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Abstract Proteasomal activity is compromised in diabetic hearts that contributes to proteotoxic stresses and cardiac dysfunction. Osteocrin (OSTN) acts as a novel exercise-responsive myokine and is implicated in various cardiac diseases. Herein, we aim to investigate the role and underlying molecular basis of OSTN in diabetic cardiomyopathy (DCM). Mice received a single intravenous injection of the cardiotrophic adeno-associated virus serotype 9 to overexpress OSTN in the heart and then were exposed to intraperitoneal injections of streptozotocin (STZ, 50 mg/kg) for consecutive 5 days to generate diabetic models. Neonatal rat cardiomyocytes were isolated and stimulated with high glucose to verify the role of OSTN in vitro. OSTN expression was reduced by protein kinase B/forkhead box O1 dephosphorylation in diabetic hearts, while its overexpression significantly attenuated cardiac injury and dysfunction in mice with STZ treatment. Besides, OSTN incubation prevented, whereas OSTN silence aggravated cardiomyocyte apoptosis and injury upon hyperglycemic stimulation in vitro. Mechanistically, OSTN treatment restored protein kinase G (PKG)-dependent proteasomal function, and PKG or proteasome inhibition abrogated the protective effects of OSTN in vivo and in vitro. Furthermore, OSTN replenishment was sufficient to prevent the progression of pre-established DCM and had synergistic cardioprotection with sildenafil. OSTN protects against DCM via restoring PKG-dependent proteasomal activity and it is a promising therapeutic target to treat DCM.

Abstract Background Health care workers (HCWs) are at risk for occupationally acquired Mycobacterium tuberculosis infection and tuberculosis (TB) disease due to repeated exposure to workplace tubercle bacilli. To determine whether continual mycobacterial stimulation correlates with increased expression of inhibitory T cell receptors, here we compared PD-1 receptor expression on surfaces of circulating T cells between naïve (uninfected) HCWs and HCWs with latent TB infection (LTBI). Result Data collected from 133 medical workers who met study selection criteria were included in the final analysis. QuantiFERON-TB Gold In-​Tube (QFT-GIT) testing yielded positive results for 32 HCWs, for an overall LTBI rate of 24.1%. Multivariate analysis identified HCW length of service > 15 years as an independent risk factor for a positive QFT-GIT result. In addition, comparisons of blood T cell subgroup profiles between QFT- and QFT+ groups indicated QFT+ subjects possessed greater proportions of mature (TM), transitional memory (TTM) and effector memory (TEM) CD4+ T cell subgroups and lower proportions of naïve T cells (TN). Moreover, the QFT+ group percentage of CD8+ T cells with detectable surface PD-1 was significantly higher than the corresponding percentage for the QFT- group. Meanwhile, no statistical intergroup difference was observed in percentages of CD4+ T cells with detectible surface PD-1. Conclusions Our data demonstrated that upregulated PD-1 expression on circulating CD8+, but not CD4+ T cells, was associated with latent TB infection of HCWs. As compared to other hospitals, occupational TB infection risk in our hospital was substantially mitigated by implementation of multitiered infection control measures.

Research shows that across life, the incidence of mental illness is highest in the young. In the context of the COVID-19 pandemic, mental health issues of the young in particular have received global attention. The rostral anterior cingulate cortex (rACC) plays an important role in psychiatric disorders and chronic pain-psychiatric comorbidities. However, it remains unknown whether or how the afferent and efferent circuits of the rACC change with aging. In this study, we microinjected a retrograde tracer virus and an anterograde trans-monosynaptic virus into the rACC of young and middle-aged mice (both male and female), and systematically and quantitatively analyzed the whole-brain afferent and efferent connections of rACC at different ages and sexes. Notably, in young and middle-aged mice, afferents of the rACC belong to four groups of brain structures arising mainly from the amygdala [mainly basolateral amygdaloid nucleus (BLA)] and cerebral cortex (mainly orbital cortex), with a small part originating from the basal forebrain and thalamus. In contrast, efferents of the rACC belong to four groups of brain structures mainly projecting to the thalamus (mainly ventral anterior-lateral/ventromedial thalamic nucleus (VAL/VM)], with a very small part projecting to the amygdala, basal forebrain, and cerebral cortex. Compared with young mice, the BLA-rACC circuit in middle-aged mice (male and female) did not change significantly, while the rACC-VAL/VM circuit in middle-aged mice (male and female) decreased significantly. In conclusion, this study comprehensively analyzed the input-output neural projections of rACC in mice of different ages and sexes and provided preliminary evidence for further targeted research.

