Free Triiodothyronine Levels Are Associated with Diabetic Nephropathy in Euthyroid Patients with Type 2 Diabetes

Objective. To investigate the association of thyroid function and diabetic nephropathy (DN) in euthyroid patients with type 2 diabetes. Methods. A total of 421 patients were included in this cross-sectional study. The following parameters were assessed: anthropometric measurements, fast plasma glucose, serum creatinine, lipid profile, HbA1c, free triiodothyronine (FT3), free thyroxine, thyroid-stimulating hormone levels, and urinary albumin-to-creatinine ratio (UACR). Patients with UACR of ≥30 mg/g were defined as those suffering from DN. Results. Of the 421 patients, 203 (48.2%) suffered from DN, and no difference was found between males and females. The patients with DN yielded significantly lower FT3 levels than those without DN (P<0.01). The prevalence of DN showed a significantly decreasing trend across the three tertiles based on FT3 levels (59.6%, 46.4%, and 38.6%, P<0.01). After adjustment for gender and age, FT3 levels were found to correlate positively with estimated glomerular filtration rate (P=0.03) and negatively with UACR (P<0.01). Multiple linear regression analysis showed that FT3 level was independently associated with UACR (β=-0.18, t=-3.70, and P<0.01). Conclusion. Serum FT3 levels are inversely associated with DN in euthyroid patients with type 2 diabetes, independent of traditional risk factors

Fourier Transformer: Fast Long Range Modeling by Removing Sequence Redundancy with FFT Operator

The transformer model is known to be computationally demanding, and prohibitively costly for long sequences, as the self-attention module uses a quadratic time and space complexity with respect to sequence length. Many researchers have focused on designing new forms of self-attention or introducing new parameters to overcome this limitation, however a large portion of them prohibits the model to inherit weights from large pretrained models. In this work, the transformer's inefficiency has been taken care of from another perspective. We propose Fourier Transformer, a simple yet effective approach by progressively removing redundancies in hidden sequence using the ready-made Fast Fourier Transform (FFT) operator to perform Discrete Cosine Transformation (DCT). Fourier Transformer is able to significantly reduce computational costs while retain the ability to inherit from various large pretrained models. Experiments show that our model achieves state-of-the-art performances among all transformer-based models on the long-range modeling benchmark LRA with significant improvement in both speed and space. For generative seq-to-seq tasks including CNN/DailyMail and ELI5, by inheriting the BART weights our model outperforms the standard BART and other efficient models. \footnote{Our code is publicly available at \url{https://github.com/LUMIA-Group/FourierTransformer}

Experimental estimation of the quantum Fisher information from randomized measurements

The quantum Fisher information (QFI) represents a fundamental concept in quantum physics. On the one hand, it quantifies the metrological potential of quantum states in quantum-parameter-estimation measurements. On the other hand, it is intrinsically related to the quantum geometry and multipartite entanglement of many-body systems. Here, we explore how the QFI can be estimated via randomized measurements, an approach which has the advantage of being applicable to both pure and mixed quantum states. In the latter case, our method gives access to the sub-quantum Fisher information, which sets a lower bound on the QFI. We experimentally validate this approach using two platforms: a nitrogen-vacancy center spin in diamond and a 4-qubit state provided by a superconducting quantum computer. We further perform a numerical study on a many-body spin system to illustrate the advantage of our randomized-measurement approach in estimating multipartite entanglement, as compared to quantum state tomography. Our results highlight the general applicability of our method to general quantum platforms, including solid-state spin systems, superconducting quantum computers and trapped ions, hence providing a versatile tool to explore the essential role of the QFI in quantum physics.Comment: 11 pages, 6 figures, comments are welcom

Mesenchymal stem cells improve mouse non-heart-beating liver graft survival by inhibiting Kupffer cell apoptosis via TLR4-ERK1/2-Fas/FasL-caspase3 pathway regulation

