Miracle-dz/UniFAR-FTUs huggingface dataset:
Effects of Nanosecond Pulsed Electric Fields in Cell Vitality, Apoptosis, and Proliferation of TPC-1 Cells

Objective To evaluate the effects of nanosecond pulsed electric fields (nsPEFs) with different pulse durations in cell vitality, apoptosis, and proliferation of TPC-1 cells, optimize pulse parameters and expand the application range of nsPEFs. Methods The pulse duration of 0, 300 ns, 500 ns, and 900 ns is generated with nsPEF generator. CCK-8 was used to investigate the effect of nsPEFs on the viability of TPC-1 cells. Flow cytometry was used to evaluate the apoptosis of TPC-1 after pulse treatment. The effect of nsPEFs on the proliferation ability of TPC-1 cells was detected by 5-ethy-nyl-2′-deoxyuridine. The morphological changes of TPC-1 cells after pulse treatment were observed by transmission electron microscopy. Results NsPEFs with 900 ns pulse duration can significantly affect the viability of TPC-1 cells and inhibit the proliferation ability of TPC-1 cells. In addition, nsPEFs can also induce apoptosis of TPC-1 cells. Conclusion NsPEFs with longer pulse duration can significantly affect the biological behavior of TPC-1 cells, such as cell viability and proliferation ability, and can also induce cell apoptosis, thereby inhibiting cell growth.
Analysis of Communal Molecular Mechanism and Potential Therapeutic Targets in Heart Failure and Type 2 Diabetes Mellitus

Background Although increasing evidence has suggested an interaction between heart failure (HF) and Type 2 diabetes mellitus (T2DM), the common mechanisms of the two diseases remain unclear. Therefore, this study aimed to obtain the differentially expressed genes (DEGs) and potential biomarkers or therapeutic targets in HF and T2DM. Methods The communal DEGs of HF and T2DM were identified by analyzing the two microarray datasets (GSE84796 and GSE95849), and functional annotation was performed for the communal DEGs to uncover the potential molecular mechanisms of HF and T2DM. Subsequently, STRING database and Cytoscape software were used to construct the protein–protein interaction (PPI) network and screen the hub genes. Finally, co-expression and drug–gene interaction prediction analysis and mRNA–miRNA regulatory network analysis were performed for hub genes. Results A total of 233 up-regulated genes and 3 down-regulated genes were found between HF and T2DM. The functional enrichment of DEGs and genes in each four modules were mainly involved in immunity. In addition, five hub genes were identified from PPI network, including SYK, SELL, RAC2, TLR8 and ITGAX. Conclusion The communal DEGs and hub genes identified in this research contribute to discover the underlying biological mechanisms and presents potential biomarkers or therapeutic targets in HF and T2DM.
Anticoagulation with Argatroban in a Patient with Heparin-Induced Thrombocytopenia and Renal Insufficiency Undergoing Orthotopic Heart Transplantation

Unfractionated heparin is the anticoagulant of choice for cardiac surgery that requires cardiopulmonary bypass. However, it can cause serious side effects like heparin-induced thrombocytopenia (HIT), an immune-mediated process where antibodies are directed against heparin and platelet 4 complexes. In such cases, alternative pharmacologic strategies are implemented to facilitate safe bypass conditions. A woman with severe decompensated heart failure was heparinized for intra-aortic balloon pump and subsequent LVAD placement. On day 6, a fall in platelets from 113,000 to 26,000 was noted. She was diagnosed with HIT. Heparin was discontinued and replaced with an argatroban infusion for the duration of her care until heart transplantation was completed. We review the mechanism, diagnosis, and complications of HIT. We discuss cardiopulmonary bypass and its relation to heparin, HIT, and heparin alternatives. We discuss argatroban's relevant pharmacology, clinical use, advantages, and disadvantages.
Idiopathic Pulmonary Comorbidities and Mechanisms