ObjectiveCoronavirus disease 2019 (COVID-19), which was caused by severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2), had already resulted in widespread epidemics worldwide and millions of people's deaths since its outbreak in 2019. COVID-19 had also been demonstrated to affect people's cardiac function. However, the specific mechanism and influence of this damage were not clear yet. The purpose of the present study was to provide a bibliometric analysis of the current studies related to cardiac involvement after SARS-CoV-2 infection.MethodsA bibliometric literature search was performed on the web of science. The number and type of publications, countries, institutional sources, journals, and citation patterns were analyzed. In addition, qualitative and quantitative evaluations were carried out to visualize the scientific achievements in this field by using the VOSviewer software.ResultsWeb of science had recorded 2,24,097 documents on COVID-19 at the time of data collection (May 12, 2022). A total of 2,025 documents related to cardiac involvement were recorded at last. The countries with the most published articles were the United States of America (USA) (n =747, 36.9%), Italy (n =324, 16%), and England (n =213, 10.5%). Although the countries and institutions that published the most articles were mainly from the USA, the top three authors were from Germany, England, and Poland. Frontiers in Cardiovascular Medicine was the journal with the most studies (65 3.2%), followed by ESC Heart Failure (59 2.9%) and Journal of Clinical Medicine (56 2.8%). We identified 13,739 authors, among which Karin Klingel and Amer Harky had the most articles, and Shaobo Shi was co-cited most often. There existed some cooperation between different authors, but the scope was limited. Myocarditis and heart failure (HF) were the main research hotspots of COVID-19 on cardiac dysfunction and may be crucial to the prognosis of patients.ConclusionsIt was the first bibliometric analysis of publications related to COVID-19-associated cardiac disorder. This study provided academics and researchers with useful information on the most influential articles of COVID-19 and cardiac dysfunction. Cooperation between countries and institutions must be strengthened on myocarditis and HF during COVID-19 pandemic.

Acute-on-chronic liver failure (ACLF) is a group of clinical syndromes related to severe acute liver function impairment and multiple-organ failure caused by various acute triggering factors on the basis of chronic liver disease. Due to its severe condition, rapid progression, and high mortality, it has received increasing attention. Recent studies have shown that the pathogenesis of ACLF mainly includes direct injury and immune injury. In immune injury, cytotoxic T lymphocytes (CTLs), dendritic cells (DCs), and CD4+ T cells accumulate in the liver tissue, secrete a variety of proinflammatory cytokines and chemokines, and recruit more immune cells to the liver, resulting in immune damage to the liver tissue, massive hepatocyte necrosis, and liver failure, but the key molecules and signaling pathways remain unclear. The “danger hypothesis” holds that in addition to the need for antigens, damage-associated molecular patterns (DAMPs) also play a very important role in the occurrence of the immune response, and this hypothesis is related to the pathogenesis of ACLF. Here, the research status and development trend of ACLF, as well as the mechanism of action and research progress on various DAMPs in ACLF, are summarized to identify biomarkers that can predict the occurrence and development of diseases or the prognosis of patients at an early stage.

Summary: Direct myocardial and vascular injuries due to severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection-driven inflammation is the leading cause of acute cardiac injury associated with coronavirus disease 2019 (COVID-19). However, in-depth knowledge of the injury characteristics of the heart affected by inflammation is lacking. In this study, using a quantitative spatial proteomics strategy that combines comparative anatomy, laser-capture microdissection, and histological examination, we establish a region-resolved proteome map of the myocardia and microvessels with obvious inflammatory cells from hearts of patients with COVID-19. A series of molecular dysfunctions of myocardia and microvessels is observed in different cardiac regions. The myocardia and microvessels of the left atrial are the most susceptible to virus infection and inflammatory storm, suggesting more attention should be paid to the lesion and treatment of these two parts. These results can guide in improving clinical treatments for cardiovascular diseases associated with COVID-19.

