We have developed a new approach to solve the inverse problem of electrocardiography in terms of heart model parameters. The inverse solution of the electrocardiogram (ECG) inverse problem is defined, in the present study, as the parameters of the heart model, which are closely related to the physiological and pathophysiological status of the heart, and is estimated by using an optimization system of heart model parameters, instead of solving the matrix equation relating the body surface ECGs and equivalent cardiac sources. An artificial neural network based preliminary diagnosis system has been developed to limit the searching space of the optimization algorithm and to initialize the model parameters in the computer heart model. The optimal heart model parameters were obtained by minimizing the objective functions, as functions of the observed and model-generated body surface ECGs. We have tested the feasibility of the newly developed technique in localizing the site of origin of cardiac activation using a pace mapping protocol. The present computer simulation results show that, the present approach for localization of the site of origin of ventricular activation achieved an averaged localization error of about 3 mm [for 5-muV Gaussian white noise (GWN)] and 4 mm (for 10-muV GWN), with standard deviation of the localization errors of being about 1.5 mm. The present simulation study suggests that this newly developed approach provides a robust inverse solution, circumventing the difficulties of the ECG inverse problem, and may become an important alternative to other ECG inverse solutions.

When combined, the results highlight the feasibility and efficacy of combined rTMS  +  BCI for motor recovery, demonstrated by increased ipsilesional motor activity and improvements in behavioral function for the real rTMS  +  BCI condition in particular. Our findings also demonstrate the utility of BCI training alone, as shown by behavioral improvements for the sham rTMS  +  BCI condition. This study is the first to evaluate combined rTMS and BCI training for motor rehabilitation and provides a foundation for continued work to evaluate the potential of both rTMS and virtual reality BCI training for motor recovery after stroke.

Epithelial–mesenchymal transition (EMT) has been linked to cancer stem-like (CD44+) cell in the prostate cancer (PCa) metastasis. However, the molecular mechanism remains elusive. Here, we found EMT contributed to metastasis in PCa patients failed in androgen deprivation therapy (ADT). Castration TRAMP model also proved PCa treated with ADT promoted EMT with increased CD44+ stem-like cells. Switched CD44+ cell to EMT cell is a key step for luminal PCa cell metastasis. Our results also suggested ADT might go through promoting TGFβ1-CD44 signaling to enhance swift to EMT. Targeting CD44 with salinomycin and siRNA could inhibit cell transition and decrease PCa invasion. Together, cancer stem-like (CD44+) cells could be the initiator cells of EMT modulated by TGFβ1-CD44 signaling. Combined therapy of ADT with anti-CD44 may become a new potential therapeutic approach to battle later stage PCa.

This paper suggests a new approach for cardiac source localization of origin of arrhythmias using only the 12-lead ECG by means of CNN, and may have important applications for future real-time monitoring and localizing origins of cardiac arrhythmias guiding ablation treatment.

Objective Transcranial focused ultrasound (tFUS) has been introduced as a noninvasive neuromodulation technique with good spatial selectivity. We report an experimental investigation to detect noninvasive electrophysiological response induced by low-intensity tFUS in an in vivo animal model, and perform electrophysiological source imaging (ESI) of tFUS-induced brain activity from noninvasive scalp EEG recordings. Methods A single ultrasound transducer was used to generate low-intensity tFUS (Ispta<1 mW/cm2) and induce brain activation at multiple selected sites in an in vivo rat model. Up to 16 scalp electrodes were used to record tFUS-induced EEG. Event related potentials (ERPs) were analyzed in time, frequency, and spatial domains. Current source distributions were estimated by ESI to reconstruct spatio-temporal distributions of brain activation induced by tFUS. Results Neuronal activation was observed following low-intensity tFUS, as correlated to tFUS intensity and sonication duration. ESI revealed initial focal activation in cortical area corresponding to tFUS stimulation site, and the activation propagating to surrounding areas over time. Conclusion The present results demonstrate the feasibility of noninvasively recording brain electrophysiological response in vivo following low-intensity tFUS stimulation, and the feasibility of imaging spatio-temporal distributions of brain activation as induced by tFUS in vivo. Significance The present approach may lead to a new means of imaging brain activity using tFUS perturbation and a closed-loop ESI-guided tFUS neuromodulation modality.

Castration-resistant prostate cancer (CRPC) with neuroendocrine differentiation (NED) is a lethal disease for which effective therapies are urgently needed. The mechanism underlying development of CRPC with NED, however, remains largely uncharacterized. In this study, we explored and characterized the functional role of neurotensin (NTS) in cell line and animal models of CRPC with NED. NTS was acutely induced by androgen deprivation in animal models of prostate cancer (PCa) and activated downstream signaling leading to NED through activation of neurotensin receptor 1 (NTSR1) and neurotensin receptor 3 (NTSR3), but not neurotensin receptor 2 (NTSR2). Our findings also revealed the existence of a CK8+/CK14+ subpopulation in the LNCaP cell line that expresses high levels of both NTSR1 and NTSR3, and displays an enhanced susceptibility to develop neuroendocrine-like phenotypes upon treatment with NTS. More importantly, NTSR1 pathway inhibition prevented the development of NED and castration resistance in vivo. We propose a novel role of NTS in the development of CRPC with NED, and a possible strategy to prevent the onset of NED by targeting the NTS signaling pathway.

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Importance and Objective: Frailty refers to the decline in physiological reserve capacity caused by the deterioration of multiple physiological systems (brain, endocrine system, immune system, and skeletal muscle), leading to increased vulnerability and decreased stress capacity. Women have a higher prevalence of frailty than men, although the epidemiological factors underlying this phenomenon are not fully understood. Menopause and menopause-related characteristics may be among the contributing factors. Hence, the purpose of this scoping review was to explore the relationship between menopause and frailty. We attempted to summarize information such as the age that menopause occurs, years since menopause, types of menopause, and hormones and inflammatory markers of frailty among postmenopausal women. Methods: PubMed, EMBASE, The Cochrane Library, the Cumulative Index to Nursing and Allied Health Literature and Web of Science, the China National Knowledge Infrastructure, the China Biomedical Literature Service System, Wanfang Database and the WeiPu (VIP) Database were searched from inception until April 3, 2019. Supplementary searches of the references, cited documents, and similar documents of the included literature were also carried out. Discussion and Conclusions: Of 762 papers identified, 15 articles matching the criteria were included. The prevalence of frailty among postmenopausal women ranged from 5.9% to 57.3%. Existing studies suggest that menopause is associated with frailty. Early menopause, hysterectomy, low-free testosterone levels, and high C-reactive protein levels may increase the likelihood of frailty among postmenopausal women. Few original studies have explored the relationship between estrogen and frailty and the results of these studies are conflicting. Changes in hormone and inflammatory cytokine levels may mediate frailty among postmenopausal women. More in-depth research would be required to better understand the physiological and etiological mechanisms of the occurrence of frailty among postmenopausal women.