Notes

[Kidney injury inside COVID-19].
Bladder pain syndrome (BPS) is a devastating urologic condition characterized by irritative bladder symptoms, pelvic pain, and dyspareunia. First-line treatment includes dietary, self-care and behavioral modifications. The ancient practice of yoga is well suited to treat BPS, but evidence is lacking on its use.

To investigate the feasibility and efficacy of an integrated yoga module on BPS outcomesas measured by self-reported questionnaires from baseline to 3 months after therapy.

This was a prospective single-arm study of 8 patients who underwent 3 months of integrated yoga therapy. The treatment module was performed 3 to 4 times weekly at home with 1 session performed weekly in-office during the first month to ensure proper performance of postures. Patients completed questionnaires (Pelvic Pain and Urgency/Frequency Patient Symptom Scale [PUF], Pelvic Floor Impact Questionnaire - short form 7 [PFIQ-7], Short Form 36 questionnaire [SF-36], Pittsburgh Sleep Quality Index [PSQI]) at baseline and 3 monthsa module against a control group.Glioma is the most common malignant tumor in the central nervous system. Evidence shows that clinical efficacy of immunotherapy is closely related to the tumor microenvironment. This study aims to establish a microenvironment-related genes (MRGs) model to predict the prognosis of patients with Grade II/III gliomas. Gene expression profile and clinical data of 459 patients with Grade II/III gliomas were extracted from The Cancer Genome Atlas. Then according to the immune/stromal scores generated by the ESTIMATE algorithm, the patients were scored one by one. Weighted gene co-expression network analysis (WGCNA) was used to construct a gene co-expression network to identify potential biomarkers for predicting the prognosis of patients. When adjusting clinical features including age, histology, grading, IDH status, we found that these features were independently associated with survival. The predicted value of the prognostic model was then verified in 440 samples in CGGA part B dataset and 182 samples in CGGA part C dataset by univariate and multivariate cox analysis. The clinical samples of 10 patients further confirmed our signature. Biricodar purchase Our findings suggested the eight-MRGs signature identified in this study are valuable prognostic predictors for patients with Grade II/III glioma.
Non-alcoholic fatty liver disease (NAFLD) is the most common chronic liver disorder worldwide. Multiple metabolic disorders, such as hyperlipidemia, hyperglycemia, insulin resistance and obesity, have been reportedly associated with NAFLD, but little is known about the detailed mechanisms.

Here, we explored the effects of multiple metabolic disorders, especially hyperglycemia on lipid accumulation in liver using several well-established animal models. We found that liver lipid deposition was increased in both type 1 diabetes and high-fat diet (HFD) induced hyperlipidemia models, suggesting that either hyperglycemia or hyperlipidemia alone or together was able to trigger NAFLD. Moreover, we tested whether miR-320, a miRNA promoting lipid accumulation in heart revealed by our previous study, also participated in NAFLD. Though miR-320 treatment further increased liver lipid deposition in type 1 diabetes and HFD-feeding mice, it showed no effect in leptin-receptor deficient db/db mice. Interestingly, miR-320 affected different target genes in cytosol and nucleus, respectively, which collectively led to liver lipid overload.