Idiopathic pulmonary fibrosis (IPF) is a disease with an unknown etiology mainly characterized by a progressive decline of lung function due to the scarring of the tissue deep in the lungs. The overall survival after diagnosis remains low between 3 and 5 years. IPF is a heterogeneous disease and much progress has been made in the past decade in understanding the disease mechanisms that contributed to the development of two new drugs, pirfenidone and nintedanib, which improved the therapeutic management of the disease. The understanding of the cofactors and comorbidities of IPF also contributed to improved management of the disease outcome. In the present review, we evaluate scientific evidence which indicates IPF as a risk factor for other diseases based on the complexity of molecular and cellular mechanisms involved in the disease development and of comorbidities. We conclude from the existing literature that while much progress has been made in understating the mechanisms involved in IPF development, further studies are still necessary to fully understand IPF pathogenesis which will contribute to the identification of novel therapeutic targets for IPF management as well as other diseases for which IPF is a major risk factor.
Exploring Culture, Religiosity and Spirituality Influence on Antihypertensive Medication Adherence Among Specialised Population: A Qualitative Ethnographic Approach

Background Hypertension is one of the major risk factors of stroke and leading risk factors for global death. Inadequate control of blood pressure due to medication non-adherence remains a challenge and identifying the underlying causes will provide useful information to formulate suitable interventions. Purpose This study aimed to explore the roles of culture, religiosity, and spirituality on adherence to anti-hypertensive medications. Methodology A semi-structured qualitative interview was used to explore promoters and barriers to medication adherence among hypertensive individuals residing in urban and rural areas of Perak State, West Malaysia. Study participants were individuals who are able to comprehend either in Malay or English, above 18 years old and on antihypertensive medications. Interview transcriptions from 23 participants were coded inductively and analyzed thematically. Codes generated were verified by three co-investigators who were not involved in transcribing process. The codes were matched with quotations and categorized using three levels of themes named as organizing, classifying and general themes. Results Cultural aspects categorized as societal and communication norms were related to non-adherence. The societal norms related to ignorance, belief in testimony and anything “natural is safe” affected medication adherence negatively. Communication norms manifested as superficiality, indirectness and non-confrontational were also linked to medication non-adherence. Internal and organizational religiosity was linked to increased motivation to take medication. In contrast, religious misconception about healing and treatment contributed towards medication non-adherence. The role of spirituality remains unclear and seemed to be understood as related to religiosity. Conclusion Culture and religiosity (C/R) are highly regarded in many societies and shaped people’s health belief and behaviour. Identifying the elements and mechanism through which C/R impacted adherence would be useful to provide essential information for linking adherence assessment to the interventions that specifically address causes of medication non-adherence.
Age-Related Differences of Rumination on the Loneliness–Depression Relationship: Evidence From a Population-Representative Cohort

Abstract Background and Objectives This study examined the association of loneliness with depressive symptoms across various age groups. Loneliness is a significant risk factor for precipitating depressive symptoms. Rumination, a mechanism that underpins depression, can become intense when a person feels lonely. In addition, age is a major factor associated with changes in mental and physical health. Thus, the importance of rumination and age in moderating the loneliness–depression link were investigated. Research Design and Methods This cross-sectional study was conducted during the acute phase of the coronavirus disease 2019 pandemic in Hong Kong (February 27 to March 17, 2020). A population-representative sample of 1,972 people (1,107 females; 18–92 years of age) was recruited and interviewed via telephone through random digit dialing. This sample included 394 younger adults (18–30 years), 1,106 middle-aged adults (31–64 years), and 472 older adults (65 years or above). Respondents reported depressive symptoms, subjective loneliness, state rumination, and sociodemographic factors. Results Loneliness and rumination were positively associated with depressive symptoms, and they significantly interacted in predicting cognitive-affective symptoms. Further analysis of age showed that the interaction was significant only in middle-aged adults and older adults. Both rumination and age interacted with loneliness, respectively, in predicting cognitive-affective symptoms. Discussion and Implications These findings indicate that the strength of the association between loneliness and the cognitive-affective symptoms of depression depends on rumination levels and age. An intervention to regulate rumination offers a feasible direction for health care and social care aimed at improving older adults’ mental health.
rpoB Mutations and Effects on Rifampin Resistance in Mycobacterium tuberculosis

