The Macrophage migration inhibitory factor is a vital player in Pan-Cancer by functioning as a M0 Macrophage biomarker.

BACKGROUND: The role of the macrophage migration inhibitory factor (MIF) has recently attracted considerable attention in cancer research; nonetheless, the insights provided by current investigations remain constrained. Our main objective was to investigate its role and the latent mechanisms within the pan-cancer realm. METHODS: We used comprehensive pan-cancer bulk sequencing data and online network tools to investigate the association between MIF expression and patient prognosis, genomic instability, cancer cell stemness, DNA damage repair, and immune infiltration. Furthermore, we validated the relationship between MIF expression and M0 macrophages using single-cell datasets, the SpatialDB database, and fluorescence staining. Additionally, we assessed the therapeutic response using the ROC plotter tool. RESULTS: We observed the upregulation of MIF expression across numerous cancer types. Notably, elevated MIF levels were associated with a decline in genomic stability. We found a significant correlation between increased MIF expression and increased expression of mismatch repair genes, stemness features, and homologous recombination genes across diverse malignancies. Subsequently, through an analysis using ESTIMATE and cytokine results, we revealed the involvement of MIF in immune suppression. Then, we validated MIF as a hallmark of the M0 macrophages involved in tumor immunity. Our study suggests an association with other immune-inhibitory cellular populations and restraint of CD8 + T cells. In addition, we conducted a comparative analysis of MIF expression before and after treatment in three distinct sets of therapy responders and non-responders. Intriguingly, we identified notable disparities in MIF expression patterns in bladder urothelial carcinoma and ovarian cancer following particular therapeutic interventions. CONCLUSION: Comprehensive pan-cancer analysis revealed notable enrichment of MIF within M0 macrophages, exerting a profound influence on tumor-associated immunosuppression and the intricate machinery of DNA repair.

Higher plasma aldosterone concentrations in patients with aortic diseases and hypertension: a retrospective observational study.

BACKGROUND: Aortic diseases remain a highly perilous macrovascular condition. The relationship between circulating aldosterone and aortic diseases is rarely explored, thus we investigated the difference in plasma aldosterone concentration (PAC) between patients with and without aortic disease in hypertensive people. METHODS: We analyzed 926 patients with hypertension, ranging in age from 18 to 89 years, who had their PAC measured from the hospital's electronic database. The case group and control group were defined based on inclusion and exclusion criteria. The analysis included general information, clinical data, biochemical data, and medical imaging examination results as covariates. To further evaluate the difference in PAC between primary hypertension patients with aortic disease and those without, we used multivariate logistic regression analysis and also employed propensity score matching to minimize the influence of confounding factors. RESULTS: In total, 394 participants were included in the analysis, with 66 individuals diagnosed with aortic diseases and 328 in the control group. The participants were predominantly male (64.5%) and over the age of 50 (68.5%), with an average PAC of 19.95 ng/dL. After controlling for confounding factors, the results showed hypertension patients with aortic disease were more likely to have high PAC levels than those without aortic disease (OR = 1.138, 95% CI [1.062 to 1.238]). Subgroup analysis revealed consistent relationship between PAC and primary hypertensive patients with aortic disease across the different stratification variables. Additionally, hypertensive patients with aortic disease still have a risk of higher PAC levels than those without aortic disease, even after propensity score matching. CONCLUSIONS: The results of this study suggest that primary hypertensive patients with aortic diseases have elevated levels of PAC, but the causal relationship between PAC and aortic disease requires further study.

Sex-specific differences in the association between steps per day and all-cause mortality among a cohort of adult patients from the United States with congestive heart failure.

BACKGROUND: There is a lack of understanding of how daily step counts differentially affect the risk of all-cause mortality in adult with congestive heart failure (CHF) by sex in the United States (US). OBJECTIVES: To explore the relationship between daily step counts and all-cause mortality in patients with CHF by sex. METHODS: This is a cohort analysis from the National Health and Nutrition Examination Survey from 2005 to 2006. Multiple Cox hazard regression was performed to explore the association of step counts and all-cause mortality in patients with CHF by sex. RESULTS: In this study, 363 unweighted samples were enrolled from NHANES 2005-2006, representing about 8.4 million of the US population. Further, 46.28% were women, and the average age was 46 years. Patients with CHF in the more than 5581 steps/day group (HR, 0.31 [95% CI, 0.16-0.58]) had a significantly reduced risk of all-cause mortality compared with the patients in the less 5581 steps/day group after accounting for all covariates. In men, after accounting for all the covariates, there was a significant difference in more than 5581 steps/day group (HR, 0.33 [95% CI, 0.14-0.76]) on all-cause mortality in men with CHF compared with men in the less than 5581 steps/day group. CONCLUSIONS: Step count is associated with all-cause mortality in patients with CHF. Taking 5581 daily steps was associated with a decreased risk of all-cause mortality in patients with CHF.