Complex terrain, the sparse distribution of rain gauges, and the poor resolution and quality of satellite data in remote areas severely restrict the development of watershed hydrological modeling, meteorology, and ecological research. In this study, based on the relationship between cloud optical and physical properties and precipitation, a daily geographically weighted regression (GWR) precipitation downscaling model was constructed for the Three Rivers Source region, China, for the period from 2010 to 2014. The GWR precipitation downscaling model combined three different satellite precipitation datasets (CMORPH, IMERG, and ERA5) which were downscaled from a coarse resolution (0.25° and 0.1°) to a fine resolution (1 km). At the same time, the preliminary downscaling results were calibrated and verified by employing the geographic difference analysis (GDA) and geographic ratio analysis (GRA) methods combined with rainfall data. Finally, the analytical hierarchy process (AHP) and the entropy weight method (EW) were adopted to fuse the three downscaled and calibrated satellite precipitation datasets into the merged satellite precipitation dataset (MSP), which provides a higher quality of data (CC = 0.790, RMSE = 2.189 mm/day, and BIAS = 0.142 mm). In summary, the downscaling calibration and precipitation fusion scheme proposed in this study is suitable for obtaining high-resolution daily precipitation data in the Three Rivers Source region with a complex climate and topography.

The number of orthopedic implants for bone fixation and joint arthroplasty has been steadily increasing over the past few years. However, implant-associated infection (IAI), a major complication in orthopedic surgery, impacts the quality of life and causes a substantial economic burden on patients and societies. While research and study on IAI have received increasing attention in recent years, the failure rate of IAI has still not decreased significantly. This is related to microbial biofilms and their inherent antibiotic resistance, as well as the various mechanisms by which bacteria evade host immunity, resulting in difficulties in diagnosing and treating IAIs. Hence, a better understanding of the complex interactions between biofilms, implants, and host immunity is necessary to develop new strategies for preventing and controlling these infections. This review first discusses the challenges in diagnosing and treating IAI, followed by an extensive review of the direct effects of orthopedic implants, host immune function, pathogenic bacteria, and biofilms. Finally, several promising preventive or therapeutic alternatives are presented, with the hope of mitigating or eliminating the threat of antibiotic resistance and refractory biofilms in IAI.

Abstract Aims Periprosthetic joint infection (PJI) is a serious complication of total joint arthroplasty. We performed a retrospective cohort study to evaluate (1) the change of coagulation profile in two-staged arthroplasty patients and (2) the relationship between coagulation profile and the outcomes of reimplantation. Method Between January 2011 and December 2018, a total of 202 PJI patients who were operated on with two-staged arthroplasty were included in this study initially. This study continued for 2 years and the corresponding medical records were scrutinized to establish the diagnosis of PJI based on the 2014 MSIS criteria. The coagulation profile was recorded at two designed points, (1) preresection and (2) preimplantation. The difference of coagulation profile between preresection and preimplantation was evaluated. Receiver operating characteristic curves (ROC) were used to evaluate the diagnostic efficiency of the coagulation profile and change of coagulation profile for predicting persistent infection before reimplantation. Results The levels of APTT, INR, platelet count, PT, TT, and plasma fibrinogen before spacer implantation were significantly higher than before reimplantation. No significant difference was detected in the levels of D-dimer, ACT, and AT3 between the two groups. The AUC of the combined coagulation profile and the change of combined coagulation profile for predicting persistent infection before reimplantation was 0.667 (95% CI 0.511, 0.823) and 0.667 (95% CI 0.526, 0.808), respectively. Conclusion The coagulation profile before preresection is different from before preimplantation in two-staged arthroplasty and the coagulation markers may play a role in predicting infection eradication before reimplantation when two-stage arthroplasty is performed. Level of evidence Level III, diagnostic study.

E-commerce poverty alleviation has become a new wisdom in China’s rural poverty alleviation, but there are a few empirical researches on e-commerce poverty alleviation based on farmer cooperatives. Taking four typical poverty counties in Zhejiang Province as an example, based on the actor network theory (ANT), this paper defines the participants and their obligatory passage point (OPP) from the e-commerce poverty alleviation actor network (EPAAN), combs the roles and interest demands of various stakeholders, and constructs the EPAAN model based on farmer cooperatives according to the translation process. We found that the EPAAN is a heterogeneous network by constantly updating, which consists of human actors with cooperatives as the main body and non-human actors. Moreover, our study illustrates that the formation and operation mechanism of cooperative e-commerce poverty alleviation network alliance under the background of targeted poverty alleviation can be well analyzed with the help of actor network theory. This study contributes to provide a feasible Chinese plan for the cause of poverty eradication all over the world, and provides a great reference value for global poverty governance.

