Effects of Perioperative Psychological Intervention on Rehabilitation Process of Patients with Total Knee Arthroplasty

Background: This study focuses on evaluating the effects of perioperative psychological intervention on rehabilitation process of patients with total knee arthroplasty (TKA). Method: We selected 40 patients randomly which all need to receive total knee arthroplasty in Nanjing Drum Tower Hospital during the period from January 2022 to March 2022. The patients were randomly assigned to two Groups (20 in each group): an intervention group (Psychological intervention combined with routine nursing, drug and rehabilitation therapy) and a control group (routine nursing, drug rehabilitation therapy). During each patients’ perioperative TKA surgeries, three scales (including VAS, ROM and ADL) are used to assess two groups. Result: After one week of psychological intervention, the pain score of the intervention group was lower than that of control group, the knee motion was greater than that of control group, and the ADL score was higher than that of control group. There was a significant difference in the treatment recovery between the two groups (P<0.05) Conclusions: Perioperative psychological intervention can promote the rehabilitation process of TKA patients, It can significantly improve pain, joint activity limitation, disuse muscle atrophy and other problems in a short period of time after surgery. Besides, it will effectively help patients to overcome the fear of movement, anxiety and improve patients' confidence, rehabilitation cooperation and prevention of complications, make patients adapt to the later rehabilitation life

Uncertainty quantification for noisy inputs-outputs in physics-informed neural networks and neural operators

Uncertainty quantification (UQ) in scientific machine learning (SciML) becomes increasingly critical as neural networks (NNs) are being widely adopted in addressing complex problems across various scientific disciplines. Representative SciML models are physics-informed neural networks (PINNs) and neural operators (NOs). While UQ in SciML has been increasingly investigated in recent years, very few works have focused on addressing the uncertainty caused by the noisy inputs, such as spatial-temporal coordinates in PINNs and input functions in NOs. The presence of noise in the inputs of the models can pose significantly more challenges compared to noise in the outputs of the models, primarily due to the inherent nonlinearity of most SciML algorithms. As a result, UQ for noisy inputs becomes a crucial factor for reliable and trustworthy deployment of these models in applications involving physical knowledge. To this end, we introduce a Bayesian approach to quantify uncertainty arising from noisy inputs-outputs in PINNs and NOs. We show that this approach can be seamlessly integrated into PINNs and NOs, when they are employed to encode the physical information. PINNs incorporate physics by including physics-informed terms via automatic differentiation, either in the loss function or the likelihood, and often take as input the spatial-temporal coordinate. Therefore, the present method equips PINNs with the capability to address problems where the observed coordinate is subject to noise. On the other hand, pretrained NOs are also commonly employed as equation-free surrogates in solving differential equations and Bayesian inverse problems, in which they take functions as inputs. The proposed approach enables them to handle noisy measurements for both input and output functions with UQ

Leveraging Hamilton-Jacobi PDEs with time-dependent Hamiltonians for continual scientific machine learning

We address two major challenges in scientific machine learning (SciML): interpretability and computational efficiency. We increase the interpretability of certain learning processes by establishing a new theoretical connection between optimization problems arising from SciML and a generalized Hopf formula, which represents the viscosity solution to a Hamilton-Jacobi partial differential equation (HJ PDE) with time-dependent Hamiltonian. Namely, we show that when we solve certain regularized learning problems with integral-type losses, we actually solve an optimal control problem and its associated HJ PDE with time-dependent Hamiltonian. This connection allows us to reinterpret incremental updates to learned models as the evolution of an associated HJ PDE and optimal control problem in time, where all of the previous information is intrinsically encoded in the solution to the HJ PDE. As a result, existing HJ PDE solvers and optimal control algorithms can be reused to design new efficient training approaches for SciML that naturally coincide with the continual learning framework, while avoiding catastrophic forgetting. As a first exploration of this connection, we consider the special case of linear regression and leverage our connection to develop a new Riccati-based methodology for solving these learning problems that is amenable to continual learning applications. We also provide some corresponding numerical examples that demonstrate the potential computational and memory advantages our Riccati-based approach can provide

CUTS: Neural Causal Discovery from Irregular Time-Series Data

Causal discovery from time-series data has been a central task in machine learning. Recently, Granger causality inference is gaining momentum due to its good explainability and high compatibility with emerging deep neural networks. However, most existing methods assume structured input data and degenerate greatly when encountering data with randomly missing entries or non-uniform sampling frequencies, which hampers their applications in real scenarios. To address this issue, here we present CUTS, a neural Granger causal discovery algorithm to jointly impute unobserved data points and build causal graphs, via plugging in two mutually boosting modules in an iterative framework: (i) Latent data prediction stage: designs a Delayed Supervision Graph Neural Network (DSGNN) to hallucinate and register unstructured data which might be of high dimension and with complex distribution; (ii) Causal graph fitting stage: builds a causal adjacency matrix with imputed data under sparse penalty. Experiments show that CUTS effectively infers causal graphs from unstructured time-series data, with significantly superior performance to existing methods. Our approach constitutes a promising step towards applying causal discovery to real applications with non-ideal observations.Comment: https://openreview.net/forum?id=UG8bQcD3Em