Performance Exploration of Ni-Doped MoS2 in CO2 Hydrogenation to Methanol.

The preparation of methanol chemicals through CO2 and H2 gas is a positive measure to achieve carbon neutrality. However, developing catalysts with high selectivity remains a challenge due to the irreversible side reaction of reverse water gas shift (RWGS), and the low-temperature characteristics of CO2 hydrogenation to methanol. In-plane sulfur vacancies of MoS2 can be the catalytic active sites for CH3OH formation, but the edge vacancies are more inclined to the occurrence of methane. Therefore, MoS2 and a series of MoS2/Nix and MoS2/Cox catalysts doped with different amounts are prepared by a hydrothermal method. A variety of microscopic characterizations indicate that Ni and Co doping can form NiS2 and CoS2, the existence of these substances can prevent CO2 and H2 from contacting the edge S vacancies of MoS2, and the selectivity of the main product is improved. DFT calculation illustrates that the larger range of orbital hybridization between Ni and MoS2 leads to CO2 activation and the active hydrogen is more prone to surface migration. Under optimized preparation conditions, MoS2/Ni0.2 exhibits relatively good methanol selectivity. Therefore, this strategy of improving methanol selectivity through metal doping has reference significance for the subsequent research and development of such catalysts.

Admission Lysophosphatidic Acid Is Related to Impaired Kidney Function in Acute Aortic Dissection: 2-Year Retrospective Follow-Up Study.

Background: Delayed treatment of acute aortic dissection (AAD)-related acute kidney injury (AKI) significantly increases the burden of chronic kidney disease (CKD) and mortality. Lysophosphatidic acid (LPA) is a shared mediator of kidney disease and AAD. Here, we evaluated the relationship between LPA and kidney injury in AAD patients. Methods: We measured the plasma concentration of LPA in a cohort of 80 patients with AAD. Least Absolute Shrinkage and Selection Operator (LASSO) regression and Logistic regression were used to evaluate the effect and interaction of LPA on AKI. Additive generalized model and penalized spline method were used to describe the non-linear association. Multivariable analyses with the Cox proportional-hazards model were used for subgroup analysis and interaction in LPA and subsequent CKD. Results: The participant's average age was 54.27 ± 11.00 years, 68.75% of them were males, and the incidence of AKI was 43.75%. Patients with AKI had higher levels of LPA on admission, and the more significant the increase, the higher the risk of AKI. There was a non-linear positive correlation between admission LPA and AKI, and the premeditated inflection point was 346.33 (µg/dL) through two-piecewise linear regression and recursive algorithm. Subgroup analysis identified a stronger association between admission LPA and AKI in the elder, female and medically treated patients. The incidence of CKD was 22.67% in the 2-year follow-up. Patients with subsequent CKD had higher LPA levels on admission in the follow-up cohort, and a similar interaction trend was also observed through Cox proportional-hazards model. Conclusion: Admission LPA levels show a non-linear positive correlation with AKI and increase the risk of subsequent CKD, which is more pronounced in elderly, female, and medically treated patients.

A Prediction Model of the Incidence of Nonalcoholic Fatty Liver Disease With Visceral Fatty Obesity: A General Population-Based Study.

