Neuropathic Pain Induces Interleukin-1β Sensitive Bimodal Glycinergic Activity in the Central Amygdala

Neuropathic pain reduces GABA and glycine receptor (GlyR)-mediated activity in spinal and supraspinal regions associated with pain processing. Interleukin-1β (IL-1β) alters Central Amygdala (CeA) excitability by reducing glycinergic inhibition in a mechanism that involves the auxiliary β-subunit of GlyR (βGlyR), which is highly expressed in this region. However, GlyR activity and its modulation by IL-1β in supraspinal brain regions under neuropathic pain have not been studied. We performed chronic constriction injury (CCI) of the sciatic nerve in male Sprague Dawley rats, a procedure that induces hind paw plantar hyperalgesia and neuropathic pain. Ten days later, the rats were euthanized, and their brains were sliced. Glycinergic spontaneous inhibitory currents (sIPSCs) were recorded in the CeA slices. The sIPSCs from CeA neurons of CCI animals show a bimodal amplitude distribution, different from the normal distribution in Sham animals, with small and large amplitudes of similar decay constants. The perfusion of IL-1β (10 ng/mL) in these slices reduced the amplitudes within the first five minutes, with a pronounced effect on the largest amplitudes. Our data support a possible role for CeA GlyRs in pain processing and in the neuroimmune modulation of pain perception.     

Ferroptosis Modulation: Potential Therapeutic Target for Glioblastoma Treatment

Glioblastoma multiforme is a lethal disease and represents the most common and severe type of glioma. Drug resistance and the evasion of cell death are the main characteristics of its malignancy, leading to a high percentage of disease recurrence and the patients’ low survival rate. Exploiting the modulation of cell death mechanisms could be an important strategy to prevent tumor development and reverse the high mortality and morbidity rates in glioblastoma patients. Ferroptosis is a recently described type of cell death, which is characterized by iron accumulation, high levels of polyunsaturated fatty acid (PUFA)-containing phospholipids, and deficiency in lipid peroxidation repair. Several studies have demonstrated that ferroptosis has a potential role in cancer treatment and could be a promising approach for glioblastoma patients. Thus, here, we present an overview of the mechanisms of the iron-dependent cell death and summarize the current findings of ferroptosis modulation on glioblastoma including its non-canonical pathway. Moreover, we focused on new ferroptosis-inducing compounds for glioma treatment, and we highlight the key ferroptosis-related genes to glioma prognosis, which could be further explored. Thereby, understanding how to trigger ferroptosis in glioblastoma may provide promising pharmacological targets and indicate new therapeutic approaches to increase the survival of glioblastoma patients.     

Anatomical Laser Microdissection of the Ileum Reveals mtDNA Depletion Recovery in A Mitochondrial Neuro-Gastrointestinal Encephalomyopathy (MNGIE) Patient Receiving Liver Transplant

mitochondrial neuro-gastrointestinal encephalomyopathy (MNGIE) is a rare genetic disorder characterized by thymidine phosphorylase (TP) enzyme defect. The absence of TP activity induces the imbalance of mitochondrial nucleotide pool, leading to impaired mitochondrial DNA (mtDNA) replication and depletion. Since mtDNA is required to ensure oxidative phosphorylation, metabolically active tissues may not achieve sufficient energy production. The only effective life-saving approach in MNGIE has been the permanent replacement of TP via allogeneic hematopoietic stem cell or liver transplantation. However, the follow-up of transplanted patients showed that gut tissue changes do not revert and fatal complications, such as massive gastrointestinal bleeding, can occur. The purpose of this study was to clarify whether the reintroduction of TP after transplant can recover mtDNA copy number in a normal range. Using laser capture microdissection and droplet-digital-PCR, we assessed the mtDNA copy number in each layer of full-thickness ileal samples of a naive MNGIE cohort vs. controls and in a patient pre- and post-TP replacement. The treatment led to a significant recovery of gut tissue mtDNA amount, thus showing its efficacy. Our results indicate that a timely TP replacement is needed to maximize therapeutic success before irreversible degenerative tissue changes occur in MNGIE.     

A dual-affinity gene fusion system to express small recombinant proteins in a soluble form: expression and characterization of protein A deletion mutants

A novel gene fusion system to express and purify small recombinantproteins in Escherichia coli has been constructed. The conceptallows for affinity purification of soluble gene products bysequential albumin- and Zn²+-affinity chromatography. The dual-affinitysystem is well suited for expression of unstable proteins asonly full-length protein is obtained after purification andproteins gain proteolytic stability in the fusion protein. Herewe show that the dual-affinity approach can be used for theexpression of various unstable derivatives of a single IgG-bindingdomain based on staphylococcal protein A. Analysis of the proteolyticstabilities and the IgG-binding properties of the differentmutant proteins suggest that the model for the structure ofan IgG-binding domain must be re-evaluated.     

