The correlations among racial/ethnic groups,hypertriglyceridemia, thrombosis,and mortality in hospitalized patients with COVID-19

Reports of racial and ethnic disparities regarding both rates of infection of the SARS-CoV-2 virus and morbidity of the coronavirus disease-19 (COVID-19) contain profound differences depending on the population. Our previous study has shown that patients with COVID-19 who developed hypertriglyceridemia during hospitalization have a 2.3 times higher mortality rate. However, whether the correlation between hypertriglyceridemia and mortality has disparity among different racial and ethnic groups is unknown.In this study, we investigated the impact of race/ethnicity on the correlation between hypertriglyceridemia and mortality in hospitalized patients with COVID-19. De-identified information from 904 hospitalized patients diagnosed with COVID-19 between March 2020 and June 2021 were extracted from the Medical College of Wisconsin Clinical Data Warehouse. A multivariable regression analysis suggested that the Asians and non-White Hispanics had 4 or 3.9 times higher mortality rate, respectively, after adjusting for age, morbid obesity (BMI ≥40), and gender. The hypertriglyceridemia (≥150 mg/dL) was associated with higher mortality, after adjusting for age, gender, and morbid obesity. The baseline hypertriglyceridemia occurrence had relevantly more consistent percentages among all racial/ethnic groups. However, non-White Hispanic and Asian patients had the highest frequencies of peak hypertriglyceridemia occurrence during hospitalization. The peak hypertriglyceridemia developed during hospitalization correlates with the incidence of thrombosis after adjusting for morbid obesity, age, and sex. In summary, in this retrospective study of 904 hospitalized COVID-19 patients, Asians and non-White Hispanics had a greater likelihood of developing hypertriglyceridemia during hospitalization and mortality than White patients.     

自制康复助走器加综合疗法治疗下肢瘫痪疗效观察

为探讨自制康复助走器治疗对下肢瘫痪的临床效果，将100例下肢瘫痪患者随机分为康复组，对照组各50例。对照组应用常规治疗，康复组在常规治疗基础上加用自制康复助走器 ；3个月后评定疗效。结果康复组站位于平衡功能Ⅱ级和Ⅲ级者占50％，运动功能BrunnstromⅣ级以上者占74％，步行速度明显提高者占84％，均明显高于对照组（P均＜0．01）。证实康复助走器治疗下肢瘫痪疗效显著。     

Real-time toxicity prediction of Aconitum stewing system using extractive electrospray ionization mass spectrometry

Due to numerous obstacles such as complex matrices, real-time monitoring of complex reaction systems (e.g., medicinal herb stewing system) has always been a challenge though great values for safe and rational use of drugs. Herein, facilitated by the potential ability on the tolerance of complex matrices of extractive electrospray ionization mass spectrometry, a device was established to realize continuous sampling and real-time quantitative analysis of herb stewing system for the first time. A complete analytical strategy, including data acquisition, data mining, and data evaluation was proposed and implemented with overcoming the usual difficulties in real-time mass spectrometry quantification. The complex Fuzi (the lateral root of Aconitum)–meat stewing systems were real-timely monitored in 150 min by qualitative and quantitative analysis of the nine key alkaloids accurately. The results showed that the strategy worked perfectly and the toxicity of the systems were evaluated and predicated accordingly. Stewing with trotters effectively accelerated the detoxification of Fuzi soup and reduced the overall toxicity to 68%, which was recommended to be used practically for treating rheumatic arthritis and enhancing immunity. The established strategy was versatile, simple, and accurate, which would have a wide application prospect in real-time analysis and evaluation of various complex reaction systems.     

Defensins: Natural component of human innate immunity

The widespread use of antibiotics has contributed to a huge increase in the number of resistant bacteria. New classes of drugs are therefore being developed of which defensins are a potential source. Defensins are a group of antimicrobial peptides found in different living organisms, involved in the first line of defense in their innate immune response against pathogens. This review summarizes the results of studies of this family of human antimicrobial peptides (AMPs). There is a special emphasis on describing the entire group and individual peptides, history of their discovery, their functions and expression sites. The results of the recent studies on the use of the biologically active peptides in human medicine are also presented. The pharmaceutical potential of human defensins cannot be ignored, especially considering their strong antimicrobial activity and properties such as low molecular weight, reduced immunogenicity, broad activity spectrum and resistance to proteolysis, but there are still many challenges and questions regarding the possibilities of their practical application.     

Improving knowledge of patient skills thanks to automatic analysis of online discussions

Objective

Automatically analyze the online discussions related to diabetes and extract information on patient skills for managing this disease.

Methods

Two collections of about 7000 and 23,000 messages from online discussion fora and 174 skills from an available taxonomy are processed with Natural Language Processing methods and semantically enriched. Skills are projected on the messages to detect those skills which are mentioned by patients. Quantitative and qualitative evaluation is performed.

Results

The method recognizes almost all the aimed skills in fora. The quality of the skills’ recognition varies with the method's parameters. Most of the selected messages are relevant to at least one of the associated skills. Manual analysis shows a substantial number of messages is dedicated to daily self-care and psychosocial skills.