Epithelial tissues constitute an exotic type of active matter with non-linear properties reminiscent of amorphous materials. In the context of a proliferating epithelium, modeled by the quasistatic vertex model, we identify novel discrete tissue scale rearrangements, i.e. cellular rearrangement avalanches, which are a form of collective cell movement. During the avalanches, the vast majority of cells retain their neighbors, and the resulting cellular trajectories are radial in the periphery, a vortex in the core. After the onset of these avalanches, the epithelial area grows discontinuously. The avalanches are found to be stochastic, and their strength is correlated with the density of cells in the tissue. Overall, avalanches redistribute accumulated local spatial pressure along the tissue. Furthermore, the distribution of avalanche magnitudes is found to obey a power law, with an exponent consistent with sheer induced avalanches in amorphous materials. To understand the role of avalanches in organ development, we simulate epithelial growth of the Drosophila eye disc during the third instar using a computational model, which includes both chemical and mechanistic signaling. During the third instar, the morphogenetic furrow (MF), a ~10 cell wide wave of apical area constriction propagates through the epithelium. These simulations are used to understand the details of the growth process, the effect of the MF on the growth dynamics on the tissue scale, and to make predictions for experimental observations. The avalanches are found to depend on the strength of the apical constriction of cells in the MF, with a stronger apical constriction leading to less frequent and more pronounced avalanches. The results herein highlight the dependence of simulated tissue growth dynamics on relaxation timescales, and serve as a guide for in vitro experiments. Author summary Epithelial tissues have interesting properties. They are often described as viscoelastic materials, behaving like an elastic solid on short time scales and as a liquid on long time scales. When tissues proliferate, cell division requires the cells to rearrange and displace each other. To relieve proliferative stress, cells are equipped with machinery that allows them to respond to mechanical cues and rearrange. Herein, we perform computational simulations, based on the vertex model, to investigate epithelial growth. In our simulations, when the tissue size reaches thousands of cells, we observe the prevalence of stochastic step-like growth discontinuities. These newly identified events, which we term avalanches, involve the collective rearrangement of all cells in the tissue, and they are accompanied with localized cellular pressure redistribution and a collective net radial displacement of cells. We study their properties in the simple setting of a spatially uniform and time independent growth model and in the context of an experimentally calibrated Drosophila eye disc growth model. Utilizing the eye disc growth model, we also investigate the relationship between localized cellular mechanical deformations and global growth dynamics. We conclude that avalanches constitute a macroscopic pathway for epithelial tissues to release accumulated proliferative stress when local processes are insufficient.

As a promising industrial wireless network technology, 5G URLLC is playing a key role in industrial control systems which employ different kinds of industrial control protocols. This leaves a significant challenge in how to adapt 5G URLLC with existing industrial control protocols and develop novel industrial wireless controllers. Motivated by this, this paper investigates the protocol adaptation and experimental evaluations for the development of a 5G-based industrial wireless controller prototype. Specifically, using the globally used industrial protocol Modbus as an example, we first deeply analyze the characteristics of the data packet format and the communication time sequence of Modbus RTU, Modbus TCP, and 5G protocols. Then, we propose a protocol resolution and conversion scheme for the protocol adaptation between Modbus RTU/Modbus TCP and 5G. Furthermore, we develop a wireless programmable logic controller (PLC) prototype system based on 5G. To evaluate the protocol adaptation scheme and the developed prototype, we perform extensive experiments to test the reliability and latency. The results demonstrate that the latency of the protocol adaptation scheme is smaller than that of protocol transparent transmission without resolution and conversion, while the reliability loss is not large. Moreover, the speed and reliability for the protocol adaptation between Modbus TCP and 5G are better than those between Modbus RTU and 5G.