Objective: This study aimed to distinguish the risk variables of nonalcoholic fatty liver disease (NAFLD) and to construct a prediction model of NAFLD in visceral fat obesity in Japanese adults. Methods: This study is a historical cohort study that included 1,516 individuals with visceral obesity. All individuals were randomly divided into training group and validation group at 70% (n = 1,061) and 30% (n = 455), respectively. The LASSO method and multivariate regression analysis were performed for selecting risk factors in the training group. Then, overlapping features were selected to screen the effective and suitable risk variables for NAFLD with visceral fatty obesity, and a nomogram incorporating the selected risk factors in the training group was constructed. Then, we used the C-index, calibration plot, decision curve analysis, and cumulative hazard analysis to test the discrimination, calibration, and clinical meaning of the nomogram. At last, internal validation was used in the validation group. Results: We contract a nomogram and validated it using easily available and cost-effective parameters to predict the incidence of NAFLD in participants with visceral fatty obesity, including ALT, HbA1c, body weight, FPG, and TG. In training cohort, the area under the ROC was 0.863, with 95% CI: 0.84-0.885. In validation cohort, C-index was 0.887, with 95%CI: 0.857-0.888. The decision curve analysis showed that the model's prediction is more effective. Decision curve analysis of the training cohort and validation cohort showed that the predictive model was more effective in predicting the risk of NAFLD in Japanese patients with visceral fatty obesity. To help researchers and clinicians better use the nomogram, our online version can be accessed at https://xy2yyjzyxk.shinyapps.io/NAFLD/. Conclusions: Most patients with visceral fatty obesity have a risk of NALFD, but some will not develop into it. The presented nomogram can accurately identify these patients at high risk.

Numerical Simulation and Experimental Verification of Electric-Acoustic Conversion Property of Tangentially Polarized Thin Cylindrical Transducer.

In solving piezoelectric equations of motion, we established an electric-acoustic equivalent circuit of tangentially polarized thin cylindrical transducers and derived analytical expressions of the electric-acoustic response from the harmonic driving-voltage excitation. To experimentally verify the findings, we manufactured a parallel electric-acoustic transmission network for transducers excited by multifrequency driving signals. We found that the tangentially polarized thin cylindrical transducers achieved a much higher electric-acoustic conversion efficiency than the radially polarized thin cylindrical transducers. The electric-acoustic impulse response of the transducers consisted of a direct-current damping with lower-frequency components, a damping oscillation with higher-frequency elements, and a higher resonant frequency of the transducer over its center frequency. The characteristics of radiated acoustic signals included contributions from the geometrical shape and size of the transducer, the physical parameters of piezoelectric material, the type of driving-voltage signals, and the polarization mode of the transducers. In comparison, our theoretical predictions are in good agreement with experimental observations. It is plausible that using the tangentially polarized thin cylindrical transducers as sensors in the acoustic-logging tool may significantly improve the signal-to-noise ratio of the measured acoustic-logging signals.

Machine Learning Models for Predicting In-Hospital Mortality in Acute Aortic Dissection Patients.

Background: Acute aortic dissection is a potentially fatal cardiovascular disorder associated with high mortality. However, current predictive models show a limited ability to efficiently and flexibly detect this mortality risk, and have been unable to discover a relationship between the mortality rate and certain variables. Thus, this study takes an artificial intelligence approach, whereby clinical data-driven machine learning was utilized to predict the in-hospital mortality of acute aortic dissection. Methods: Patients diagnosed with acute aortic dissection between January 2015 to December 2018 were voluntarily enrolled from the Second Xiangya Hospital of Central South University in the study. The diagnosis was defined by magnetic resonance angiography or computed tomography angiography, with an onset time of the symptoms being within 14 days. The analytical variables included demographic characteristics, physical examination, symptoms, clinical condition, laboratory results, and treatment strategies. The machine learning algorithms included logistic regression, decision tree, K nearest neighbor, Gaussian naive bayes, and extreme gradient boost (XGBoost). Evaluation of the predictive performance of the models was mainly achieved using the area under the receiver operating characteristic curve. SHapley Additive exPlanation was also implemented to interpret the final prediction model. Results: A total of 1,344 acute aortic dissection patients were recruited, including 1,071 (79.7%) patients in the survivor group and 273 (20.3%) patients in non-survivor group. The extreme gradient boost model was found to be the most effective model with the greatest area under the receiver operating characteristic curve (0.927, 95% CI: 0.860-0.968). The three most significant aspects of the extreme gradient boost importance matrix plot were treatment, type of acute aortic dissection, and ischemia-modified albumin levels. In the SHapley Additive exPlanation summary plot, medical treatment, type A acute aortic dissection, and higher ischemia-modified albumin level were shown to increase the risk of hospital-based mortality.

Lysophosphatidic Acid May Be a Novel Biomarker for Early Acute Aortic Dissection.