Objective To investigate the mutations within the whole rpoB gene of Mycobacterium tuberculosis and analyze their effects on rifampin (RIF) resistance based on crystal structure. Methods We sequenced the entire rpoB gene in 175 tuberculosis isolates and quantified their minimum inhibitory concentrations using microplate-based assays. Additionally, the structural interactions between wild-type/mutant RpoB and RIF were also analyzed. Results Results revealed that a total of 34 mutations distributed across 17 different sites within the whole rpoB gene were identified. Of the 34 mutations, 25 could alter the structural interaction between RpoB and RIF and contribute to RIF resistance. Statistical analysis showed that S450L, H445D, H445Y and H445R mutations were associated with high-level RIF resistance, while D435V was associated with moderate-level RIF resistance. Conclusion Some mutations within the rpoB gene could affect the interaction between RpoB and RIF and thus are associated with RIF resistance. These findings could be helpful to design new antibiotics and develop novel diagnostic tools for drug resistance in TB.
Endoscopic Ultrasound-Guided Acquisition of Portal Venous Circulating Tumor Cells as a Potential Diagnostic and Prognostic Tool for Pancreatic Cancer

Background Circulating tumor cells (CTCs) were a promising liquid biopsy for pancreatic cancer (PC) but circulate in low counts in peripheral blood. We evaluated the diagnostic and prognostic values of portal vein (PoV) CTCs in PC patients. Methods PoV was aspirated under EUS guidance from 40 patients with suspected pancreaticobiliary cancers. Epithelial–mesenchymal-transition-related subtypes of CTCs were identified via immunofluorescence using EpCAM and Twist antibodies. The diagnostic and prognostic performance of PoV CTCs was investigated by receiver-operating characteristic (AUC) curve and Kaplan–Meier survival analysis. Results In total, 40 patients including 31 with PC, 4 with non-pancreatic periampullary cancer and 5 with benign pancreatic diseases (BPD) were enrolled. CTCs were detected more in PoV compared with peripheral blood. PoV CTC numbers in BPD patients were lower than in PC patients. The number of PoV CTCs, especially mesenchymal-CTCs (M-CTCs), was positively correlated with the tumor burden, instead of epithelial-CTCs (E-CTCs). The combination of PoV CTC numbers and CA19-9 demonstrated better diagnostic efficiency (AUC value 0.987) than either alone in differentiating PC with BPD. Moreover, the diagnostic efficacy of PoV CTCs and M-CTCs were obviously better than that of E-CTCs and CA19-9 in distinguishing early and late stage PC. Lastly, high PoV CTC and M-CTC numbers were both associated with shorter overall survival. Conclusion Acquisition of the PoV samples in PC patients via EUS-guided procedures has been proved safe and feasible. PoV CTCs, especially M-CTCs, have great potentials in diagnosing and predicting the prognosis of PC, especially in combination with CA19-9.
Role of Glucagon-Like Peptide-1 (GLP-1) Receptor Agonists in Hypoglycemia

A relatively recent addition to the arsenal of antidiabetic drugs used for the treatment of type 2 diabetes mellitus (T2DM) has been the “incretin mimetics,” a group of drugs that work on the glucagon-like peptide-1 (GLP-1) receptor and enhance insulin secretion from the pancreatic β-cells in a glucose-dependent manner, more potently in hyperglycemic conditions, while suppressing glucagon secretion at the same time. Therefore, it was assumed that this class of drugs would have a lower risk of hypoglycemia than insulin secretagogues like sulphonylureas. However, GLP-1 receptor agonists have been proposed to cause hypoglycemia in healthy normoglycemic subjects implying that their action is not as glucose-dependent as once thought. Other studies concluded that they might not induce hypoglycemia and the risk is dependent on other individual factors. However, the FDA announced that the 12 GLP-1 receptor agonists currently available on the market had potential safety signs and evaluated the need for regulatory action. This review provides an overview of the studies that investigated the possible hypoglycemic effect of GLP-1 receptor agonists. In addition, the current review describes other adverse effects of GLP-1 receptor agonist treatment.
The Efficacy of Moxibustion on the Serum Levels of CXCL1 and β-EP in Patients with Rheumatoid Arthritis