Traditional gas pipeline leak detection methods require task offload decisions in the cloud, which has low real time performance. The emergence of edge computing provides a solution by enabling offload decisions directly at the edge server, improving real-time performance; however, energy is the new bottleneck. Therefore, focusing on the gas transmission pipeline leakage detection scenario in real time, a novel detection algorithm that combines the benefits of both the heuristic algorithm and the advantage actor critic (AAC) algorithm is proposed in this paper. It aims at optimization with the goal of real-time guarantee of pipeline mapping analysis tasks and maximizing the survival time of portable gas leak detectors. Since the computing power of portable detection devices is limited, as they are powered by batteries, the main problem to be solved in this study is how to take into account the node energy overhead while guaranteeing the system performance requirements. By introducing the idea of edge computing and taking the mapping relationship between resource occupation and energy consumption as the starting point, the optimization model is established, with the goal to optimize the total system cost (TSC). This is composed of the node’s transmission energy consumption, local computing energy consumption, and residual electricity weight. In order to minimize TSC, the algorithm uses the AAC network to make task scheduling decisions and judge whether tasks need to be offloaded, and uses heuristic strategies and the Cauchy–Buniakowsky–Schwarz inequality to determine the allocation of communication resources. The experiments show that the proposed algorithm in this paper can meet the real-time requirements of the detector, and achieve lower energy consumption. The proposed algorithm saves approximately 56% of the system energy compared to the Deep Q Network (DQN) algorithm. Compared with the artificial gorilla troops Optimizer (GTO), the black widow optimization algorithm (BWOA), the exploration-enhanced grey wolf optimizer (EEGWO), the African vultures optimization algorithm (AVOA), and the driving training-based optimization (DTBO), it saves 21%, 38%, 30%, 31%, and 44% of energy consumption, respectively. Compared to the fully local computing and fully offloading algorithms, it saves 50% and 30%, respectively. Meanwhile, the task completion rate of this algorithm reaches 96.3%, which is the best real-time performance among these algorithms.

Today, people rely heavily on infrastructure networks. Attacks on infrastructure networks can lead to significant property damage and production stagnation. The game theory provides a suitable theoretical framework for solving the problem of infrastructure protection. Existing models consider only the beneficial effects that the defender obtains from information gaps. If the attacker’s countermeasures are ignored, the defender will become passive. Herein, we consider that a proficient attacker with a probability in the game can fill information gaps in the network. First, we introduce the link-hiding rule and the information dilemma. Second, based on the Bayesian static game model, we establish an attack–defense game model with multiple types of attackers. In the game model, we consider resource-consistent and different types of distributions of the attacker. Then, we introduce the solution method of our model by combining the Harsanyi transformation and the bi-matrix game. Finally, we conduct experiments using a scale-free network. The result shows that the defender can be benefited by hiding some links when facing a normal attacker or by estimating the distribution of the attacker correctly. The defender will experience a loss if it ignores the proficient attacker or misestimates the distribution.

In this study we present new mineral chemistry, whole-rock geochemical and zircon U–Pb geochronological data for 12 metamafic dykes in the mantle sequence of the Sangsang ophiolite in South Tibet (China). Modal analyses of these dykes gave averages of ~40%–65% plagioclase and ~35%–60% amphibole and small amounts of (igneous) clinopyroxene, epidote and opaque minerals. This mineral assemblage resembles that of typical orthoamphibolites. Nevertheless, due to the absence of foliation the investigated rocks are described as metamafic lithologies. These rocks have primitive mantle (PM)-normalized multi-element patterns with negative Nb and Ta anomalies as well as weak, negative Ti anomalies. In addition, they have initial 87Sr/86Sr ratios [(87Sr/86Sr)i] of 0.702844–0.703581, initial 143Nd/144Nd ratios [(143Nd/144Nd)i] of 0.512891–0.512959 and high εNd(t) values (+7.9 to +9.3). Uranium-Pb ages of magmatic zircons separated from the investigated metamafic dykes indicate that the parental melts of their protoliths intruded the Sangsang mantle at ~119.0–118.5 ​Ma.The metamorphic mineral assemblages recognized in the investigated dykes are suggestive of a retrograde metamorphic process, from (epidote-)amphibolite facies (~470–610 ​°C, ~1.9–4.3 ​kbar) and to prehnite-pumpellyite facies (≤280 ​°C, ≤ 3 ​kbar), active within a rift-produced oceanic lithosphere. Microtextural and geochemical data suggest that the protoliths of the dykes were most likely massive gabbros. Compositional data show that the parental magmas of the gabbroic protoliths were generated by melting of a depleted mantle (DM) source that had been weakly modified by fluids emanating from a subducted oceanic lithospheric slab. The age of the gabbroic protoliths is slightly younger than the existing ages for ophiolites from the central Yarlung-Zangbo Suture Zone (YZSZ) in the literature (~129–123 ​Ma). We, therefore, suggest that the gabbroic protoliths of the Sangsang metamafic dykes were formed in an incipient forearc setting during Neo-Tethyan subduction re-initiation (Aptian). Our tectonomagmatic model provides insights into the igneous accretion and post-solidification evolution of the oceanic lithosphere in South Tibet.