QIDANTONGMAI PROTECTS ENDOTHELIAL CELLS AGAINST HYPOXIA-INDUCED DAMAGE THROUGH REGULATING THE SERUM VEGF-A LEVEL

Qidantongmai (QDTM) is a Traditional Chinese Medicine (TCM) preparation that has long been used in folk medicine for the treatment of cardiovascular diseases. However, the underlying mechanisms are poorly understood. The present study was designed to determine the effects of QDTM on endothelial cells under hypoxic conditions both in vitro and in vivo. Primary human umbilical vein endothelial cells (HUVECs) were isolated, pretreated with QDTM medicated serum or saline control, and then cultured under hypoxia (2% oxygen) for 24 h. Sprague-Dawley rats were administered 1 ml/100 g of QDTM or saline twice a day for 4 days and treated with hypoxia (6 hours/day, discontinuous hypoxia, 360 mm Hg). QDTM not only protected HUVECs from hypoxia-induced damage by significantly retaining cell viability (P < 0.05) and decreasing apoptosis (P < 0.05) in vitro, but also protected liver endothelial cells from hypoxia-induced damage in vivo. Moreover, QDTM increased the serum VEGF-A level (P < 0.05) in rats treated with hypoxia for 7 days but suppressed the upregulation of serum VEGF-A in rats treated with hypoxia for 14 days. QDTM is a potent preparation that can protect endothelial cells against hypoxia-induced damage. The ability of QDTM to modulate the serum VEGF-A level may play an important role in its effects on endothelial cells

Red blood cell distribution width combined with age as a predictor of acute ischemic stroke in stable COPD patients

AimThis retrospective study aimed to investigate the independent clinical variables associated with the onset of acute cerebral ischemic stroke (AIS) in patients with stable chronic obstructive pulmonary disease (COPD).MethodA total of 244 patients with COPD who had not experienced a relapse within 6 months were included in this retrospective study. Of these, 94 patients hospitalized with AIS were enrolled in the study group, and the remaining 150 were enrolled in the control group. Clinical data and laboratory parameters were collected for both groups within 24 h after hospitalization, and the data of the two groups were statistically analyzed.ResultsThe levels of age, white blood cell (WBC), neutrophil (NEUT), glucose (GLU), prothrombin time (PT), albumin (ALB), and red blood cell distribution width (RDW) were different in the two groups (P &lt; 0.01). Logistic regression analysis showed that age, WBC, RDW, PT, and GLU were independent risk factors for the occurrence of AIS in patients with stable COPD. Age and RDW were selected as new predictors, and the receiver operating characteristic curves (ROC) were plotted accordingly. The areas under the ROC curves of age, RDW, and age + RDW were 0.7122, 0.7184, and 0.7852, respectively. The sensitivity was 60.5, 59.6, and 70.2%, and the specificity was 72.4, 86.0, and 60.0%, respectively.ConclusionThe combination of RDW and age in patients with stable COPD might be a potential predictor for the onset of AIS

Topical application of daphnetin hydrogel for traumatic brain injury

BackgroundTraumatic brain injury (TBI) causes neuronal cell damage and dysfunction. According to previous studies, daphnetin (Dap) has a protective effect in neurological injury. However, the in vivo bioavailability of daphnetin is not high. The purpose of this study was to determine whether administering daphnetin directly into the site of injury via a hydrogel drug carrier could improve its therapeutic impact.MethodsTripolycerol monostearates / daphnetin (TM/Dap) hydrogels were prepared and characterised using water bath heating, scanning electron microscopy (SEM) and small animal in vivo imaging techniques. The TBI model was established using the Feeney free fall impact method. Using the Morris water maze test, the mNSS neurological deficit rating scale, haematoxylin-eosin staining, and liver and kidney function tests, the therapeutic benefit of TM/Dap and its toxic side effects were assessed. The therapeutic effects of TM/Dap were further investigated using wet and dry gravimetric methods, Evans blue staining, protein immunoblotting, immunofluorescence staining techniques and ELISA.ResultsThe efficacy of the TM/Dap hydrogel in gradually releasing daphnetin in the context of traumatic brain damage was shown by both in vitro and in vivo tests. Behavioral experiments showed that the learning and spatial memory abilities of TM/Dap hydrogel treated mice were significantly improved in the water maze experiment. And TM/Dap hydrogel has high biosafety for organisms. The results of the therapeutic mechanism of action showed that TM/Dap hydrogel showed more significant efficacy in reducing the neuroinflammatory response caused by TNF-α, IL-6 and other factors, as well as promoting the recovery of post-traumatic neurological function.ConclusionThe use of hydrogel as a drug carrier for daphnetin showed more significant efficacy in reducing neuroinflammatory response, protecting nerve tissue and promoting post-traumatic neurological recovery compared with traditional drug delivery methods

