microRNA pathological mechanisms between Parkinson's disease, Alzheimer's disease, glaucoma and macular degeneration.

Reactive oxygen species (ROS) play an essential role in regulating various functions of organisms such as gene transcription, signalling transduction and immune response. However, overproduction of ROS can lead to oxidative stress, which is related to various ageing diseases including eye and brain degenerative diseases. Ocular measurements have recently been suggested as potential sources of biomarkers for the early detection of brain neurodegenerative diseases. MicroRNAs (miRNAs) are useful biomarkers for various diseases including degenerative diseases. miRNAs play an important role in the oxidative stress mechanisms of ageing diseases. In this paper, the role of miRNAs related to oxidative stress mechanisms in four ageing diseases, Parkinson's disease (PD), Alzheimer's disease (AD), glaucoma and age-related macular degeneration was reviewed. The common miRNA biomarkers related to the four diseases were also discussed. The results show that these eye and brain ageing diseases share many common miRNA biomarkers. It indicates that the ocular condition may be a prognostic biomarker for PD or AD patients. When a patient's eye condition changes, this can be a warning of a change in PD or AD status.

The Human Pre-miRNA Distance Distribution for Exploring Disease Association.

MicroRNAs (miRNAs), playing an important role in cell differentiation, development, gene regulation, and apoptosis, have attracted much attention in recent years. miRNAs were shown to be involved in the mechanisms of various diseases, and certainly, they can be employed as useful disease biomarkers. The phylogenetic tree analysis of miRNA biomarkers is a useful tool to investigate the association between various diseases as well as the association between viruses and disease. In addition to the phylogenetic tree analysis, a more advanced study is to use the miRNA distance distribution to evaluate the similarity of the miRNA biomarkers. The mature miRNA distance distribution based on mature miRNA sequences has been derived. The averages of the pairwise distances of miRNA biomarkers for several associated diseases were shown to be smaller than the overall mean of all miRNAs, which indicates the high similarity of miRNA biomarkers for associated diseases. In addition to the mature miRNA, the precursor miRNA (pre-miRNA) may be more useful to explore the similarity of miRNAs because the mature miRNA duplex is released from the pre-miRNA. Therefore, in this study, the distance distributions based on human pre-miRNA stem-loop sequences were derived. The 1917 human miRNA stem-loop sequences in the miRBase dataset were used to derive the pre-miRNA distance distribution, and this is the first study to provide the distance distribution based on the human pre-miRNAs. The similarity of miRNA biomarkers for several associated diseases or vaccines was examined using the derived distribution, and the results show that the similarity of pre-miRNA biomarkers may be a feasible way to help explore the disease association.

Multiple imputation confidence intervals for the mean of the discrete distributions for incomplete data.

Confidence intervals for the mean of discrete exponential families are widely used in many applications. Since missing data are commonly encountered, the interval estimation for incomplete data is an important problem. The performances of the existing multiple imputation confidence intervals are unsatisfactory. We propose modified multiple imputation confidence intervals to improve the existing confidence intervals for the mean of the discrete exponential families with quadratic variance functions. A simulation study shows that the coverage probabilities of the modified confidence intervals are closer to the nominal level than the existing confidence intervals when the true mean is near the boundaries of the parameter space. These confidence intervals are also illustrated with real data examples.

MicroRNAs, Parkinson's Disease, and Diabetes Mellitus.

Parkinson's disease (PD) is a neurodegenerative disorder that affects 1% of the population over the age of 60. Diabetes Mellitus (DM) is a metabolic disorder that affects approximately 25% of adults over the age of 60. Recent studies showed that DM increases the risk of developing PD. The link between DM and PD has been discussed in the literature in relation to different mechanisms including mitochondrial dysfunction, oxidative stress, and protein aggregation. In this paper, we review the common microRNA (miRNA) biomarkers of both diseases. miRNAs play an important role in cell differentiation, development, the regulation of the cell cycle, and apoptosis. They are also involved in the pathology of many diseases. miRNAs can mediate the insulin pathway and glucose absorption. miRNAs can also regulate PD-related genes. Therefore, exploring the common miRNA biomarkers of both PD and DM can shed a light on how these two diseases are correlated, and targeting miRNAs is a potential therapeutic opportunity for both diseases.

MicroRNAs, Multiple Sclerosis, and Depression.