Abstract Background Total hip arthroplasty (THA) can cause considerable blood loss and perioperative transfusion in ankylosing spondylitis (AS) patients. This study aimed to identify the factors related to blood loss in AS patients with hip involvement undergoing THA. Methods We analyzed 243 AS patients with advanced hip involvement undergoing primary THA from 2012 to 2017. Bilateral THA was performed by a one-stage operation during one general anesthesia session. The patients were divided into three groups according to the grade of blood loss, as determined by the Advanced Trauma Life Support hypovolemic shock classification system. Ordinal logistic regression was used to identify factors associated with blood loss in the patients. Results The proportion of patients who were male, underwent bilateral THA, had a hip range of motion (ROM) = 0°, had a BASRI-hip score of 4, underwent iliopsoas and adductor release, and underwent autologous or allogenic transfusion increased significantly with the grade of blood loss, while that of the patients who received tranexamic acid (TXA) decreased significantly (P < 0.05). The preoperative hemoglobin (Hb) level, hematocrit level, and operating time also increased significantly with the grade of blood loss (P < 0.05). The ordinal logistic regression results identified the factors related to blood loss during THA in AS patients with hip involvement to be the male sex (odds ratio [OR] = 3.287; 95% confidence interval [CI] 1.022, 10.567), bilateral THA (OR = 13.896; 95% CI 4.950, 39.011), hip ROM = 0° (OR = 2.513; 95% CI 1.277, 4.946), an elevated erythrocyte sedimentation rate (ESR) level (OR = 3.042; 95% CI 1.320, 7.014), an elevated preoperative Hb level (OR = 1.043; 95% CI 1.017, 1.070), a long operating time (OR = 1.009; 95% CI 1.003, 1.016), and the administration of TXA (OR = 0.252; 95% CI 0.134, 0.472). Conclusions The male sex, bilateral THA, a hip ROM = 0°, an elevated ESR level, a high preoperative Hb level, and a long operating time are risk factors associated with blood loss in AS patients with hip involvement undergoing THA, while the administration of TXA is a protective factor. These results might help determine the risk of bleeding in the perioperative assessment and develop more efficient blood management strategies for THA in AS patients with hip involvement.

Abstract Objective To assess the clinical effect of staged joint replacement for the treatment of septic arthritic knee and the therapeutic differences between antibiotic cement beads and the tibial plateau spacer. Methods Twenty-three patients (24 knee joints) treated with a staged joint replacement for septic arthritis knee were retrospectively reviewed between March 2014 and April 2018. At the first stage, thorough debridement and irrigation with self-made antibiotic cement beads or tibial plateau spacer were performed. After that, systemic antibiotic treatment was followed; when the infection was surely eliminated, the second-stage TKA was performed. Knee mobility (range of motion, abbreviated to ROM) and function (HSS scores system) were evaluated before surgery, in the interval period, and after joint replacement. Results All patients finished follow-up, and the mean follow-up time was 27.3 months (12–54 months). Each group has one patient replaced with a homotypic spacer, and all patients eventually cleared the infection. None of the patients had a recurrent infection. The mobility and HSS scores of the two groups were significantly improved postoperation (p < 0.05). And there was no significant difference in the post-surgery ROM (p = 0.153) and the HSS score (p = 0.054) between the two groups. Conclusion Staged joint replacement is an efficacious way for septic arthritic knees, whether tibial plateau spacer or antibiotic cement beads were used, which can effectively control infection and improve knee function.

Abstract Background Sudden exacerbations and respiratory failure are major causes of death in patients with severe coronavirus disease 2019(COVID-19) pneumonia, but indicators for the prediction and treatment of severe patients are still lacking. Methods A retrospective analysis of 67 collected cases was conducted and included approximately 67 patients with COVID-19 pneumonia who were admitted to the Suzhou Fifth People’s Hospital from January 1, 2020 to February 8, 2020. The epidemiological, clinical and imaging characteristics as well as laboratory data of the 67 patients were analyzed. Results The study found that fibrinogen (FIB) was increased in 45 (65.2%) patients, and when FIB reached a critical value of 4.805 g/L, the sensitivity and specificity、DA, helping to distinguish general and severe cases, were 100 and 14%、92.9%, respectively, which were significantly better than those for lymphocyte count and myoglobin. Chest CT images indicated that the cumulative number of lung lobes with lesions in severe patients was significantly higher than that in general patients (P

