A Nested Case-Control Study to Explore the Association between Immunoglobulin G N-glycans and Ischemic Stroke

Objective This study prospectively investigates the association between immunoglobulin G(IgG)N-glycan traits and ischemic stroke(IS) risk.Methods A nested case-control study was conducted in the China suboptimal health cohort study,which recruited 4,313 individuals in 2013–2014. Cases were identified as patients diagnosed with IS, and controls were 1:1 matched by age and sex with cases. Ig G N-glycans in baseline plasma samples were analyzed.Results A total of 99 IS cases and 99 controls were included, and 24 directly measured glycan peaks(GPs) were separated from Ig G N-glycans. In directly measured GPs, GP4, GP9, GP21, GP22, GP23, and GP24 were associated with the risk of IS in men after adjusting for age, waist and hip circumference,obesity, diabetes, hypertension, and dyslipidemia. Derived glycan traits representing decreased galactosylation and sialylation were associated with IS in men(FBG2S2/(FBG2 + FBG2S1 + FBG2S2): odds ratio(OR) = 0.92, 95% confidence interval(CI): 0.87–0.97;G1n: OR = 0.74, 95% CI: 0.63–0.87;G0n: OR =1.12, 95% CI: 1.03–1.22). However, these associations were not found among women.Conclusion This study validated that altered Ig G N-glycan traits were associated with incident IS in men, suggesting that sex discrepancies might exist in these associations.

Application of Quality Control Circle Activity in Improving Effectiveness of Drug Intervention in Lung Cancer Patients with Moderate to Severe Pain

Objective:Lung cancer has the highest incidence and mortality of all malignant tumors in China.Cancer pain dramatically affects patients’comfort level,causing insomnia,anorexia,anxiety,fear,depression,and a decline in the quality of life(QOL).The literature suggests a shortage of adequate cancer pain management for 59.1% of patients in China.The quality control circle(QCC)activity reflects the people-oriented core idea of management.This study aimed to assess the efficacy of QCC in enhancing the effectiveness of drug interventions in lung cancer patients with moderate to severe pain.Methods:From January 2019 to July 2019,lung cancer patients with moderate to severe pain were treated with drugs.The total number of drug interventions was 3072.A QCC activity was performed following the ten steps of the plan-do-check-act(PDCA)model.The reasons for the poor effectiveness of drug intervention in lung cancer patients with moderate to severe pain were analyzed.Countermeasures were designed to improve the effectiveness of drug intervention,including setting up a pain college,writing a medication education manual,and formulating operational rules for the administration of narcotic drugs.The effectiveness of drug intervention in lung cancer patients with moderate to severe pain and activity ability scores of QCC members were analyzed statistically before and after QCC activity.The effectiveness of drug intervention was investigated and compared before and after establishing the QCC.Results:After establishing the PDCA model,the effectiveness of drug intervention for moderate to severe pain in lung cancer patients increased from 56.28% to 85.29%.Members had significant improvement in problem-solving ability,responsibility,communication,coordination,self-confidence,team cohesion,enthusiasm,QCC skills,and harmony.Conclusion:QCC activity can significantly improve the efficiency of drug intervention in lung cancer patients with moderate to severe pain and their quality of life.

The Epidemiology, Diagnosis and Prognosis of Long-COVID

INTRODUCTION As of October 26, 2022, more than 625 million confirmed cases of COVID-19 and more than 6.56 million deaths have been reported to the World Health Organization(WHO), and the natural history,clinical course, and long-term consequences of this new disease are still not completely understood.After more than two years of fighting against the SARS-CoV-2 pandemic, the number of patients with long-term persistent COVID-19 symptoms after acute infection is noteworthy.

The critical need to establish standards for data quality in intelligent medicine

Medical artificial intelligence(AI)is an important technical asset to support medical supply-side reforms and national development in the big data era.Clinical data from multiple disciplines represent building blocks for the development and application of AI-aided diagnostic and treatment systems based on medical big data.However,the inconsistent quality of these data resources in AI research leads to waste and inefficiencies.Therefore,it is crucial that the field formulatesthe requirements and content related to data processing as part of the development of intelligent medicine.To promote medical AI research worldwide,the“Belt and Road”International Ophthalmic Artificial Intelligence Research and Development Alliance will establish a series of expert recommendations for data quality in intelligent medicine.

