukin (IL)-1ß, IL-17A, tumor necrosis factor-α, and substance P tear levels significantly increased at all postoperative visits compared to preoperative levels. Corneal staining scores positively correlated with tear IL-1ß and IL-17A levels, whereas corneal nerve morphology positively correlated with corneal sensitivity and negatively correlated with substance P levels. CONCLUSIONS: Although most clinical variables improved at 12 months postoperatively, some tear inflammatory cytokines and substance P remain altered beyond 12 months, indicating that ocular homeostasis is not completely recovered. [J Refract Surg. 2024;40(8):e508-e519.].

Integrating Multi-omics to Identify Age-Related Macular Degeneration Subtypes and Biomarkers.

Age-related macular degeneration (AMD) is one of the most common causes of irreversible vision loss in the elderly. Its pathogenesis is likely multifactorial, involving a complex interaction of metabolic and environmental factors, and remains poorly understood. Previous studies have shown that mitochondrial dysfunction and oxidative stress play a crucial role in the development of AMD. Oxidative damage to the retinal pigment epithelium (RPE) has been identified as one of the major mediators in the pathogenesis of age-related macular degeneration (AMD). Therefore, this article combines transcriptome sequencing (RNA-seq) and single-cell sequencing (scRNA-seq) data to explore the role of mitochondria-related genes (MRGs) in AMD. Firstly, differential expression analysis was performed on the raw RNA-seq data. The intersection of differentially expressed genes (DEGs) and MRGs was performed. This paper proposes a deep subspace nonnegative matrix factorization (DS-NMF) algorithm to perform a multi-layer nonlinear transformation on the intersection of gene expression profiles corresponding to AMD samples. The age of AMD patients is used as prior information at the network's top level to change the data distribution. The classification is based on reconstructed data with altered distribution. The types obtained significantly differ in scores of multiple immune-related pathways and immune cell infiltration abundance. Secondly, an optimal AMD diagnosis model was constructed using multiple machine learning algorithms for external and qRT-PCR verification. Finally, ten potential therapeutic drugs for AMD were identified based on cMAP analysis. The AMD subtypes identified in this article and the diagnostic model constructed can provide a reference for treating AMD and discovering new drug targets.

Aqueous humor cytokine levels in patients with subretinal fibrosis in neovascular age-related macular degeneration.

PURPOSE: To investigate aqueous humor cytokine levels in neovascular age-related macular degeneration (nAMD) patients with subretinal fibrosis and to explore the relationship between cytokine levels and disease severity. METHODS: The aqueous humor samples were collected from 16 eyes with subretinal fibrosis due to nAMD (SRFi group), 33 eyes with nAMD without subretinal fibrosis (nAMD group) and 28 eyes with cataract patients (control group). Clinical samples were analyzed for 5 cytokines,including vascular endothelial growth factor (VEGF), interleukin-6 (IL-6), basic fibroblast growth factor (bFGF), transforming growth factor-α (TGF-α), platelet-derived growth factor-BB (PDGF-BB). RESULTS: Aqueous humor cytokines VEGF and bFGF were significantly higher in nAMD patients than controls (all P < 0.05), and VEGF, bFGF and TGF-α levels were significantly higher in SRFi patients than controls (all P < 0.05). No significant differences in 4 cytokine levels were observed between nAMD and SRFi patients in aqueous humor. We also identified a positive correlation between the aqueous humor levels of IL-6 and VEGF in the SRFi group, while bFGF and TGF-α in the nAMD group. Moreover, VEGF levels were strongly related to BCVA, and bFGF levels were positively related to the maximum thickness of subretinal hyperreflective material (SHRM) in fibrosis due to nAMD. CONCLUSION: VEGF and bFGF levels in aqueous humor were elevated in macular neovascularization with and without subretinal fibrosis. TGF-α levels exclusively differed in neovascular AMD with fibrosis. Cytokines are distributed differently and play a synergistic role in different stages (angiogenesis and fibrogenesis) of nAMD. The bFGF levels could predict the negative prognosis in fibrosis due to nAMD.