Abstract Background Inflammatory diseases are chronic autoimmune systemic autoimmune diseases, which may increase the risk of prosthetic joint infection (PJI) after total joint arthroplasty (TJA). However, to our best knowledge, few studies have studied the association between inflammatory diseases and subsequent failure after two-stage exchange reimplantation. The aims of this study were to identify the differences in (1) serum markers, synovial indicators and pathology results and (2) treatment outcomes following two-stage exchange arthroplasty between patients with or without inflammatory diseases. Methods A retrospective review of 184 patients with PJI who underwent two-stage revision from 2014 to 2018 was conducted. PJI was diagnosed by using the MSIS criteria. Serum biomarkers, synovial fluid, organism and pathology results at the time of the PJI diagnosis and reimplantation were compared between patients with or without inflammatory diseases. Treatment success was defined according to the Delphi-based consensus criteria; Kaplan-Meier survivorship curves of the patients were generated and compared. Results There was no difference in the biomarkers, pathology results or organism profile at the time of the PJI diagnosis. At reimplantation, the patients with inflammatory diseases generally had higher values of serum markers than those without inflammatory diseases. However, synovial white blood cell count was comparable in patients with inflammatory diseases (1142.8 ± 1385.3*109/mL) and group C (1315.8 ± 1849.3*109/mL, p = 0.841). The total treatment success rate was 91.3% (92% for individuals with inflammatory diseases and 91.2% for the controls). The survivorship of the inflammatory disease group was comparable with that of the control group. Conclusion Two-stage exchange arthroplasty is a viable option for PJIs with inflammatory diseases. Synovial fluid analysis may be less affected by inflammatory diseases than serum markers did in the diagnosis persistent infection at reimplantation.

Abstract Background Because there is no single gold standard method for the diagnosis of periprosthetic joint infection (PJI), the combination of valuable methods to evaluate infection appears to achieve a better diagnostic result. The objective of the present study was to evaluate the diagnostic value of serum interleukin (IL)-6 and C-reactive protein (CRP) for the diagnosis of PJI. Methods PubMed, Embase, and the Web of Science databases were searched for articles describing PJI diagnosis using serum IL-6 and CRP published between January 1990 and December 2019. Results Eight studies were included in the meta-analysis. The pooled sensitivity was 0.84 (95% confidence interval [CI], 0.80−0.88) for the combined method (serum IL-6 and CRP) in series and parallel approaches, 0.87 (95% CI, 0.82−0.90) for IL-6, and 0.84 (95% CI, 0.79−0.88) for CRP. The pooled specificity was 0.85 (95% CI, 0.82−0.88) for the combined method, 0.83 (95% CI, 0.79−0.87) for IL-6, and 0.83 (95% CI, 0.79−0.87) for CRP. The combined method had the highest value for the area under the curve (0.9453), followed by IL-6 (0.9237) and CRP (0.9074). Subgroup analyses showed that the sensitivity of the combined method in parallel tests was higher than that in IL-6 or CRP (94% vs. 89% and 84%, respectively). Serial testing of the combined method showed increased specificity compared to a single indicator (96% vs. 83% and 80%). Conclusion The combination of serum IL-6 and CRP was a reliable tool for the diagnosis of periprosthetic hip and knee infection, demonstrating a better diagnostic accuracy than single marker analysis.

Abstract Background The proper timing of reimplantation is importation to treatment success in the two-stage exchange revision. The 2018 International Consensus Meeting suggested that a variation trend toward normalization in serum markers was useful for determining the proper timing of reimplantation. However, the opposite results were found by previous studies, and the normalization of serum markers was reported to fail to predict infection control. We investigated whether value changes and percent changes in four common serum markers (erythrocyte sedimentation rate (ESR), C-reactive protein (CRP), interleukin-6 (IL-6), and fibrinogen) can predict persistent infection. Methods A retrospective review of 141 patients treated with the two-stage revision from 2014 to 2018 was conducted. The variation trend in serum indicators was evaluated by the percent changes (using values of serum markers pre-reimplantation divided by values pre-resection) and value changes (using values of serum markers pre-resection minus values pre-reimplantation). Treatment success was defined according to the Delphi-based consensus criteria with a minimum follow-up of 1 year, and the receiver operator characteristic (ROC) was used to examine the usefulness of changes in serum markers. Results Twenty-two patients (15.60%) were persistently infected. No significant difference was found in either the value change or percent change in serum markers between reinfection and non-reinfection patients. When predicting persistent infection, the area under the curves (AUC) demonstrated that both percent changes and value changes in serum markers were poor indicators. The AUC of value changes was 0.533 for the CRP, 0.504 for the IL-6, 0.508 for the ESR, and 0.586 for fibrinogen when predicted persistent PJI. In addition, the AUC indicated that percent changes in the CRP (0.464), the IL-6 (0.534), the ESR (0.527), and fibrinogen (0.586) were all poor markers. Conclusions We have shown that both value changes and percent changes in serum markers were not sufficiently rigorous to aid in persistent infection diagnosis. The proper timing of reimplantation must, therefore, take into account various clinical tests rather than the downward trend of serum markers only.