Association of C-reactive Protein with Cardiovascular Outcomes: A Mendelian Randomization Study in the Japanese Population

Objective Traditional epidemiological studies have shown that C-reactive protein(CRP)is associated with the risk of cardiovascular diseases(CVDs).However,whether this association is causal remains unclear.Therefore,Mendelian randomization(MR)was used to explore the causal relationship of CRP with cardiovascular outcomes including ischemic stroke,atrial fibrillation,arrhythmia and congestive heart failure.Methods We performed two-sample MR by using summary-level data obtained from Japanese Encyclopedia of Genetic association by Riken(JENGER),and we selected four single-nucleotide polymorphisms associated with CRP level as instrumental variables.MR estimates were calculated with the inverse-variance weighted(IVW),penalized weighted median and weighted median.MR-Egger regression was used to explore pleiotropy.Results No significant causal association of genetically determined CRP level with ischemic stroke,atrial fibrillation or arrhythmia was found with all four MR methods(all Ps>0.05).The IVW method indicated suggestive evidence of a causal association between CRP and congestive heart failure(OR:1.337,95%CI:1.005–1.780,P=0.046),whereas the other three methods did not.No clear pleiotropy or heterogeneity were observed.Conclusions Suggestive evidence was found only in analysis of congestive heart failure;therefore,further studies are necessary.Furthermore,no causal association was found between CRP and the other three cardiovascular outcomes.

Expert recommendation on collection,storage,annotation,and management of data related to medical artificial intelligence

Medical artificial intelligence(AI)and big data technology have rapidly advanced in recent years,and they are now routinely used for image-based diagnosis.China has a massive amount of medical data.However,a uniform criteria for medical data quality have yet to be established.Therefore,this review aimed to develop a standardized and detailed set of quality criteria for medical data collection,storage,annotation,and management related to medical AI.This would greatly improve the process of medical data resource sharing and the use of AI in clinical medicine.

Characterization and determinant factors of critical illness and in-hospital mortality of COVID-19 patients: A retrospective cohort of 1,792 patients in Kenya

Limited data is available on the coronavirus disease 2019(COVID-19),critical illness rate,and in-hospital mortality in the African setting.This study investigates determinants of critical illness and in-hospital mortality among COVID-19 patients in Kenya.We conducted a retrospective cohort study at Kenyatta National Hospital(KNH)in Kenya.Multivariate logistic regression and Cox proportional hazard regression were employed to determine predictor factors for intensive care unit(ICU)admission and in-hospital mortality,respectively.In addition,the Kaplan-Meier model was used to compare the survival times using log-rank tests.As a result,346(19.3%)COVID-19 patients were admitted to ICU,and 271(15.1%)died.The majority of those admitted to the hospital were male,1,137(63.4%)and asymptomatic,1,357(75.7%).The most prevalent clinical features were shortness of breath,fever,and dry cough.In addition,older age,male,health status,patient on oxygen(O2),oxygen saturation levels(SPO2),headache,dry cough,comorbidities,obesity,cardiovascular diseases(CVDs),diabetes,chronic lung disease(CLD),and malignancy/cancer can predicate the risk of ICU admission,with an area under the receiver operating characteristic curve(AUC-ROC)of 0.90(95%confidence interval[CI]:0.88–0.92).Survival analysis indicated 271(15.1%)patients died and identified older age,male,headache,shortness of breath,health status,patient on oxygen,SPO2,headache,comorbidity,CVDs,diabetes,CLD,malignancy/cancer,and smoking as risk factors for mortality(AUC-ROC:0.90,95%CI:0.89–0.91).This is the first attempt to explore predictors for ICU admission and hospital mortality among COVID-19 patients in Kenya.