Multiple sclerosis (MS) is a chronic disease of the central nervous system that affects the brain and spinal cord. There are several disease courses in MS including relapsing-remitting MS (RRMS), primary progressive MS (PPMS), and secondary progressive MS (SPMS). Up to 50% of MS patients experience depressive disorders. Major depression (MD) is a serious comorbidity of MS. Many dysfunctions including neuroinflammation, peripheral inflammation, gut dysbiosis, chronic oxidative and nitrosative stress, and neuroendocrine and mitochondrial abnormalities may contribute to the comorbidity between MS and MD. In addition to these actions, medical treatment and microRNA (miRNA) regulation may also be involved in the mechanisms of the comorbidity between MS and MD. In the study, I review many common miRNA biomarkers for both diseases. These common miRNA biomarkers may help further explore the association between MS and MD.

MicroRNAs and Apoptosis in Colorectal Cancer.

Colorectal cancer (CRC) is the third leading cause of cancer death in the world, and its incidence is rising in developing countries. Treatment with 5-Fluorouracil (5-FU) is known to improve survival in CRC patients. Most anti-cancer therapies trigger apoptosis induction to eliminate malignant cells. However, de-regulated apoptotic signaling allows cancer cells to escape this signaling, leading to therapeutic resistance. Treatment resistance is a major challenge in the development of effective therapies. The microRNAs (miRNAs) play important roles in CRC treatment resistance and CRC progression and apoptosis. This review discusses the role of miRNAs in contributing to the promotion or inhibition of apoptosis in CRC and the role of miRNAs in modulating treatment resistance in CRC cells.

Exploring microRNA Biomarker for Amyotrophic Lateral Sclerosis.

Amyotrophic lateral sclerosis (ALS) is among the severe neuro degenerative diseases that lack widely available effective treatments. As the disease progresses, patients lose the control of voluntary muscles. Although the neuronal degeneration is the cause of this disease, the failure mechanism is still unknown. In order to seek genetic mechanisms that initiate and progress ALS, the association of microRNA (miRNA) expression with this disease was considered. Serum miRNAs from healthy controls, sporadic ALS (sALS), familial ALS (fALS) and ALS mutation carriers were investigated. Principal component analysis (PCA)-based unsupervised feature extraction (FE) was applied to these serum miRNA profiles. As a result, we predict miRNAs that can discriminate patients from healthy controls with high accuracy. Thus, these miRNAs can be potential prognosis miRNA biomarkers for ALS.

Anti-NMDA Receptor Encephalitis and Vaccination.

Anti-N-methyl-d-aspartate (Anti-NMDA) receptor encephalitis is an acute autoimmune neurological disorder. The cause of this disease is often unknown, and previous studies revealed that it might be caused by a virus, vaccine or tumor. It occurs more often in females than in males. Several cases were reported to be related to vaccination such as the H1N1 vaccine and tetanus/diphtheria/pertussis and polio vaccines. In this study, we reported an anti-NMDA receptor encephalitis case that may be caused by Japanese encephalitis vaccination. To investigate the association between anti-NMDA receptor encephalitis and vaccination, we analyzed the phylogenetic relationship of the microRNAs, which significantly regulate these vaccine viruses or bacteria, and the phylogenetic relationship of these viruses and bacteria. This reveals that anti-NMDA receptor encephalitis may be caused by Japanese encephalitis vaccination, as well as H1N1 vaccination or tetanus/diphtheria/pertussis and polio vaccinations, from the phylogenetic viewpoint.

Predicting MicroRNA Biomarkers for Cancer Using Phylogenetic Tree and Microarray Analysis.

MicroRNAs (miRNAs) are shown to be involved in the initiation and progression of cancers in the literature, and the expression of miRNAs is used as an important cancer prognostic tool. The aim of this study is to predict high-confidence miRNA biomarkers for cancer. We adopt a method that combines miRNA phylogenetic structure and miRNA microarray data analysis to discover high-confidence miRNA biomarkers for colon, prostate, pancreatic, lung, breast, bladder and kidney cancers. There are 53 miRNAs selected through this method that either have potential to involve a single cancer's development or to involve several cancers' development. These miRNAs can be used as high-confidence miRNA biomarkers of these seven investigated cancers for further experiment validation. miR-17, miR-20, miR-106a, miR-106b, miR-92, miR-25, miR-16, miR-195 and miR-143 are selected to involve a single cancer's development in these seven cancers. They have the potential to be useful miRNA biomarkers when the result can be confirmed by experiments.