Many grandparents today are physically separated from their families. Given that maintaining close family relationships (with both adult children and grandchildren) is associated with increased physical, mental, and emotional health across generations, it is important to determine how families can maintain close relationships with grandparents when physically separated. Technology offers one potential support: the proliferation of video chat. Recent work suggests that the frequency of video chat and the variety of behaviors engaged in during video calls predicts family closeness and enjoyment of using this communication method, regardless of the physical distance between parties. However, the frequency of grandparent-grandchild video chat varies across families. Here, we explore how demographic, physical (e.g., distance), technological (e.g., number of devices and barriers), and social factors (e.g., children are distracted) predicted: (1) whether or not video chat was used by grandparent-grandchild dyads, and (2) the frequency of video chat in the families using the technology. This work suggests that geographical distance, having met in person, and the number of devices owned were positive predictors of grandparents and grandchildren having ever video chatted. However, the factors associated with the frequency of video chat were different in the parent and grandparent models and included grandparents’ comfort with technology and the type of device used by the parent and child. These findings not only have implications for supporting grandparent-grandchild relationships but also for all family members separated by distance, immigration, incarceration, health emergencies, and displacement.

Post-dryout heat transfer is an important thermal hydraulics phenomenon happening in the loss-of-coolant accident of water-cooled nuclear reactors and the off-design conditions of water-cooled ceramic breeder blanket in fusion reactors. It is necessary to research the flow and heat transfer mechanisms of post-dryout. To economically and comprehensively study on the post-dryout heat transfer characteristics, this work conducted fluid-to-fluid modeling research on water and R-134a data banks by using Buckingham dimensional analysis methodology. In addition, a new interpolation method was developed and verified to make the scaling between the two data banks possible. Finally, a scaling method of post-dryout heat transfer was proposed and assessed by using experimental data. The results showed that the obtained scaling method is reliable in certain conditions.

Deep learning is very promising in solving problems in omics research, such as genomics, epigenomics, proteomics, and metabolics. The design of neural network architecture is very important in modeling omics data against different scientific problems. Residual fully-connected neural network (RFCN) was proposed to provide better neural network architectures for modeling omics data. The next challenge for omics research is how to integrate information from different omics data using deep learning, so that information from different molecular system levels could be combined to predict the target. In this paper, we present a novel multi-omics integration approach named AutoOmics that could efficiently integrate information from different omics data and achieve better accuracy than previous approaches. We evaluated our method on four different tasks: drug repositioning, target gene prediction, breast cancer subtyping and cancer type prediction, and all the four tasks achieved state of art performances.

Deep learning has achieved great successes in traditional fields like computer vision (CV), natural language processing (NLP), speech processing, and more. These advancements have greatly inspired researchers in genomics and made deep learning in genomics an exciting and popular topic. The convolutional neural network (CNN) and recurrent neural network (RNN) are frequently used to solve genomic sequencing and prediction problems, and multiple layer perception (MLP) and auto-encoders (AE) are frequently used for genomic profiling data like RNA expression data and gene mutation data. Here, we introduce a new neural network architecture-the residual fully-connected neural network (RFCN)-and describe its advantage in modeling genomic profiling data. We also incorporate AutoML algorithms and implement AutoGenome, an end-to-end, automated deep learning framework for genomic studies. By utilizing the proposed RFCN architecture, automatic hyper-parameter search, and neural architecture search algorithms, AutoGenome can automatically train high-performance deep learning models for various kinds of genomic profiling data. To help researchers better understand the trained models, AutoGenome can assess the importance of different features and export the most critical features for supervised learning tasks and the representative latent vectors for unsupervised learning tasks. We expect AutoGenome will become a popular tool in genomic studies.