Conclusion

Study of real exchanges between patients leads to a better understanding of their skills in daily self-management of diabetes.

Practice implications

Our experiments can be useful for a better understanding and better knowledge of self-management of diseases by patients. They can also refine existing patient education programs.     

Prediction of delirium using data mining: A systematic review

Delirium remains a significant cause of morbidity, mortality and economic burden to society. “Big data” refers to data of significantly large volume, obtained from a variety of resources, which is created and processed at high velocity. We conducted a systematic review and meta-analysis exploring whether big data could predict the incidence of delirium of patients in the inpatient setting. Medline, Embase, the Cochrane Library, Web of Science, CINAHL, clinicaltrials.gov, who.int and IEEE Xplore were searched using MeSH terms “big data”, “data mining”, “delirium” and “confusion” up to 30th September 2019. We included both randomised and observational studies. The primary outcome of interest was development of delirium and the secondary outcomes of interest were type of statistical methods used, variables included in the mining algorithms and clinically important outcomes such as mortality and length of hospital stay. The quality of studies was graded using the CHARMs checklist. Six retrospective single centre observational studies were included (n = 178,091), of which 17, 574 participants developed delirium. Studies were of generally of low to moderate quality. The most commonly studied method was random forest, followed by support vector machine and artificial neural networks. The model with best performance for delirium prediction was random forest, with area under receiver operating curve (AUROC) ranging from 0.78 to 0.91. Sensitivity ranged from 0.59 to 0.81 and specificity ranged from 0.73 to 0.92. Our systematic review suggests that machine-learning techniques can be utilised to predict delirium.     

Interferon-Gamma Producing Regulatory T Cells as a Diagnostic and Therapeutic Tool in Organ Transplantation

There is increasing evidence that IFNg plays a major role in both induction of Tregs as well as immunosuppression mediated by IFNg-producing Tregs. The present review focuses on a small subset of iTregs that produces IFNg, comprises only 0.04% of all CD4⁺ T lymphocytes in the blood of healthy individuals, and increases strongly during an immune response. IFNg⁺ Tregs are induced by IFNg and IL12, making them sensors for inflammatory cytokines. They develop rapidly during inflammation and represent the first line of Tregs that suppress initial immune responses. The pool of IFNg⁺ Tregs consists of activated stable immunosuppressive thymus-derived nTregs as well as peripherally proliferating iTregs with in part only transient immunosuppressive function, which limits their diagnostic and therapeutic usefulness in organ transplantation. Apparently, a part of IFNg⁺ Tregs dies during the immune response, whereas others, after efficient immunosuppression with resolution of the immune response, differentiate toward Th1 lymphocytes. Goals of further research are the development of appropriate diagnostic tests for rapid and exact determinination of immunosuppressive IFNg⁺ iTregs, as well as the induction and propagation of stable immunosuppressive IFNg⁺ Tregs that establish and maintain good long-term graft function in transplant recipients.     

Identification of key genes and pathways in scleral extracellular matrix remodeling in glaucoma: Potential therapeutic agents discovered using bioinformatics analysis

Background: Glaucoma is a leading cause of irreversible blindness. Remodeling of the scleral extracellular matrix (ECM) plays an important role in the development of glaucoma. The aim of this study was to identify the key genes and pathways for the ECM remodeling of sclera in glaucoma by bioinformatics analysis and to explore potential therapeutic agents for glaucoma management.Methods: Genes associated with glaucoma, sclera and ECM remodeling were detected using the text mining tool pubmed2ensembl, and assigned Gene Ontology (GO) biological process terms and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways using the GeneCodis program. A protein-protein interaction (PPI) network was constructed by STRING and visualized in Cytoscape, module analysis was performed using the Molecular Complex Detection (MCODE) plugin, and GO and KEGG analyses of the gene modules were performed using the Database of Annotation, Visualization and Integrated Discovery (DAVID) platform. The genes that clustered in the significant module were selected as core genes, and functions and pathways of the core genes were visualized using ClueGO and CluePedia. Lastly, the drug-gene interaction database was used to explore drug-gene interactions of the core genes to find drug candidates for glaucoma.Results: We identified 125 genes common to “Glaucoma”, “Sclera”, and “ECM remodeling” by text mining. Gene functional enrichment analysis yielded 30 enriched GO terms and 20 associated KEGG pathways. A PPI network that included 60 nodes with 249 edges was constructed, and three gene modules were obtained using the MCODE. We selected 13 genes that clustered in module 1 as core candidate genes that were associated mainly with ECM degradation and cell proliferation and division. The HIF-1 signaling pathway, FOXO signaling pathway, PI3K-Akt signaling pathway and TGFB signaling pathway were found to be enriched. We found that 11 of the 13 selected genes could be targeted by 26 existing drugs.Conclusions: The results showed that VEGFA, TGFB1, TGFB2, TGFB3, IGF2, IGF1, EGF, FN1, KNG1, TIMP1, SERPINE1, THBS1, and VWF were potential key genes involved to scleral ECM remodeling. Furthermore, 26 drugs were identified as potential therapeutic agents for glaucoma treatment and management.