Background: Misdiagnosis and delayed diagnosis of acute aortic dissection (AAD) significantly increase mortality. Lysophosphatidic acid (LPA) is a biomarker related to coagulation cascade and cardiovascular-injury. The extent of LPA elevation in AAD and whether it can discriminate sudden-onset of acute chest pain are currently unclear. Methods: We measured the plasma concentration of LPA in a cohort of 174 patients with suspected AAD chest pain and 30 healthy participants. Measures to discriminate AAD from other acute-onset thoracalgia were compared and calculated. Results: LPA was significantly higher in AAD than in the AMI, PE, and the healthy (344.69 ± 59.99 vs. 286.79 ± 43.01 vs. 286.61 ± 43.32 vs. 96.08 ± 11.93, P < 0.01) within 48 h of symptom onset. LPA level peaked at 12 h after symptom onset, then gradually decreased from 12 to 48 h in AAD. LPA had an AUC of 0.85 (0.80-0.90), diagnosis threshold of 298.98 mg/dl, a sensitivity of 0.81, specificity of 0.77, and the negative predictive value of 0.85. The ROC curve of LPA is better than D-dimer (P = 0.041, Delong test). The decision curve showed that LPA had excellent standardized net benefits. Conclusion: LPA showed superior overall diagnostic performance to D-dimer in early AAD diagnosis may be a potential biomarker, but additional studies are needed to determine the rapid and cost-effective diagnostic tests in the emergency department.

Soluble expression, rapid purification, biological identification of chicken interferon-alpha using a thioredoxin fusion system in E. coli and its antiviral effects to H9N2 avian influenza virus.

In this paper, we report a soluble expression based on Escherichia coli and two-step purification of a novel thioredoxin-tagged chicken interferon-α fusion protein (Trx-rChIFN-α) by using pET32a(+) expression system. The mature ChIFN-α gene was amplified by Reverse transcriptase-polymerase chain reaction (RT-PCR) and subcloned into pET-32a (+) vector prior to transformation into Rosetta (DE3) competent cells. After IPTG induction, the recombinant fusion protein was expressed efficiently in the soluble fraction. The protein purification was performed by nickel affinity chromatography and DEAE anion exchange chromatography. The purified product has a purity of 95% with a yield of 47.3 mg/L of culture. The specific activity of the fusion protein reaches to 2.0 × 107 IU/mg as determined in the CEF/VSV titration system. After excision of the Trx tag by enterokinase, the remaining solo protein was confirmed as rChIFN-α protein by SDS-PAGE, N-terminal sequencing and mass spectrometry. The effects of this Trx-rChIFN-α fusion protein against H9N2 influenza virus infection were also evaluated in ovo. The results showed that the Trx-rChIFN-α protein could significantly reduce the hemagglutination titer of H9N2 virus, and the H9N2 viruses HA gene copy numbers. These findings will enable us to produce large amount and bio-active rChIFN-α protein for future applications.

Burnout is associated with poor recovery of physical performance and low quality of life in patients after their first episode of acute coronary syndrome: A hospital-based prospective cohort study.

OBJECTIVES: "Burnout" is an affective response that is different to depression or anxiety. Studies on the relationship between burnout and physical recovery after acute coronary syndrome (ACS) in the long-term are lacking, hence the rationale of this study. METHODS: Participants were patients after their first onset of ACS divided into the high burnout group (HBG) and low burnout group (LBG) based on the upper quartile of their burnout score on the day before discharge. At three times (1month, 6months, and 1year) after ACS, participants were scheduled for assessment of physical function and quality of life (QoL). To determine the association between burnout and physical function at different times, as well as for burnout and the QoL scores, generalized estimating equations were conducted. RESULTS: Of the 208 participants, 68 participants were assigned to HBG, and 140 were assigned to LBG. QoL scores in both groups at three times showed that HBG had lower scores than LBG (p<0.01). HBG had lower physical scores at three times (z=7.28, p<0.001). Even after adjustment for confounding factors (age, sex, marital status, socioeconomic status, cigarette/alcohol consumption, ACS type, Killip class upon hospital admission), the difference was significant (z=7.78, p<0.001). CONCLUSIONS: Patients with high burnout have poor physical recovery and low quality of life after ACS.

[Parallel PLS algorithm using MapReduce and its aplication in spectral modeling].