Objective This study aims to evaluate the efficacy of moxibustion on joint swelling and pain and the levels of C-X-C motif chemokine ligand 1 (CXCL1), β-endorphin (β-EP) in serum of rheumatoid arthritis (RA) patients and to investigate the anti-inflammatory and analgesic mechanism of moxibustion on improving RA. Methods Sixty-eight patients with RA were randomly and equally classified into the control and treatment groups. The control group was treated with routine drug therapy, while the treatment group received routine drug therapy and moxibustion. Both groups were treated for eight weeks. The symptoms and laboratory indicators of RA patients were compared in the two groups before and after intervention. Results Sixty-one patients completed the study: four patients dropped out from the treatment group and three from the control group. Trial endpoints were change (∆) in symptoms, measured by Ritchie's articular index (RAI), swollen joint count (SJC), and laboratory indicators, measured by the level of CXCL1, β-EP, tumor necrosis factor-a (TNF-α), and interleukin-1β (IL-1β). ∆RAI, ∆SJC, ∆CXCL1, ∆β-EP, ∆TNF-α, and ∆IL-1β in the treatment group were superior to the control group (13.50 [14.50] versus 6.00 [13.00] in ∆RAI, 4.00 [3.00] versus 2.00 [4.00] in ∆SJC, 0.04 ± 0.79 ng/mL versus -0.01 ± 0.86 ng/mL in ∆CXCL1, -2.43 [5.52] pg/mg versus -0.04 [4.09] pg/mg in ∆β-EP, 3.45 [5.90] pg/mL versus 1.55 [8.29] pg/mL in ∆TNF-α, and 6.15 ± 8.65 pg/mL versus 1.28 ± 8.51 pg/mL in ∆IL-1β; all P < 0.05). Conclusion Moxibustion can improve the joint swelling and pain symptoms in patients with RA, which may be related to the fact that moxibustion can reduce the release of inflammatory factors in patients with RA and downregulate the level of CXCL1 and increase the level of β-EP at the same time. This trial is registered with ChiCTR-IOR-17012282.
A Deep Generative Approach to Conditional Sampling

Abstract We propose a deep generative approach to sampling from a conditional distribution based on a unified formulation of conditional distribution and generalized nonparametric regression function using the noise-outsourcing lemma. The proposed approach aims at learning a conditional generator, so that a random sample from the target conditional distribution can be obtained by transforming a sample drawn from a reference distribution. The conditional generator is estimated nonparametrically with neural networks by matching appropriate joint distributions using the Kullback-Liebler divergence. An appealing aspect of our method is that it allows either of or both the predictor and the response to be high-dimensional and can handle both continuous and discrete type predictors and responses. We show that the proposed method is consistent in the sense that the conditional generator converges in distribution to the underlying conditional distribution under mild conditions. Our numerical experiments with simulated and benchmark image data validate the proposed method and demonstrate that it outperforms several existing conditional density estimation methods. Supplementary materials for this article are available online.
Statistical Physics Meets Wireless Communications: A Resource Allocation Solution for Large Networks

The ever-increasing number of nodes in current and future wireless communication networks brings unprecedented challenges for the allocation of the available communication resources. This is caused by the combinatorial nature of the resource allocation problems, which limits the performance of state-of-the-art techniques when the network size increases. In this paper, we take a new direction and investigate how meth-ods from statistical physics can be used to address resource allocation problems in large networks. To this aim, we propose a novel model of the wireless network based on a type of disordered physical systems called spin glasses, and on the contributions of the recently Nobel laureate G. Parisi. We show that resource allocation problems, e.g., time, code or frequency assignment, have the same structure as the problem of ﬁnding spe-ciﬁc conﬁgurations in spin glasses. Based on this parallel, we investigate the use of the Survey Propagation method from statistical physics in the solution of resource allocation problems in wireless networks. Through numerical simulations we show that the proposed statistical-physics-based resource allocation algorithm is a promising tool for the eﬃcient allocation of communication resources in large wireless communications networks. Given a ﬁxed number of resources, we are able to serve a larger number of nodes, compared to state-of-the-art reference schemes, without introducing more interference into the system.
Generalized Out-of-Distribution Detection: A Survey

