MicroRNA-21 Silencing in Diabetic Nephropathy: Insights on Therapeutic Strategies

In diabetes, possibly the most significant site of microvascular damage is the kidney. Due to diabetes and/or other co-morbidities, such as hypertension and age-related nephron loss, a significant number of people with diabetes suffer from kidney diseases. Improved diabetic care can reduce the prevalence of diabetic nephropathy (DN); however, innovative treatment approaches are still required. MicroRNA-21 (miR-21) is one of the most studied multipotent microRNAs (miRNAs), and it has been linked to renal fibrosis and exhibits significantly altered expression in DN. Targeting miR-21 offers an advantage in DN. Currently, miR-21 is being pharmacologically silenced through various methods, all of which are in early development. In this review, we summarize the role of miR-21 in the molecular pathogenesis of DN and several therapeutic strategies to use miR-21 as a therapeutic target in DN. The existing experimental interventions offer a way to rectify the lower miRNA levels as well as to reduce the higher levels. Synthetic miRNAs also referred to as miR-mimics, can compensate for abnormally low miRNA levels. Furthermore, strategies like oligonucleotides can be used to alter the miRNA levels. It is reasonable to target miR-21 for improved results because it directly contributes to the pathological processes of kidney diseases, including DN

Smart Consumer Wearables as Digital Diagnostic Tools: A Review.

The increasing usage of smart wearable devices has made an impact not only on the lifestyle of the users, but also on biological research and personalized healthcare services. These devices, which carry different types of sensors, have emerged as personalized digital diagnostic tools. Data from such devices have enabled the prediction and detection of various physiological as well as psychological conditions and diseases. In this review, we have focused on the diagnostic applications of wrist-worn wearables to detect multiple diseases such as cardiovascular diseases, neurological disorders, fatty liver diseases, and metabolic disorders, including diabetes, sleep quality, and psychological illnesses. The fruitful usage of wearables requires fast and insightful data analysis, which is feasible through machine learning. In this review, we have also discussed various machine-learning applications and outcomes for wearable data analyses. Finally, we have discussed the current challenges with wearable usage and data, and the future perspectives of wearable devices as diagnostic tools for research and personalized healthcare domains

Traumatic brain injury and immunological outcomes: the double-edged killer

Traumatic brain injury (TBI) is a significant cause of mortality and morbidity worldwide resulting from falls, car accidents, sports, and blast injuries. TBI is characterized by severe, life-threatening consequences due to neuroinflammation in the brain. Contact and collision sports lead to higher disability and death rates among young adults. Unfortunately, no therapy or drug protocol currently addresses the complex pathophysiology of TBI, leading to the long-term chronic neuroinflammatory assaults. However, the immune response plays a crucial role in tissue-level injury repair. This review aims to provide a better understanding of TBI's immunobiology and management protocols from an immunopathological perspective. It further elaborates on the risk factors, disease outcomes, and preclinical studies to design precisely targeted interventions for enhancing TBI outcomes

Mesenchymal stem cells as living anti-inflammatory therapy for COVID-19 related acute respiratory distress syndrome.

Coronavirus disease 2019 (COVID-19), a pandemic disease caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV2), is growing at an exponential rate worldwide. Manifestations of this disease are heterogeneous; however, advanced cases often exhibit various acute respiratory distress syndrome-like symptoms, systemic inflammatory reactions, coagulopathy, and organ involvements. A common theme in advanced COVID-19 is unrestrained immune activation, classically referred to as a cytokine storm , as well as deficiencies in immune regulatory mechanisms such as T regulatory cells. While mesenchymal stem cells (MSCs) themselves are objects of cytokine regulation, they can secrete cytokines to modulate immune cells by inducing anti-inflammatory regulatory Treg cells, macrophages and neutrophils; and by reducing the activation of T and B cells, dendritic and nature killer cells. Consequently, they have therapeutic potential for treating severe cases of COVID-19. Here we discuss the unique ability of MSCs, to act as a living anti-inflammatory , which can rebalance the cytokine/immune responses to restore equilibrium. We also discuss current MSC trials and present different concepts for optimization of MSC therapy in patients with COVID-19 acute respiratory distress syndrome

Binned Data Provide Better Imputation of Missing Time Series Data from Wearables.