Variance estimation for nucleotide substitution models.

The current variance estimators for most evolutionary models were derived when a nucleotide substitution number estimator was approximated with a simple first order Taylor expansion. In this study, we derive three variance estimators for the F81, F84, HKY85 and TN93 nucleotide substitution models, respectively. They are obtained using the second order Taylor expansion of the substitution number estimator, the first order Taylor expansion of a squared deviation and the second order Taylor expansion of a squared deviation, respectively. These variance estimators are compared with the existing variance estimator in terms of a simulation study. It shows that the variance estimator, which is derived using the second order Taylor expansion of a squared deviation, is more accurate than the other three estimators. In addition, we also compare these estimators with an estimator derived by the bootstrap method. The simulation shows that the performance of this bootstrap estimator is similar to the estimator derived by the second order Taylor expansion of a squared deviation. Since the latter one has an explicit form, it is more efficient than the bootstrap estimator.

Anti-NMDA Receptor Encephalitis, Human Papillomavirus, and microRNA.

BACKGROUND: Anti-N-methyl-d-aspartate (Anti-NMDA) receptor encephalitis is a rare autoimmune disease, which is caused by antibodies attacking NMDA receptors in the brain. Previous studies revealed that this disorder might be induced by vaccination. Vaccination is the most useful strategy to prevent human or animal infectious diseases. MATERIALS AND METHODS: Although vaccines can produce immunity against diseases, at low risk, they may trigger serious adverse events. Anti-NMDA receptor encephalitis has been studied to be related to the H1N1 (influenza A virus subtype H1N1), tetanus/diphtheria/ pertussis and polio vaccine, Japanese encephalitis, yellow fever, and coronavirus disease 2019 (COVID-19) vaccination. Several cases have been reported that anti-NMDA receptor encephalitis could also be triggered by the human papillomavirus (HPV) vaccine. However, there is a lack of studies to investigate the underlying mechanism. RESULT: In this paper, the association between anti-NMDA receptor encephalitis and HPV vaccination is discussed in terms of their microRNA (miRNA) biomarkers. Phylogenetic tree and distance similarity analyses are used to explore the relationship between their miRNA biomarkers. The results show a higher degree of similarity between miRNA biomarkers associated with HPV and anti-NMDA receptor encephalitis or related vaccines when compared to the overall miRNAs. It indicates that while the risk of HPV triggering anti-NMDA receptor encephalitis is low, a connection between anti-NMDA receptor encephalitis and HPV vaccination cannot be ruled out. CONCLUSION: This finding suggests that in cases where individuals receiving HPV vaccination experience psychiatric or neurological symptoms, it should be considered to diagnose anti-NMDA receptor encephalitis, given the exclusion of other possible complications.

Beneficial medicinal effects and material applications of rose.

Rose is a beautiful and fragrant plant with a variety of medicinal and substance uses. Various parts of rose such as fruits, flowers, leaves, and bark can be used in various product development, including cosmetics, food, pharmaceuticals, and engineering. The medical benefits of roses include the treatment of inflammation, diabetes, dysmenorrhea, depression, stress, seizures, and aging. Rose water is precious beauty water for skin care and has antibacterial effects on various microbiota. The surface of a rose petal exhibits a hierarchical structure comprising microscale papillae, with each papilla further featuring intricate nanofolds. With this structural feature, rose petals have high water contact angles together with antagonistic wetting properties. The hierarchical structures of rose petals were shown to have anti-reflection and light-harvesting abilities, which have the potential to be materials for various electronic products. Rose petals are an excellent biomimetic/bioinspired material that can be applied to the popular material graphene. This paper reviews the medical function and material application of roses. During the COVID-19 pandemic, medical materials or food shortages have become a global issue. Natural biomaterials could be a good alternative. Roses, with so many benefits, definitely deserve more exploration and promotion.

RRSM with a data-dependent threshold for miRNA target prediction.