Our findings illustrated the complex roles of miRNAs in subcellular fractions including nucleus and cytoplasm, which may lead to new insights into the mechanisms and treatment strategies for NAFLD in the future.
Our findings illustrated the complex roles of miRNAs in subcellular fractions including nucleus and cytoplasm, which may lead to new insights into the mechanisms and treatment strategies for NAFLD in the future.Alternative splicing events are a major source of transcript and protein diversity in eukaryotes. Aberrant alternative splicing events have been increasingly reported in various cancers, including gastric cancer. To further explore the prognostic significance of alternative splicing events in gastric cancer patients, a comprehensive and systematic investigation was conducted by integrating alternative splicing event data and clinical information. Univariate Cox regression analysis identified 1383 alternative splicing events to be significantly associated with the overall survival of gastric cancer patients. Then, least absolute shrinkage and selection operator (LASSO) and multivariate Cox analyses were performed for the development of prognostic signatures. The final prognostic signature based on all seven types of alternative splicing events can act as an independent prognostic indicator after multivariate adjustment of several clinical parameters. Furthermore, the correlation and function analysis identified CELF2, BAG2, RBFOX2, PTBP2 and QKI as hub splicing factors, and the focal adhesion signaling pathway was most significantly correlated with survival-associated alternative splicing events. The results of this study may establish a foundation for further research investigating the underlying mechanism of alternative splicing events in the progression of gastric cancer.The pandemic COVID-19 is caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) and it is spreading very rapidly worldwide. To date, the origin and intermediate hosts of SARS-CoV-2 remain unclear. In this study, we conducted comparative analysis among SARS-CoV-2 and non-SARS-CoV-2 coronavirus strains to elucidate their phylogenetic relationships. We found 1, the SARS-CoV-2 strains analyzed could be divided into 3 clades with regional aggregation; 2, the non-SARS-CoV-2 common coronaviruses that infect humans or other organisms to cause respiratory syndrome and epizootic catarrhal gastroenteritis could also be divided into 3 clades; 3, the hosts of the common coronaviruses closest to SARS-CoV-2 were Apodemus chevrieri (a rodent), Delphinapterus leucas (beluga whale), Hypsugo savii (bat) , Camelus bactrianus (camel) and Mustela vison (mink); and 4, the gene sequences of the receptor ACE2 from different hosts could also be divided into 3 clades. The ACE2 gene sequences closest to that of humans in evolution include those from Nannospalax galili (Upper Galilee mountains blind mole rat), Phyllostomus discolor (pale spear-nosed bat), Mus musculus (house mouse), Delphinapterus leucas (beluga whale), and Catharus ustulatus (Swainson's thrush). We conclude that SARS-CoV-2 may have evolved from a distant common ancestor with the common coronaviruses but not a branch of any of them, implying that the prevalent pandemic COVID-19 agent SARS-CoV-2 may have existed in a yet to be identified primary host for a long time.This paper reports on a low-power readout IC (ROIC) for high-fidelity recording of the photoplethysmogram (PPG) signal. The system comprises a highly reconfigurable, continuous-time, second-order, incremental delta-sigma modulator (I-ΔΣM) as a light-to-digital converter (LDC), a 2-channel 10b light-emitting diode (LED) driver, and an integrated digital signal processing (DSP) unit. The LDC operation in intermittent conversion phases coupled with digital assistance by the DSP unit allow signal-aware, on-the-fly cancellation of the dc and ambient light-induced components of the photodiode current for more efficient use of the full-scale input range for recording of the small-amplitude, ac, PPG signal. Fabricated in TSMC 0.18 μm 1P/6M CMOS, the PPG ROIC exhibits a high dynamic range of 108.2 dB and dissipates on average 15.7 μW from 1.5 V in the LDC and 264 μW from 2.5 V in one LED (and its driver), while operating at a pulse repetition frequency of 250 Hz and 3.2% duty cycling. The overall functionality of the ROIC is also demonstrated by high-fidelity recording of the PPG signal from a human subject fingertip in the presence of both natural light and indoor light sources of 60 Hz.EMG-based continuous wrist joint motion estimation has been identified as a promising technique with huge potential in assistive robots. Conventional data-driven model-free methods tend to establish the relationship between the EMG signal and wrist motion using machine learning or deep learning techniques, but cannot interpret the functional relationship between neuro-commands and relevant joint motion. In this paper, an EMG-driven musculoskeletal model is proposed to estimate continuous wrist joint motion. This model interprets the muscle activation levels from EMG signals. A muscle-tendon model is developed to compute the muscle force during the voluntary flexion/extension movement, and a joint kinematic model is established to estimate the continuous wrist motion. To optimize the subject-specific physiological parameters, a genetic algorithm is designed to minimize the differences of joint motion prediction from the musculoskeletal model and joint motion measurement using motion data during training. Results show that mean root-mean-square-errors are 10.08°, 10.33°, 13.22° and 17.59° for single flexion/extension, continuous cycle and random motion trials, respectively. The mean coefficient of determination is over 0.9 for all the motion trials. The proposed EMG-driven model provides an accurate tracking performance based on user's intention.This article presents an analytical method that offers both spectral and spatial information to predict local electric fields capable of driving neural activities for neuromuscular activation, and the findings of an experimental investigation on a common strategy utilizing multiple high-frequency (HF) electric fields to create an interference to recruit neural firing at depth. By introducing a cut-off frequency [Formula see text] too high to recruit neural firing in a frequency-based field descriptor, the analytical method offers an effective means to position a focused temporal interference (TI) without mechanically moving the electrodes. The experiment, which was conducted on both forearms of five healthy volunteers, validates the feasibility of the method for selective neuromuscular stimulation, where three nerve/muscles that control human fingers were independently stimulated with two current channels. The numerical and experimental findings demonstrate that the frequency-based method overcomes several limitations associated with surface-based electrical stimulation.In this study, we develop a new approach, called zero-shot learning to index on semantic trees (LTI-ST), for efficient image indexing and scalable image retrieval. Our method learns to model the inherent correlation structure between visual representations using a binary semantic tree from training images which can be effectively transferred to new test images from unknown classes. Based on predicted correlation structure, we construct an efficient indexing scheme for the whole test image set. Unlike existing image index methods, our proposed LTI-ST method has the following two unique characteristics. First, it does not need to analyze the test images in the query database to construct the index structure. Instead, it is directly predicted by a network learnt from the training set. This zero-shot capability is critical for flexible, distributed, and scalable implementation and deployment of the image indexing and retrieval services at large scales. Second, unlike the existing distance-based index methods, our index structure is learnt using the LTI-ST deep neural network with binary encoding and decoding on a hierarchical semantic tree.
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