Bayesian network-based survival prediction model for patients having undergone post-transjugular intrahepatic portosystemic shunt for portal hypertension.

BACKGROUND: Portal hypertension (PHT), primarily induced by cirrhosis, manifests severe symptoms impacting patient survival. Although transjugular intrahepatic portosystemic shunt (TIPS) is a critical intervention for managing PHT, it carries risks like hepatic encephalopathy, thus affecting patient survival prognosis. To our knowledge, existing prognostic models for post-TIPS survival in patients with PHT fail to account for the interplay among and collective impact of various prognostic factors on outcomes. Consequently, the development of an innovative modeling approach is essential to address this limitation. AIM: To develop and validate a Bayesian network (BN)-based survival prediction model for patients with cirrhosis-induced PHT having undergone TIPS. METHODS: The clinical data of 393 patients with cirrhosis-induced PHT who underwent TIPS surgery at the Second Affiliated Hospital of Chongqing Medical University between January 2015 and May 2022 were retrospectively analyzed. Variables were selected using Cox and least absolute shrinkage and selection operator regression methods, and a BN-based model was established and evaluated to predict survival in patients having undergone TIPS surgery for PHT. RESULTS: Variable selection revealed the following as key factors impacting survival: age, ascites, hypertension, indications for TIPS, postoperative portal vein pressure (post-PVP), aspartate aminotransferase, alkaline phosphatase, total bilirubin, prealbumin, the Child-Pugh grade, and the model for end-stage liver disease (MELD) score. Based on the above-mentioned variables, a BN-based 2-year survival prognostic prediction model was constructed, which identified the following factors to be directly linked to the survival time: age, ascites, indications for TIPS, concurrent hypertension, post-PVP, the Child-Pugh grade, and the MELD score. The Bayesian information criterion was 3589.04, and 10-fold cross-validation indicated an average log-likelihood loss of 5.55 with a standard deviation of 0.16. The model's accuracy, precision, recall, and F1 score were 0.90, 0.92, 0.97, and 0.95 respectively, with the area under the receiver operating characteristic curve being 0.72. CONCLUSION: This study successfully developed a BN-based survival prediction model with good predictive capabilities. It offers valuable insights for treatment strategies and prognostic evaluations in patients having undergone TIPS surgery for PHT.

Comparison of diagnostic efficiency of detecting IgG and IgE with immunoassay method in diagnosing ABPA: a meta-analysis.

BACKGROUND: Hitherto, the bulk of diagnostic criteria regards Aspergillus-specific immunoglobulin E as a key item, and regard IgG as an auxiliary method in diagnose. Nevertheless, there is no conclusive study in summarize the performance of IgG and IgE diagnosing ABPA. METHODS: We conducted a systematic review to identify studies report results of IgE and IgG detection in diagnosing ABPA. QUADAS-2 tool was used to evaluate included studies, and we applied the HSROC model to calculate the pooled sensitivity and specificity. Deeks' funnel was derived to evaluated the public bias of included studies, and Cochrane Q test and I2 statistic were used to test the heterogeneity. RESULTS: Eleven studies were included in this study (1127 subjects and 215 for IgE and IgG). Deeks's test for IgE and IgG were 0.10 and 0.19. The pooled sensitivity and specificity for IgE were 0.83 (95%CI: 0.77, 0.90) and 0.89 (0.83, 0.94), and for IgG were 0.93 (0.87, 0.97) and 0.73 (0.62,0.82), with P value < 0.001. The PLR and NLR for IgE were 7.80 (5.03,12.10) and 0.19 (0.13,0.27), while for IgG were 3.45 (2.40,4.96) and 0.09 (0.05,0.17). The combined diagnostic odds ratio and diagnostic score were 41.49 (26.74,64.36) and3.73 (3.29,4.16) for IgE, respectively, and were 38.42 (19.23,76.79) and 3.65 (2.96,4.34) for IgG. CONCLUSION: The sensitivity for IgG diagnosing ABPA is higher than IgE, while the specificity for IgE is higher. IgG might be able to play a more important role in filtering ABPA patients.

A new theory to promote self-management of symptom clusters and healthcare quality in patients with decompensated cirrhosis.