Predicting miRNA target genes is one of the important issues in bioinformatics. The correlation analysis is a widely used method for exploring miRNA targets through microarray data. However, the experimental results show that correlation analysis leads to large false positive or negative results. In addition, the correlation analysis is not appropriate when multiple miRNAs simultaneously regulate a gene. Recently, the relative R squared method (RRSM) has been proposed for miRNA target prediction, which is shown to be superior to some existing methods. To adopt the RRSM, we need first to set thresholds to select a proportion of potential targets. In the previous studies, the threshold is set to be fixed, which does not depend on the characteristic of a gene. Due to the diversity of the functions of genes, a data-dependent threshold may be more feasible in real data applications than a data-independent threshold. In this study, we propose a threshold selection method which is based on the distribution of the relative R squared statistic. The proposed method is shown to significantly improve the previous prediction results by selecting more experimentally validated targets.

Biomaterials in Medical Applications.

Natural biomaterials are materials extracted from living organisms or their by-products [...].

The Potential of Collagen Treatment for Comorbid Diseases.

Collagen, the most abundant protein in our bodies, plays a crucial role in maintaining the structural integrity of various tissues and organs. Beyond its involvement in skin elasticity and joint health, emerging research suggests that collagen may significantly impact the treatment of complex diseases, particularly those associated with tissue damage and inflammation. The versatile functions of collagen, including skin regeneration, improving joint health, and increasing bone strength, make it potentially useful in treating different diseases. To the best of my knowledge, the strategy of using collagen to treat comorbid diseases has not been widely studied. This paper aims to explore the potential of collagen in treating comorbid diseases, including rheumatoid arthritis, osteoarthritis, osteoporosis, psoriatic arthritis, sarcopenia, gastroesophageal reflux, periodontitis, skin aging, and diabetes mellitus. Collagen-based therapies have shown promise in managing comorbidities due to their versatile properties. The multifaceted nature of collagen positions it as a promising candidate for treating complex diseases and addressing comorbid conditions. Its roles in wound healing, musculoskeletal disorders, cardiovascular health, and gastrointestinal conditions highlight the diverse therapeutic applications of collagen in the context of comorbidity management.

COVID-19, Anti-NMDA Receptor Encephalitis and MicroRNA.

The coronavirus disease 2019 (COVID-19) pandemic has had an enormous impact on the world, affecting people's lifestyle, economy, and livelihood. Recently, with the development of vaccines, the number of infected cases has decreased. Many case reports have revealed that COVID-19 may induce other serious comorbidities such as anti-N-methyl-d-aspartate (anti-NMDA) receptor encephalitis. Anti-NMDA receptor encephalitis is an acute autoimmune disease that occurs more commonly in women than in men. To explore the association between COVID-19 and anti-NMDA receptor encephalitis, the microRNA (miRNA) biomarkers of COVID-19, anti-NMDA receptor encephalitis, and other related diseases from the literature are reviewed; then on the basis of these miRNA biomarkers, the relationship between COVID-19 and anti-NMDA receptor encephalitis is discussed. miRNAs are small non-coding RNAs that play important roles in cell differentiation, development, cell-cycle regulation, and apoptosis. miRNAs have been used as biological biomarkers for many diseases. The results in this study reveal that the relationship between anti-NMDA receptor encephalitis and COVID-19 infection or COVID-19 vaccination cannot be excluded; however, the risk that COVID-19 triggers the anti-NMDA receptor encephalitis is not high.

The distance distribution of human microRNAs in MirGeneDB database.

MicroRNAs (miRNAs) are small single-stranded non-coding RNAs around 22 nucleotide lengths found in organisms, playing an important role in cell differentiation, development, gene regulation, and apoptosis. The distance of disease miRNA biomarkers has been used to explore the association between various diseases as well as the association between virus and disease in the literature. To date, there have been no studies on deriving the distribution of the pairwise distance of human miRNAs. As the pairwise distance of miRNA biomarkers might be a useful tool in studying the disease association, in this paper, the distance distributions of human miRNAs were derived such that they could be used to measure the closeness between miRNAs. Two distance models were used to calculate the pairwise distances of 567 Homo sapiens miRNA genes accessed from the MirGeneDB database. These miRNA pairwise distances were fitted by the normal distribution, gamma distribution, empirical cumulative distribution, and the kernel density estimation method. This is the first study to provide the distance distribution of human miRNAs. The similarity of miRNA biomarkers for several diseases was examined using the derived distributions.

Exploring the Causal Effect of Constipation on Parkinson's Disease Through Mediation Analysis of Microbial Data.