Out-of-distribution (OOD) detection is critical to ensuring the reliability and safety of machine learning systems. For instance, in autonomous driving, we would like the driving system to issue an alert and hand over the control to humans when it detects unusual scenes or objects that it has never seen during training time and cannot make a safe decision. The term, OOD detection, first emerged in 2017 and since then has received increasing attention from the research community, leading to a plethora of methods developed, ranging from classification-based to density-based to distance-based ones. Meanwhile, several other problems, including anomaly detection (AD), novelty detection (ND), open set recognition (OSR), and outlier detection (OD), are closely related to OOD detection in terms of motivation and methodology. Despite common goals, these topics develop in isolation, and their subtle differences in definition and problem setting often confuse readers and practitioners. In this survey, we first present a unified framework called generalized OOD detection, which encompasses the five aforementioned problems, i.e., AD, ND, OSR, OOD detection, and OD. Under our framework, these five problems can be seen as special cases or sub-tasks, and are easier to distinguish. We then review each of these five areas by summarizing their recent technical developments, with a special focus on OOD detection methodologies. We conclude this survey with open challenges and potential research directions.
On Multiply Robust Mendelian Randomization (MR2) With Many Invalid Genetic Instruments

Mendelian randomization (MR) is a popular instrumental variable (IV) approach, in which genetic markers are used as IVs. In order to improve efficiency, multiple markers are routinely used in MR analyses, leading to concerns about bias due to possible violation of IV exclusion restriction of no direct effect of any IV on the outcome other than through the exposure in view. To address this concern, we introduce a new class of Multiply Robust MR (MR2) estimators that are guaranteed to remain consistent for the causal effect of interest provided that at least one genetic marker is a valid IV without necessarily knowing which IVs are invalid. We show that the proposed MR2 estimators are a special case of a more general class of estimators that remain consistent provided that a set of at least k{dagger} out of K candidate instrumental variables are valid, for k{dagger} [≤] K set by the analyst ex ante, without necessarily knowing which IVs are invalid. We provide formal semiparametric theory supporting our results, and characterize the semiparametric efficiency bound for the exposure causal effect which cannot be improved upon by any regular estimator with our favorable robustness property. We conduct extensive simulation studies and apply our methods to a large-scale analysis of UK Biobank data, demonstrating the superior empirical performance of MR2 compared to competing MR methods.
DVIO: Depth-Aided Visual Inertial Odometry for RGBD Sensors

In past few years we have observed an increase in the usage of RGBD sensors in mobile devices. These sensors provide a good estimate of the depth map for the camera frame, which can be used in numerous augmented reality applications. This paper presents a new visual inertial odometry (VIO) system, which uses measurements from a RGBD sensor and an inertial measurement unit (IMU) sensor for estimating the motion state of the mobile device. The resulting system is called the depth-aided VIO (DVIO) system. In this system we add the depth measurement as part of the nonlinear optimization process. Specifically, we propose methods to use the depth measurement using one-dimensional (1D) feature parameterization as well as three-dimensional (3D) feature parameterization. In addition, we propose to utilize the depth measurement for estimating time offset between the unsynchronized IMU and the RGBD sensors. Last but not least, we propose a novel block-based marginalization approach to speed up the marginalization processes and maintain the real-time performance of the overall system. Experimental results validate that the proposed DVIO system outperforms the other state-of-the-art VIO systems in terms of trajectory accuracy as well as processing time.
Quantum-enhanced atomic gyroscope with tunable precision

We model a gyroscope that exploits quantum effects in an atomic Bose–Einstein condensate to gain a tunable enhancement in precision. Current inertial navigation systems rely on the Sagnac effect using unentangled photons in fibre-optic systems and there are proposals for improving how the precision scales with the number of particles by using entanglement. Here we exploit a different route based on sharp resonances associated with quantum phase transitions. By adjusting the interaction between the particles and/or the shape of their trapping potential we are able to tune the width of the resonance and hence the precision of the measurement. Here we show how we can use this method to increase the overall sensitivity of a gyroscope by adjusting the system parameters as the measurement proceeds and our knowledge of the rotation improves. We illustrate this with an example where the precision is enhanced by a factor of more than 20 over the case without tuning, after 100 repetitions. Metrology schemes with tunable precision based on quantum phase transitions could offer an important complementary method to other quantum-enhanced measurement and sensing schemes.