Vitamin K2-7, also known as menaquinone-7 (MK-7) is a form of vitamin K that has health-beneficial effects in osteoporosis, cardiovascular disease, inflammation, cancer, Alzheimer’s disease, diabetes and peripheral neuropathy. Compared to vitamin K1 (phylloquinone), K2-7 is absorbed more readily and is more bioavailable. Clinical studies have unequivocally demonstrated the utility of vitamin K2-7 supplementation in ameliorating peripheral neuropathy, reducing bone fracture risk and improving cardiovascular health. We examine how undercarboxylated osteocalcin (ucOC) and matrix Gla protein (ucMGP) are converted to carboxylated forms (cOC and cMGP respectively) by K2-7 acting as a cofactor, thus facilitating the deposition of calcium in bones and preventing vascular calcification. K2-7 is beneficial in managing bone loss because it upregulates osteoprotegerin which is a decoy receptor for RANK ligand (RANKL) thus inhibiting bone resorption. We also review the evidence for the health-beneficial outcomes of K2-7 in diabetes, peripheral neuropathy and Alzheimer’s disease. In addition, we discuss the K2-7-mediated suppression of growth in cancer cells via cell-cycle arrest, autophagy and apoptosis. The mechanistic basis for the disease-modulating effects of K2-7 is mediated through various signal transduction pathways such as PI3K/AKT, MAP Kinase, JAK/STAT, NF-κB, etc. Interestingly, K2-7 is also responsible for suppression of proinflammatory mediators such as IL-1α, IL-1β and TNF-α. We elucidate various genes modulated by K2-7 as well as the clinical pharmacometrics of vitamin K2-7 including K2-7-mediated pharmacokinetics/pharmacodynamics (PK/PD). Further, we discuss the current status of clinical trials on K2-7 that shed light on dosing strategies for maximum health benefits. Taken together, this is a synthetic review that delineates the health-beneficial effects of K2-7 in a clinical setting, highlights the molecular basis for these effects, elucidates the clinical pharmacokinetics of K2-7, and underscores the need for K2-7 supplementation in the global diet.

Neuropathic pain is a chronic pain condition seen in patients with diabetic neuropathy, cancer chemotherapy-induced neuropathy, idiopathic neuropathy as well as other diseases affecting the nervous system. Only a small percentage of people with neuropathic pain benefit from current medications. The complexity of the disease, poor identification/lack of diagnostic and prognostic markers limit current strategies for the management of neuropathic pain. Multiple genes and pathways involved in human diseases can be regulated by microRNA (miRNA) which are small non-coding RNA. Several miRNAs are found to be dysregulated in neuropathic pain. These miRNAs regulate expression of various genes associated with neuroinflammation and pain, thus, regulating neuropathic pain. Some of these key players include adenylate cyclase (Ac9), toll-like receptor 8 (Tlr8), suppressor of cytokine signaling 3 (Socs3), signal transducer and activator of transcription 3 (Stat3) and RAS p21 protein activator 1 (Rasa1). With advancements in high-throughput technology and better computational power available for research in present-day pharmacology, biomarker discovery has entered a very exciting phase. We dissect the architecture of miRNA biological networks encompassing both human and rodent microRNAs involved in the development of neuropathic pain. We delineate various microRNAs, and their targets, that may likely serve as potential biomarkers for diagnosis, prognosis, and therapeutic intervention in neuropathic pain. miRNAs mediate their effects in neuropathic pain by signal transduction through IRAK/TRAF6, TLR4/NF-κB, TXIP/NLRP3 inflammasome, MAP Kinase, TGFβ and TLR5 signaling pathways. Taken together, the elucidation of the landscape of signature miRNA regulatory networks in neuropathic pain will facilitate the discovery of novel miRNA/target biomarkers for more effective management of neuropathic pain.

Diabetes is one of the most frequently occurring metabolic disorders, affecting almost one tenth of the global population. Despite advances in antihyperglycemic therapeutics, the management of diabetes is limited due to its complexity and associated comorbidities, including diabetic neuropathy, diabetic nephropathy and diabetic retinopathy. Noncoding RNAs (ncRNAs), including microRNAs (miRNAs) and long noncoding RNAs (lncRNAs), are involved in the regulation of gene expression as well as various disease pathways in humans. Several ncRNAs are dysregulated in diabetes and are responsible for modulating the expression of various genes that contribute to the ‘symptom complex’ in diabetes. We review various miRNAs and lncRNAs implicated in diabetes and delineate ncRNA biological networks as well as key ncRNA targets in diabetes. Further, we discuss the spatial regulation of ncRNAs and their role(s) as prognostic markers in diabetes. We also shed light on the molecular mechanisms of signal transduction with diabetes-associated ncRNAs and ncRNA-mediated epigenetic events. Lastly, we summarize clinical trials on diabetes-associated ncRNAs and discuss the functional relevance of the dysregulated ncRNA interactome in diabetes. This knowledge will facilitate the identification of putative biomarkers for the therapeutic management of diabetes and its comorbidities. Taken together, the elucidation of the architecture of signature ncRNA regulatory networks in diabetes may enable the identification of novel biomarkers in the discovery pipeline for diabetes, which may lead to better management of this metabolic disorder.

Text mining, also known as text data mining or text analytics, is a field of study that focuses on extracting meaningful information and knowledge from textual data. The rapid advancement of digital data acquisition techniques has resulted in an unprecedented volume of data. In fact, over 80 percent of the data generated today comprises unstructured or semi-structured formats. Extracting meaningful patterns and trends from such massive amounts of text data poses a significant challenge. Text mining addresses this challenge by extracting valuable and nontrivial patterns from vast collections of text documents. Various techniques and tools are available for mining text documents and uncovering valuable information to inform decision-making and future processing. Selecting the appropriate text mining technique is crucial as it can significantly enhance the speed and efficiency of retrieving valuable information, reducing the time and effort required. This paper provides a concise analysis and discussion of text mining techniques and their applications. As technology continues to advance, the availability of digital data continues to increase. A substantial portion, approximately 85 percent, of this data exists in unstructured textual form. Consequently, it has become imperative to develop improved techniques and algorithms to effectively extract useful and interesting information from these vast amounts of textual data. This has resulted in the emergence of information extraction and text mining as popular research areas dedicated to uncovering valuable and necessary information from textual data.