The presence of missing values in a time-series dataset is a very common and well-known problem. Various statistical and machine learning methods have been developed to overcome this problem, with the aim of filling in the missing values in the data. However, the performances of these methods vary widely, showing a high dependence on the type of data and correlations within the data. In our study, we performed some of the well-known imputation methods, such as expectation maximization, k-nearest neighbor, iterative imputer, random forest, and simple imputer, to impute missing data obtained from smart, wearable health trackers. In this manuscript, we proposed the use of data binning for imputation. We showed that the use of data binned around the missing time interval provides a better imputation than the use of a whole dataset. Imputation was performed for 15 min and 1 h of continuous missing data. We used a dataset with different bin sizes, such as 15 min, 30 min, 45 min, and 1 h, and we carried out evaluations using root mean square error (RMSE) values. We observed that the expectation maximization algorithm worked best for the use of binned data. This was followed by the simple imputer, iterative imputer, and k-nearest neighbor, whereas the random forest method had no effect on data binning during imputation. Moreover, the smallest bin sizes of 15 min and 1 h were observed to provide the lowest RMSE values for the majority of the time frames during the imputation of 15 min and 1 h of missing data, respectively. Although applicable to digital health data, we think that this method will also find applicability in other domains

Binned Data Provide Better Imputation of Missing Time Series Data from Wearables

The presence of missing values in a time-series dataset is a very common and well-known problem. Various statistical and machine learning methods have been developed to overcome this problem, with the aim of filling in the missing values in the data. However, the performances of these methods vary widely, showing a high dependence on the type of data and correlations within the data. In our study, we performed some of the well-known imputation methods, such as expectation maximization, k-nearest neighbor, iterative imputer, random forest, and simple imputer, to impute missing data obtained from smart, wearable health trackers. In this manuscript, we proposed the use of data binning for imputation. We showed that the use of data binned around the missing time interval provides a better imputation than the use of a whole dataset. Imputation was performed for 15 min and 1 h of continuous missing data. We used a dataset with different bin sizes, such as 15 min, 30 min, 45 min, and 1 h, and we carried out evaluations using root mean square error (RMSE) values. We observed that the expectation maximization algorithm worked best for the use of binned data. This was followed by the simple imputer, iterative imputer, and k-nearest neighbor, whereas the random forest method had no effect on data binning during imputation. Moreover, the smallest bin sizes of 15 min and 1 h were observed to provide the lowest RMSE values for the majority of the time frames during the imputation of 15 min and 1 h of missing data, respectively. Although applicable to digital health data, we think that this method will also find applicability in other domains

Nanoparticle-mediated active and passive drug targeting in oral squamous cell carcinoma: current trends and advances

Oral squamous cell carcinoma (OSCC) is an invasive and highly malignant cancer with significant morbidity and mortality. Existing treatments including surgery, chemotherapy and radiation have poor overall survival rates and prognosis. The intended therapeutic effects of chemotherapy are limited by drug resistance, systemic toxicity and adverse effects. This review explores advances in OSCC treatment, with a focus on lipid-based platforms (solid lipid nanoparticles, nanostructured lipid carriers, lipid-polymer hybrids, cubosomes), polymeric nanoparticles, self-assembling nucleoside nanoparticles, dendrimers, magnetic nanovectors, graphene oxide nanostructures, stimuli-responsive nanoparticles, gene therapy, folic acid receptor targeting, gastrin-releasing peptide receptor targeting, fibroblast activation protein targeting, urokinase-type plasminogen activator receptor targeting, biotin receptor targeting and transferrin receptor targeting. This review also highlights oncolytic viruses as OSCC therapy candidates.<br/

Exploring the role of cerebrospinal fluid as analyte in neurologic disorders.

The cerebrospinal fluid (CSF) is a clear ultrafiltrate of blood that envelopes and protects the central nervous system while regulating neuronal function through the maintenance of interstitial fluid homeostasis in the brain. Due to its anatomic location and physiological functions, the CSF can provide a reliable source of biomarkers for the diagnosis and treatment monitoring of different neurological diseases, including neurodegenerative diseases such as Alzheimer\u27s disease, Parkinson\u27s disease, amyotrophic lateral sclerosis, and primary and secondary brain malignancies. The incorporation of CSF biomarkers into the drug discovery and development can improve the efficiency of drug development and increase the chances of success. This review aims to consolidate the current use of CSF biomarkers in clinical practice and explore future perspectives for the field