Patients with decompensated cirrhosis, a symptomatic phase of cirrhosis, commonly experience multiple symptoms concurrently, referred to as symptom clusters. Effective self-management of symptoms is known to improve outcomes in various chronic diseases. However, a theory for self-management of symptom clusters in decompensated cirrhosis is lacking. In this study, we applied grounded theory research methodology to construct a new theory of self-management of symptom clusters in these patients. This qualitative study prospectively enrolled 20 patients with decompensated cirrhosis within 1 week after hospital admission. Data related to patients' experiences, needs, perspectives, and abilities related to their symptoms were collected via a semi-structured, in-depth interview and analyzed with Nvivo version 20 software. Grounded theory methodology with 3 coding steps (open, axial, and selective coding) was applied to generate a theory of self-management of symptom clusters. From the step-by-step coding process, 2 core categories or major themes were identified: patients' experiences with symptoms and coping with symptoms. The first major theme included symptom clustering, multidimensionality, recurrence, and specificity, while the second consisted of endogenous motivation, endogenous resistance, and external support needs. A new theory of self-management of symptom clusters was then constructed and delineated to enhance self-management among patients with decompensated cirrhosis. Using patient experience data, we developed a new theory of self-management of symptom clusters in patients with decompensated cirrhosis. Use of this theory has the potential to promote patient self-management and guide healthcare providers in planning optimal treatments and implementing timely interventions, ultimately improving in patient outcomes.

Comparison of the clinical features of HIV-positive and HIV-negative hosts infected with Talaromyces marneffei: A multicenter, retrospective study.

OBJECTIVES: Talaromyces marneffei is an emerging pathogen, and the number of infections in HIV-negative individuals is rapidly increasing. Nevertheless, there is no sufficient comprehensive report on this issue, and awareness needs to be raised among clinicians. METHODS: We analyzed the differences in the clinical data of patients who are HIV-negative and HIV-positive with Talaromyces marneffei infection (TMI) from 2018 to 2022. RESULTS: A total of 848 patients were included, among whom 104 were HIV-negative. The obvious differences between the HIV-positive and HIV-negative groups were as follows: (i) the patients who are HIV-negative were older and more likely to exhibit cough and rash, (ii) the time in days from symptom onset to diagnosis among patients who are HIV-negative was longer, (iii) the laboratory findings and radiological presentations seemed more severe in patients who are HIV-negative, (iv) differences were observed regarding the underlying conditions and co-infection pathogens, and correlation analysis showed that correlations existed for many indicators, (v) and persistent infection was more likely to occur in patients who are HIV-negative. CONCLUSION: TMI in patients who are HIV-negative differs from that in patients who are HIV-positive in many aspects, and more investigations are needed. Clinicians should be more aware of TMI in patients who are HIV-negative.

TsOH-catalyzed acyl migration reaction of the Bz-group: innovative assembly of various building blocks for the synthesis of saccharides.

We developed an efficient method to achieve the regioselective acyl migration of benzoyl ester. In all the cases, the reactions required only the commercially available organic acid catalyst TsOH·H2O. This method enables the benzoyl group to migrate from secondary groups to primary hydroxyl groups, or from equatorial secondary hydroxyl groups to axial hydroxyl groups. The 1,2 or 1,3 acyl migration would potentially occur via five- and six-membered cyclic ortho acid intermediates. A wide range of orthogonally protected monosaccharides, which are useful intermediates for the synthesis of natural oligosaccharides, were synthesized. Finally, to demonstrate the utility of the method, a tetrasaccharide portion from a mycobacterial cell wall polysaccharide was assembled.

Mycotic infection as a risk factor for COVID-19: A meta-analysis.