Multikingdom interactions govern the microbiome in subterranean cultural heritage sites

9 páginas.- 5 figuras.- 66 referencias.- Data Availability. The amplicon sequences, shotgun metagenomics, and screened Actinobacteria strain sequences reported in this article have been deposited in the NCBI BioProject and GenBank databases (accession nos. PRJNA721777, PRJNA745276, and OL444665 to OL444682, respectively). All other study data are included in the article and/or supporting informationMicrobial biodeterioration is a major concern for the conservation of historical cultural relics worldwide. However, the ecology involving the origin, composition, and establishment of microbiomes on relics, once exposed to external environments, is largely unknown. Here, we combined field surveys with physiological assays and biological interaction experiments to investigate the microbiome in the Dahuting Han Dynasty Tomb, a Chinese tomb with more than 1,800 y of history, and its surrounding environments. Our investigation finds that multikingdom interactions, from mutualism to competition, drive the microbiome in this subterranean tomb. We reveal that Actinobacteria, Pseudonocardiaceae are the dominant organisms on walls in this tomb. These bacteria produce volatile geosmin that attracts springtails (Collembola), forming an interkingdom mutualism, which contributes to their dispersal, as one of the possible sources into the tomb from surrounding environments. Then, intrakingdom competition helps explain why Pseudonocardiaceae thrive in this tomb via the production of a mixture of cellulases, in combination with potential antimicrobial substances. Together, our findings show that multikingdom interactions play an important role in governing the microbiomes that colonize cultural relics. This knowledge is integral to understanding the ecological and physiological features of relic microbiomes and to supporting the relics’ long-term conservation.This work was supported by the National Key R&D Program (2019YFC1520700), the National Natural Science Foundation of China (42177297), Chinese Academy of Sciences (CAS) Strategic Priority Research Program Grant XDA28010302, and the Youth Innovation Promotion Association, CAS (Member No. 2014271). M.D.-B. is supported by a Ramón y Cajal Grant (RYC2018-025483-I), a project from the Spanish Ministry of Science and Innovation (PID2020-115813RA-I00), and Project Plan Andaluz de Investigación, Desarrollo e Innovación 2020 from the Junta de Andalucía (P20_00879).Peer reviewe

Cell transcriptomic atlas of the non-human primate Macaca fascicularis.

Studying tissue composition and function in non-human primates (NHPs) is crucial to understand the nature of our own species. Here we present a large-scale cell transcriptomic atlas that encompasses over 1 million cells from 45 tissues of the adult NHP Macaca fascicularis. This dataset provides a vast annotated resource to study a species phylogenetically close to humans. To demonstrate the utility of the atlas, we have reconstructed the cell-cell interaction networks that drive Wnt signalling across the body, mapped the distribution of receptors and co-receptors for viruses causing human infectious diseases, and intersected our data with human genetic disease orthologues to establish potential clinical associations. Our M. fascicularis cell atlas constitutes an essential reference for future studies in humans and NHPs.We thank W. Liu and L. Xu from the Huazhen Laboratory Animal Breeding Centre for helping in the collection of monkey tissues, D. Zhu and H. Li from the Bioland Laboratory (Guangzhou Regenerative Medicine and Health Guangdong Laboratory) for technical help, G. Guo and H. Sun from Zhejiang University for providing HCL and MCA gene expression data matrices, G. Dong and C. Liu from BGI Research, and X. Zhang, P. Li and C. Qi from the Guangzhou Institutes of Biomedicine and Health for experimental advice or providing reagents. This work was supported by the Shenzhen Basic Research Project for Excellent Young Scholars (RCYX20200714114644191), Shenzhen Key Laboratory of Single-Cell Omics (ZDSYS20190902093613831), Shenzhen Bay Laboratory (SZBL2019062801012) and Guangdong Provincial Key Laboratory of Genome Read and Write (2017B030301011). In addition, L.L. was supported by the National Natural Science Foundation of China (31900466), Y. Hou was supported by the Natural Science Foundation of Guangdong Province (2018A030313379) and M.A.E. was supported by a Changbai Mountain Scholar award (419020201252), the Strategic Priority Research Program of the Chinese Academy of Sciences (XDA16030502), a Chinese Academy of Sciences–Japan Society for the Promotion of Science joint research project (GJHZ2093), the National Natural Science Foundation of China (92068106, U20A2015) and the Guangdong Basic and Applied Basic Research Foundation (2021B1515120075). M.L. was supported by the National Key Research and Development Program of China (2021YFC2600200).S