Partial least squares (PLS) has been widely used in spectral analysis and modeling, and it is computation-intensive and time-demanding when dealing with massive data To solve this problem effectively, a novel parallel PLS using MapReduce is proposed, which consists of two procedures, the parallelization of data standardizing and the parallelization of principal component computing. Using NIR spectral modeling as an example, experiments were conducted on a Hadoop cluster, which is a collection of ordinary computers. The experimental results demonstrate that the parallel PLS algorithm proposed can handle massive spectra, can significantly cut down the modeling time, and gains a basically linear speedup, and can be easily scaled up.

Metabolite discovery of helicidum in rat urine with XCMS based on the data of ultra performance liquid chromatography coupled to time-of-flight mass spectrometry.

The present study demonstrates the use of XCMS (various forms (X) of chromatography coupled to mass spectrometry), an open-source software tool primarily used in bioinformatics, on the data of ultra-performance liquid chromatography connected online with a mass spectrometer (UPLC/MS) for the discovery of the metabolites of helicidum in urine after oral single dosage to rats. Helicidum (formaldehydephenyl-O-ß-D-pyranosyl alloside) is the major active component of the fruits of Helicid hilagirica Beed. In China, it is often used in the clinic to treat neurasthenic syndromes, vascular headache, and trigeminal neuralgia with high efficacy and low side effect and toxicity. The urine samples of five rats were collected during 0-4, 4-8, 8-12, 12-16, 16-20, 20-24, 24-32, 32-40, and 40-48 h, respectively, after oral administration of helicidum at a dosage of 25.0 mg/kg. A UPLC coupled to time-of-flight MS (UPLC/TOF MS) was used to analyze the samples. Concerning XCMS, the ".raw" format files were preliminarily converted to the open mzXML format using massWolf-4.3.1 (http://sourceforge.net/projects/sashimi/files/massWolf%20(MassLynx%20converter)/). For converting lots of files a time, we wrote a tool rawTomzXML which also uses massWolf-4.3.1. The data were processed using XCMS version l.26.0 (http://www.bioconductor.org/packages/2.8/bioc/html/xcms.html) running under R version 2.13 (http://http://www.r-project.org/) which provided the running platform for XCMS. The "centWave" method from XCMS was used for chromatographic peak detection. Based on the m/z data of the metabolites obtained by XCMS, MS was used to identify the molecular formula. Nine metabolites were finally found and identified. For six of them, the bio-transformation mechanisms of the parent compound was elucidated: glucuronide conjugation (C(19)H(24)O(14)), reduction (C(13)H(18)O(7)), oxidation (C(13)H(16)O(8)), methylation (C(14)H(18)O(7)), and the mixed transformation of reduction, methylation, and acetylation (C(16)H(22)O(8)). For the other three metabolites, C(11)H(19)N(3)O(9), C(11)H(21)N(3)O(9), and C(14)H(15)NO(7), the bio-transformation mechanisms remain unknown and need further investigation. Calculated as mass of helicidum, the cumulative urine excretion rate of the metabolites was 8.39%. The amount of oxidized helicidum was more than 50% among the metabolites while the parent compound helicidum was 13.28% and the reduced helicidum 11.72%, indicating that oxidation was the major bio-transformation that occurred in vivo.

[A novel online process monitoring method based on near infrared spectroscopy and its application to the column chromatographic separation for traditional Chinese medicine].

Near infrared spectroscopy (NIRS) is a process analysis and monitoring tool with many advantages, while it needs to set up quantitative or discriminative calibration models in advance, and needs to adjust these models when the process conditions are varied, which makes it difficult for ordinary user to take its full advantage of it. To tackle this problem, this paper presented a novel, simple and model-free methodology for online process monitoring based on two reciprocal viewpoints of measuring the variability of spectroscopy-both the similarity and dissimilarity of process spectrum, i.e., the adaptive moving window standard deviation function(AMWSW) and similarity function(S). The methodology was validated by a column chromatography process of traditional Chinese medicine using near infrared spectroscopy. The online trend curves of AMWSW and S obtained by proposed method were validated by a comparison with the content variation curves of multiple indicative components analyzed by high performance liquid chromatography (HPLC), and these trend curves demonstrated their potential for real-time process status monitoring, accurately determining the beginning point, the peak point, the end point of the elution, and the phase change from water solution to ethanol solution. The proposed methodology can also be used to other process analysis techniques, such as ultraviolet/visible, infrared, Raman, fluorescence, chromatograph and mass spectrum.