More than 405 million people have contracted coronavirus disease 2019 (COVID-19) worldwide, and mycotic infection may be related to COVID-19 development. There are a large number of reports showing that COVID-19 patients with mycotic infection have an increased risk of mortality. However, whether mycotic infection can be considered a risk factor for COVID-19 remains unknown. We searched the PubMed and Web of Science databases for studies published from inception to December 27, 2021. Pooled effect sizes were calculated according to a random-effects model or fixed-effect model, depending on heterogeneity. We also performed subgroup analyses to identify differences in mortality rates between continents and fungal species. A total of 20 articles were included in this study. Compared with the controls, patients with mycotic infection had an odds ratio (OR) of 2.69 [95% confidence interval (CI): 2.22-3.26] for mortality and an OR of 2.28 (95% CI: 1.65-3.16) for renal replacement therapy (RRT). We also conducted two subgroup analyses based on continent and fungal species, and we found that Europe and Asia had the highest ORs, while Candida was the most dangerous strain of fungi. We performed Egger's test and Begg's test to evaluate the publication bias of the included articles, and the p-value was 0.423, which indicated no significant bias. Mycotic infection can be regarded as a risk factor for COVID-19, and decision makers should be made aware of this risk.

An online technology for effectively monitoring inorganic condensable particulate matter emitted from industrial plants.

The concentration of condensable particulate matter (CPM) has gradually exceeded that of filterable particulate matter emitted from industrial plants equipped with advanced air pollution control systems. However, there is still no available online technology to measure CPM emissions. Based on the significant linear correlations (R2 > 0.87, p < 3 × 10-3) between the electrical conductivity (EC) values and ionic mass concentrations of the CPM solutions when the interference of H+ was excluded. We developed an online inorganic CPM monitoring system, including a cooling and condensation unit, pH and EC meters, a self-cleaning unit, and an automatic control unit. The CPM mass concentrations obtained by the developed online monitoring system agree well (mean bias 3.8-20.7%) with those obtained by the offline system according to USEPA Method 202 when used in parallel during real-world studies. Furthermore, individual ion mass concentrations of CPMs can even be retrieved separately with a time resolution of one hour when industrial plants are under steady operating conditions. The newly developed system makes the online monitoring of CPM emissions available and lays a foundation for the control of CPM emitted from industrial sources to further improve air quality.

Chemical Fingerprinting of HULIS in Particulate Matters Emitted from Residential Coal and Biomass Combustion.

Identification of humic-like substances (HULIS) structures and components is still a major challenge owing to their chemical complexity. This study first employed a complementary method with the combination of two-dimensional gas chromatography-time-of-flight mass spectrometry and liquid chromatography-quadrupole-time-of-flight mass spectrometry to address low-polarity and polar components of HULIS in PM2.5 (particulate matter with an aerodynamic diameter less than 2.5 µm), respectively. The combination method showed a significant correlation in identifying overlapping species and performed well in uncovering the chemical complexity of HULIS. A total of 1246 compound species in HULIS (65.6-81.0% for each sample), approximately 1 order of magnitude more compounds than that reported in previous studies, were addressed in PM2.5 collected in real-world household biomass and coal combustion. Aromatics were the most abundant compounds (37.4-64.1% in biomass and 34.5-70.0% in coal samples) of the total mass in all HULIS samples according to carbon skeleton determination, while the major components included phenols (2.6-21.1%), ketones (6.0-17.1%), aldehydes (1.1-6.8%), esters (2.9-20.0%), amines/amides (3.2-8.5%), alcohols (3.8-17.0%), and acids (4.7-15.1%). Among the identified HULIS species, 11-36% mass in biomass and 11-41% in coal were chromophores, while another 22-35 and 23-29% mass were chromophore precursors, respectively. The combination method shows promise for uncovering HULIS fingerprinting.

[Simplification of crop shortage water index and its application in drought remote sensing monitoring].

Based on the principle of energy balance, the method for calculating latent evaporation was simplified, and hence, the construction of the drought remote sensing monitoring model of crop water shortage index was also simplified. Since the modified model involved fewer parameters and reduced computing times, it was more suitable for the operation running in the routine services. After collecting the concerned meteorological elements and the NOAA/AVHRR image data, the new model was applied to monitor the spring drought in Guanzhong, Shanxi Province. The results showed that the monitoring results from the new model, which also took more considerations of the effects of the ground coverage conditions and meteorological elements such as wind speed and the water pressure, were much better than the results from the model of vegetation water supply index. From the view of the computing times, service effects and monitoring results, the simplified crop water shortage index model was more suitable for practical use. In addition, the reasons of the abnormal results of CWSI > 1 in some regions in the case studies were also discussed in this paper.