Abstract Background Liver transplantation is the optimal treatment option for end-stage liver disease, but organ shortages dramatically restrict its application. Donation after cardiac death (DCD) is an alternative approach that may expand the donor pool, but it faces challenges such as graft dysfunction, early graft loss, and cholangiopathy. Moreover, DCD liver grafts are no longer eligible for transplantation after their warm ischaemic time exceeds 30 min. Mesenchymal stem cells (MSCs) have been proposed as a promising therapy for treatment of certain liver diseases, but the role of MSCs in DCD liver graft function remains elusive. Methods In this study, we established an arterialized mouse non-heart-beating (NHB) liver transplantation model, and compared survival rates, cytokine and chemokine expression, histology, and the results of in vitro co-culture experiments in animals with or without MSC infusion. Results MSCs markedly ameliorated NHB liver graft injury and improved survival post-transplantation. Additionally, MSCs suppressed Kupffer cell apoptosis, Th1/Th17 immune responses, chemokine expression, and inflammatory cell infiltration. In vitro, PGE2 secreted by MSCs inhibited Kupffer cell apoptosis via TLR4-ERK1/2-caspase3 pathway regulation. Conclusion Our study uncovers a protective role for MSCs and elucidates the underlying immunomodulatory mechanism in an NHB liver transplantation model. Our results suggest that MSCs are uniquely positioned for use in future clinical studies owing to their ability to protect DCD liver grafts, particularly in patients for whom DCD organs are not an option according to current criteria

Deep learning in clinical natural language processing: a methodical review.

OBJECTIVE: This article methodically reviews the literature on deep learning (DL) for natural language processing (NLP) in the clinical domain, providing quantitative analysis to answer 3 research questions concerning methods, scope, and context of current research. MATERIALS AND METHODS: We searched MEDLINE, EMBASE, Scopus, the Association for Computing Machinery Digital Library, and the Association for Computational Linguistics Anthology for articles using DL-based approaches to NLP problems in electronic health records. After screening 1,737 articles, we collected data on 25 variables across 212 papers. RESULTS: DL in clinical NLP publications more than doubled each year, through 2018. Recurrent neural networks (60.8%) and word2vec embeddings (74.1%) were the most popular methods; the information extraction tasks of text classification, named entity recognition, and relation extraction were dominant (89.2%). However, there was a long tail of other methods and specific tasks. Most contributions were methodological variants or applications, but 20.8% were new methods of some kind. The earliest adopters were in the NLP community, but the medical informatics community was the most prolific. DISCUSSION: Our analysis shows growing acceptance of deep learning as a baseline for NLP research, and of DL-based NLP in the medical community. A number of common associations were substantiated (eg, the preference of recurrent neural networks for sequence-labeling named entity recognition), while others were surprisingly nuanced (eg, the scarcity of French language clinical NLP with deep learning). CONCLUSION: Deep learning has not yet fully penetrated clinical NLP and is growing rapidly. This review highlighted both the popular and unique trends in this active field

Sacral terminal filar cyst: a distinct variant of spinal meningeal cyst and midterm clinical outcome following combination resection surgery

ObjectiveSpinal meningeal cysts (SMCs) are currently classified into three types: extradural cysts without nerve root fibers (Type I), extradural cysts with nerve root fibers (Type II), and intradural cysts (Type III). However, the sacral terminal filar cyst is a distinct subtype with the filum terminale rather than nerve roots within the cyst. This study aimed to investigate the clinicoradiological characteristics and surgical outcomes of sacral terminal filar cysts.MethodsA total of 32 patients with sacral terminal filar cysts were enrolled. Clinical and radiological profiles were collected. All patients were surgically treated, and preoperative and follow-up neurological functions were evaluated.ResultsChronic lumbosacral pain and sphincter dysfunctions were the most common symptoms. On MRI, the filum terminale could be identified within the cyst in all cases, and low-lying conus medullaris was found in 23 (71.9%) cases. The filum terminale was dissociated and cut off in all cases, and the cyst wall was completely resected in 23 (71.9%) cases. After a median follow-up period of 26.5 ± 15.5 months, the pain and sphincter dysfunctions were significantly improved (both P &lt; 0.0001). The cyst recurrence was noted in only 1 (3.1%) case.ConclusionsSacral terminal filar cysts are rare, representing a distinct variant of SMCs. Typical MRI features, including filum terminale within the cyst and low-lying conus medullaris, may suggest the diagnosis. Although the optimal surgical strategy remains unclear, we recommend a combination of resection of the cyst wall and dissociation of the filum terminale. The clinical outcomes can be favorable