Mining statistically significant associations for exploratory analysis of human sleep data.

We introduce a specialized association rule mining technique that can extract patterns from complex sleep data comprising polysomnographic recordings, clinical summaries, and sleep questionnaire responses. The rules mined can describe associations among temporally annotated events and questionnaire or summary data; e.g., the likelihood that an occurrence of a rapid eye movement (REM) sleep stage during the second 100 sleep epochs of the night is associated with moderate caffeine intake. We use chi2 analysis to ensure statistical significance of the mined rules at the level P < 0.05. Our results, obtained by mining sleep-related data from 242 human subjects, reveal clinically interesting associations among the polysomnographic and summary variables. Our experience suggests that association mining may also be useful for selection of variables prior to using logistic regression.     

Shear elastic modulus estimation from indentation and SDUV on gelatin phantoms

Tissue mechanical properties such as elasticity are linked to tissue pathology state. Several groups have proposed shear wave propagation speed to quantify tissue mechanical properties. It is well known that biological tissues are viscoelastic materials; therefore, velocity dispersion resulting from material viscoelasticity is expected. A method called shearwave dispersion ultrasound vibrometry (SDUV) can be used to quantify tissue viscoelasticity by measuring dispersion of shear wave propagation speed. However, there is not a gold standard method for validation. In this study, we present an independent validation method of shear elastic modulus estimation by SDUV in three gelatin phantoms of differing stiffness. In addition, the indentation measurements are compared to estimates of elasticity derived from shear wave group velocities. The shear elastic moduli from indentation were 1.16, 3.40, and 5.6 kPa for a 7%, 10%, and 15% gelatin phantom, respectively. SDUV measurements were 1.61, 3.57, and 5.37 kPa for the gelatin phantoms, respectively. Shear elastic moduli derived from shear wave group velocities were 1.78, 5.2, and 7.18 kPa for the gelatin phantoms, respectively. The shear elastic modulus estimated from the SDUV, matched the elastic modulus measured by indentation. On the other hand, shear elastic modulus estimated by group velocity did not agree with indentation test estimations. These results suggest that shear elastic modulus estimation by group velocity will be bias when the medium being investigated is dispersive. Therefore, a rheological model should be used in order to estimate mechanical properties of viscoelastic materials.     

Waterproof,Breathable, and Antibacterial Self‐Powered e‐Textiles Based on Omniphobic Triboelectric Nanogenerators

Multifunctional electronic textiles (e‐textiles) incorporating miniaturized electronic devices will pave the way toward a new generation of wearable devices and human–machine interfaces. Unfortunately, the development of e‐textiles is subject to critical challenges, such as battery dependence, breathability, satisfactory washability, and compatibility with mass production techniques. This work describes a simple and cost‐effective method to transform conventional garments and textiles into waterproof, breathable, and antibacterial e‐textiles for self‐powered human–machine interfacing. Combining embroidery with the spray‐based deposition of fluoroalkylated organosilanes and highly networked nanoflakes, omniphobic triboelectric nanogenerators (R^F‐TENGs) can be incorporated into any fiber‐based textile to power wearable devices using energy harvested from human motion. R^F‐TENGs are thin, flexible, breathable (air permeability 90.5 mm s^?1), inexpensive to fabricate (<0.04$ cm^?2), and capable of producing a high power density (600 µW cm^?2). E‐textiles based on R^F‐TENGs repel water, stains, and bacterial growth, and show excellent stability under mechanical deformations and remarkable washing durability under standard machine‐washing tests. Moreover, e‐textiles based on R^F‐TENGs are compatible with large‐scale production processes and exhibit high sensitivity to touch, enabling the cost‐effective manufacturing of wearable human–machine interfaces.     

Application of kernel principal component analysis for single-lead-ECG-derived respiration

Recent studies show that principal component analysis (PCA) of heartbeats is a well-performing method to derive a respiratory signal from ECGs. In this study, an improved ECG-derived respiration (EDR) algorithm based on kernel PCA (kPCA) is presented. KPCA can be seen as a generalization of PCA where nonlinearities in the data are taken into account by nonlinear mapping of the data, using a kernel function, into a higher dimensional space in which PCA is carried out. The comparison of several kernels suggests that a radial basis function (RBF) kernel performs the best when deriving EDR signals. Further improvement is carried out by tuning the parameter σ(2) that represents the variance of the RBF kernel. The performance of kPCA is assessed by comparing the EDR signals to a reference respiratory signal, using the correlation and the magnitude squared coherence coefficients. When comparing the coefficients of the tuned EDR signals using kPCA to EDR signals obtained using PCA and the algorithm based on the R peak amplitude, statistically significant differences are found in the correlation and coherence coefficients (both p<0.0001), showing that kPCA outperforms PCA and R peak amplitude in the extraction of a respiratory signal from single-lead ECGs.