miR-10b-5p Rescues Diabetes and Gastrointestinal Dysmotility.

BACKGROUND & AIMS: Interstitial cells of Cajal (ICCs) and pancreatic ß cells require receptor tyrosine kinase (KIT) to develop and function properly. Degeneration of ICCs is linked to diabetic gastroparesis. The mechanisms linking diabetes and gastroparesis are unclear, but may involve microRNA (miRNA)-mediated post-transcriptional gene silencing in KIT+ cells. METHODS: We performed miRNA-sequencing analysis from isolated ICCs in diabetic mice and plasma from patients with idiopathic and diabetic gastroparesis. miR-10b-5p target genes were identified and validated in mouse and human cell lines. For loss-of-function studies, we used KIT+ cell-restricted mir-10b knockout mice and KIT+ cell depletion mice. For gain-of-function studies, a synthetic miR-10b-5p mimic was injected in multiple diabetic mouse models. We compared the efficacy of miR-10b-5p mimic treatment vs antidiabetic and prokinetic medicines. RESULTS: miR-10b-5p is highly expressed in ICCs from healthy mice, but drastically depleted in ICCs from diabetic mice. A conditional knockout of mir-10b in KIT+ cells or depletion of KIT+ cells in mice leads to degeneration of ß cells and ICCs, resulting in diabetes and gastroparesis. miR-10b-5p targets the transcription factor Krüppel-like factor 11 (KLF11), which negatively regulates KIT expression. The miR-10b-5p mimic or Klf11 small interfering RNAs injected into mir-10b knockout mice, diet-induced diabetic mice, and TALLYHO polygenic diabetic mice rescue the diabetes and gastroparesis phenotype for an extended period of time. Furthermore, the miR-10b-5p mimic is more effective in improving glucose homoeostasis and gastrointestinal motility compared with common antidiabetic and prokinetic medications. CONCLUSIONS: miR-10b-5p is a key regulator in diabetes and gastrointestinal dysmotility via the KLF11-KIT pathway. Restoration of miR-10b-5p may provide therapeutic benefits for these disorders.

Extracellular Matrix Biomarkers in Colorectal Cancer.

The cellular microenvironment composition and changes therein play an extremely important role in cancer development. Changes in the extracellular matrix (ECM), which constitutes a majority of the tumor stroma, significantly contribute to the development of the tumor microenvironment. These alterations within the ECM and formation of the tumor microenvironment ultimately lead to tumor development, invasion, and metastasis. The ECM is composed of various molecules such as collagen, elastin, laminin, fibronectin, and the MMPs that cleave these protein fibers and play a central role in tissue remodeling. When healthy cells undergo an insult like DNA damage and become cancerous, if the ECM does not support these neoplastic cells, further development, invasion, and metastasis fail to occur. Therefore, ECM-related cancer research is indispensable, and ECM components can be useful biomarkers as well as therapeutic targets. Colorectal cancer specifically, is also affected by the ECM and many studies have been conducted to unravel the complex association between the two. Here we summarize the importance of several ECM components in colorectal cancer as well as their potential roles as biomarkers.

Extreme physiological plasticity in a hibernating basoendothermic mammal, Tenrec ecaudatus.

Physiological plasticity allows organisms to respond to diverse conditions. However, can being too plastic actually be detrimental? Malagasy common tenrecs, Tenrec ecaudatus, have many plesiomorphic traits and may represent a basal placental mammal. We established a laboratory population of T. ecaudatus and found extreme plasticity in thermoregulation and metabolism, a novel hibernation form, variable annual timing, and remarkable growth and reproductive biology. For instance, tenrec body temperature (Tb) may approximate ambient temperature to as low as 12°C even when tenrecs are fully active. Conversely, tenrecs can hibernate with Tb of 28°C. During the active season, oxygen consumption may vary 25-fold with little or no change in Tb During the austral winter, tenrecs are consistently torpid but the depth of torpor may vary. A righting assay revealed that Tb contributes to but does not dictate activity status. Homeostatic processes are not always linked, e.g. a hibernating tenrec experienced a ∼34% decrease in heart rate while maintaining constant body temperature and oxygen consumption rates. Tenrec growth rates vary but young may grow ∼40-fold in the 5 weeks until weaning and may possess indeterminate growth as adults. Despite all of this profound plasticity, tenrecs are surprisingly intolerant of extremes in ambient temperature (<8 or >34°C). We contend that while plasticity may confer numerous energetic advantages in consistently moderate environments, environmental extremes may have limited the success and distribution of plastic basal mammals.