Background and Aims: Parkinson's disease (PD) is a worldwide neurodegenerative disease with an increasing global burden, while constipation is an important risk factor for PD. The gastrointestinal tract had been proposed as the origin of PD in Braak's gut-brain axis hypothesis, and there is increasing evidence indicating that intestinal microbial alteration has a role in the pathogenesis of PD. In this study, we aim to investigate the role of intestinal microbial alteration in the mechanism of constipation-related PD. Methods: We adapted our data from Hill-Burns et al., in which 324 participants were enrolled in the study. The 16S rRNA gene sequence data were processed, aligned, and categorized using DADA2. Mediation analysis was used to test and quantify the extent by which the intestinal microbial alteration explains the causal effect of constipation on PD incidence. Results: We found 18 bacterial genera and 7 species significantly different between groups of constipated and non-constipated subjects. Among these bacteria, nine genera and four species had a significant mediation effect between constipation and PD. All of them were short-chain fatty acid (SCFA)-producing bacteria that were substantially related to PD. Results from the mediation analysis showed that up to 76.56% of the effect of constipation on PD was mediated through intestinal microbial alteration. Conclusion: Our findings support that gut dysbiosis plays a critical role in the pathogenesis of constipation-related PD, mostly through the decreasing of SCFA-producing bacteria, indicating that probiotics with SCFA-producing bacteria may be promising in the prevention and treatment of constipation-related PD. Limitations: 1) Several potential confounders that should be adjusted were not provided in the original dataset. 2) Our study was conducted based on the assumption of constipation being the etiology of PD; however, constipation and PD may mutually affect each other. 3) Further studies are necessary to explain the remaining 23.44% effect leading to PD by constipation.

A Pilot Study Exploring the Association of Entacapone, Gut Microbiota, and the Subsequent Side Effects in Patients With Parkinson's Disease.

Background and Aims: Entacapone, one of the most common drugs distributed among patients with Parkinson's disease, is a peripherally acting catechol-O-methyltransferase (COMT) inhibitor that is used in addition to levodopa to control symptoms. However, there have been negative effects reported against entacapone, namely, gastrointestinal (GI) problems and drowsiness. In this pilot study, we aim to examine the hypothesis that the discomfort induced by entacapone might be originated from the shift of microbial composition by adjusting the effect of levodopa. Methods: The population in this pilot study consisted of 13 PD patients treated with levodopa only and 11 with both levodopa and entacapone. The 16S rRNA gene sequence data were processed, aligned, and categorized using the DADA2. Alpha diversity indices for Observed, Chao1, Shannon, and Simpson metrics were calculated with Phyloseq 1.32.0. Dissimilarities were calculated using unweighted unique fraction metrics (Unifrac), weighted Unifrac, and Canberra distance. Functional differences were calculated by PICRUSt2 based on the KEGG database. Results: Results of 16S rRNA sequencing analysis showed that while entacapone did not influence the species richness, the composition of the microbial community shifted considerably. Relative abundances of bacteria related to constipation and other GI disorders also altered significantly. Functional enrichment analysis revealed changes in the metabolic activity of alanine, aspartate, and glutamate. These amino acids are related to common side effects of entacapone such as auditory hallucinations, fatigue, and nightmare. Conclusion: Our findings provide testable hypothesis on the cause of unpleasant side effects of entacapone, which in the long run could possibly be reduced through gut microbiota manipulation.

Confidence intervals for the substitution number in the nucleotide substitution models.

In the nucleotide substitution model for molecular evolution, a major task in the exploration of an evolutionary process is to estimate the substitution number per site of a protein or DNA sequence. The usual estimators are based on the observation of the difference proportion of the two nucleotide sequences. However, a more objective approach is to report a confidence interval with precision rather than only providing point estimators. The conventional confidence intervals used in the literature for the substitution number are constructed by the normal approximation. The performance and construction of confidence intervals for evolutionary models have not been much investigated in the literature. In this article, the performance of these conventional confidence intervals for one-parameter and two-parameter models are explored. Results show that the coverage probabilities of these intervals are unsatisfactory when the true substitution number is small. Since the substitution number may be small in many situations for an evolutionary process, the conventional confidence interval cannot provide accurate information for these cases. Improved confidence intervals for the one-parameter model with desirable coverage probability are proposed in this article. A numerical calculation shows the substantial improvement of the new confidence intervals over the conventional confidence intervals.