Application of Machine Learning Strategies to Model the Effects of Sevoflurane on Somatosensory-Evoked Potentials during Spine Surgery.

In this study, a small sample of patients' neuromonitoring data was analyzed using machine learning (ML) tools to provide proof of concept for quantifying complex signals. Intraoperative neurophysiological monitoring (IONM) is a valuable asset for monitoring the neurological status of a patient during spine surgery. Notably, this technology, when operated by neurophysiologists and surgeons familiar with proper alarm criteria, is capable of detecting neurological deficits. However, non-surgical factors, such as volatile anesthetics like sevoflurane, can negatively influence robust IONM signal generation. While sevoflurane has been shown to affect the latency and amplitude of somatosensory evoked potential (SSEP), a more complex and nuanced analysis of the SSEP waveform has not been performed. In this study, signal processing and machine learning techniques were used to more intricately characterize and predict SSEP waveform changes as a function of varying end-tidal sevoflurane concentration. With data from ten patients who underwent spinal procedures, features describing the SSEP waveforms were generated using principal component analysis (PCA), phase space curves (PSC), and time-frequency analysis (TFA). A minimum redundancy maximum relevance (MRMR) feature selection technique was then used to identify the most important SSEP features associated with changing sevoflurane concentrations. Once the features carrying the maximum amount of information about the majority of signal waveform variability were identified, ML models were used to predict future changes in SSEP waveforms. Linear regression, regression trees, support vector machines, and neural network ML models were then selected for testing. Using SSEP data from eight patients, the models were trained using a range of features selected during MRMR calculations. During the training phase of model development, the highest performing models were identified as support vector machines and regression trees. After identifying the highest performing models for each nerve group, we tested these models using the remaining two patients' data. We compared the models' performance metrics using the root mean square error values (RMSEs). The feasibility of the methodology described provides a general framework for the applications of machine learning strategies to further delineate the effects of surgical and non-surgical factors affecting IONM signals.

In-Human Report of S2 Alar-Iliac Screw Placement Using Augmented Reality Assistance.

BACKGROUND: S2 alar-iliac (S2AI) screws provide spinopelvic fixation with the advantages of minimized dissection, easier rod contouring, and decreased symptomatic screw-head prominence. However, placement of S2AI screws may be challenging because of the anatomy of the lumbosacral junction. Augmented reality is a nascent technology that may enhance placement of S2AI screws. OBJECTIVE: To report the first in-human placement of augmented reality (AR)-assisted S2 alar-iliac screws and evaluate the accuracy of screw placement. METHODS: A retrospective review was performed of patients who underwent AR-assisted S2AI screw placement. All surgeries were performed by 2 neurosurgeons using an AR head-mounted display (Xvision, Augmedics). Screw accuracy was analyzed in a blinded fashion by an independent neuroradiologist using the cortical breach grading scale. RESULTS: Twelve patients underwent AR-assisted S2AI screw placement for a total of 23 screws. Indications for surgery included deformity, degenerative disease, and tumor. Twenty-two screws (95.6%) were accurate-defined as grade 0 or grade 1. Twenty-one screws (91.3%) were classified as grade 0, 1 screw (4.3%) was grade 1, and 1 screw (4.3%) was grade 3. All breaches were asymptomatic. CONCLUSION: AR-assisted S2AI screw placement had an overall accuracy rate of 95.6% (grade 0 and grade 1 screws) in a cohort of 12 patients and 23 screws. This compares favorably with freehand and robotic placement. 1,2 AR enables spine surgeons to both better visualize anatomy and accurately place spinal instrumentation. Future studies are warranted to research the learning curve and cost analysis of AR-assisted spine surgery.