Application of Feature, Event, and Process Methods to Leakage Scenario Development for Offshore CO_(2) Geological Storage

Offshore carbon dioxide(CO_(2)) geological storage(OCGS) represents a significant strategy for addressing climate change by curtailing greenhouse gas emissions. Nonetheless, the risk of CO_(2) leakage poses a substantial concern associated with this technology. This study introduces an innovative approach for establishing OCGS leakage scenarios, involving four pivotal stages, namely, interactive matrix establishment, risk matrix evaluation, cause–effect analysis, and scenario development, which has been implemented in the Pearl River Estuary Basin in China. The initial phase encompassed the establishment of an interaction matrix for OCGS systems based on features, events, and processes. Subsequent risk matrix evaluation and cause–effect analysis identified key system components, specifically CO_(2) injection and faults/features. Building upon this analysis, two leakage risk scenarios were successfully developed, accompanied by the corresponding mitigation measures. In addition, this study introduces the application of scenario development to risk assessment, including scenario numerical simulation and quantitative assessment. Overall, this research positively contributes to the sustainable development and safe operation of OCGS projects and holds potential for further refinement and broader application to diverse geographical environments and project requirements. This comprehensive study provides valuable insights into the establishment of OCGS leakage scenarios and demonstrates their practical application to risk assessment, laying the foundation for promoting the sustainable development and safe operation of ocean CO_(2) geological storage projects while proposing possibilities for future improvements and broader applications to different contexts.

MSEs Credit Risk Assessment Model Based on Federated Learning and Feature Selection

Federated learning has been used extensively in business inno-vation scenarios in various industries.This research adopts the federated learning approach for the first time to address the issue of bank-enterprise information asymmetry in the credit assessment scenario.First,this research designs a credit risk assessment model based on federated learning and feature selection for micro and small enterprises(MSEs)using multi-dimensional enterprise data and multi-perspective enterprise information.The proposed model includes four main processes:namely encrypted entity alignment,hybrid feature selection,secure multi-party computation,and global model updating.Secondly,a two-step feature selection algorithm based on wrapper and filter is designed to construct the optimal feature set in multi-source heterogeneous data,which can provide excellent accuracy and interpretability.In addition,a local update screening strategy is proposed to select trustworthy model parameters for aggregation each time to ensure the quality of the global model.The results of the study show that the model error rate is reduced by 6.22%and the recall rate is improved by 11.03%compared to the algorithms commonly used in credit risk research,significantly improving the ability to identify defaulters.Finally,the business operations of commercial banks are used to confirm the potential of the proposed model for real-world implementation.

Liver injury in COVID-19: Clinical features, potential mechanisms, risk factors and clinical treatments

The coronavirus disease 2019(COVID-19)pandemic has been a serious threat to global health for nearly 3 years.In addition to pulmonary complications,liver injury is not uncommon in patients with novel COVID-19.Although the prevalence of liver injury varies widely among COVID-19 patients,its incidence is significantly increased in severe cases.Hence,there is an urgent need to understand liver injury caused by COVID-19.Clinical features of liver injury include detectable liver function abnormalities and liver imaging changes.Liver function tests,computed tomography scans,and ultrasound can help evaluate liver injury.Risk factors for liver injury in patients with COVID-19 include male sex,preexisting liver disease including liver transplantation and chronic liver disease,diabetes,obesity,and hypertension.To date,the mechanism of COVID-19-related liver injury is not fully understood.Its pathophysiological basis can generally be explained by systemic inflammatory response,hypoxic damage,ischemia-reperfusion injury,and drug side effects.In this review,we systematically summarize the existing literature on liver injury caused by COVID-19,including clinical features,underlying mechanisms,and potential risk factors.Finally,we discuss clinical management and provide recommendations for the care of patients with liver injury.

A reliability-oriented genetic algorithm-levenberg marquardt model for leak risk assessment based on time-frequency features

Since leaks in high-pressure pipelines transporting crude oil can cause severe economic losses,a reliable leak risk assessment can assist in developing an effective pipeline maintenance plan and avoiding unexpected incidents.The fast and accurate leak detection methods are essential for maintaining pipeline safety in pipeline reliability engineering.Current oil pipeline leakage signals are insufficient for feature extraction,while the training time for traditional leakage prediction models is too long.A new leak detection method is proposed based on time-frequency features and the Genetic Algorithm-Levenberg Marquardt(GA-LM)classification model for predicting the leakage status of oil pipelines.The signal that has been processed is transformed to the time and frequency domain,allowing full expression of the original signal.The traditional Back Propagation(BP)neural network is optimized by the Genetic Algorithm(GA)and Levenberg Marquardt(LM)algorithms.The results show that the recognition effect of a combined feature parameter is superior to that of a single feature parameter.The Accuracy,Precision,Recall,and F1score of the GA-LM model is 95%,93.5%,96.7%,and 95.1%,respectively,which proves that the GA-LM model has a good predictive effect and excellent stability for positive and negative samples.The proposed GA-LM model can obviously reduce training time and improve recognition efficiency.In addition,considering that a large number of samples are required for model training,a wavelet threshold method is proposed to generate sample data with higher reliability.The research results can provide an effective theoretical and technical reference for the leakage risk assessment of the actual oil pipelines.

Clinical Features of Renal Insufficiency due to Multiple Myeloma and Related Risk Factors

Objective: To study clinical features of the patients with multiple myeloma(MM) accompanied by renal insufficiency and investigate the related risk factors of renalimpairment. Methods: A control study of clinical characteristics was performed between 91 patientswith renal insufficiency due to MM and 165 patients with normal renal function in MM during the sameperiod. The data were statistically analyzed by chi-square test and logistic regression analysis.Results: Renal insufficiency was the initial presentation in 48 (52.7%) of the 91 patients, and 30(62.5%) of the 48 patients were misdiagnosed. The prognosis of group with renal insufficiency wassignificantly poorer than that of group with normal renal function: mortality in 3 months, 3months-1 year was 26/91 vs 14/165 (P 【 0.0001), 14/91 vs 12/165 (P 【 0.05) respectively, andpatients survived 】 1 year was 18/91 vs 95/165 (P 【 0.0001). The incidence of hypercalcemia,hyperuricemic, severe anemia, high serum M-protein concentration and lytic bone lesions weresignificantly higher in renal insufficiency group than those in control group (P 【 0.05). Logisticregression analysis identified 5 risk factors of renal impairment, including, severe anemia(Exp(β)=13.819, P 【 0.0001), use of nephrotoxic drugs (Exp(β)=6.217, P = 0.001), high serumM-protein concentration (Exp(β) = 5.026, P = 0.001), male (Exp(β)=3.745, P=0.006), andhypercalcemia (Exp(β)=3A72, P=0.006), but age, serum density of uric acid, type of serum M-protein,and Bence Jones proteinuria were not significantly associated with renal insufficiency. Conclusion:Renal insufficiency was a common early complication of MM, which often resulted in misdiagnosis.The status of these patients tended to be very bad, with many other complications, when MM wasdiagnosed, so their prognosis was poor. The occurrence of renal insufficiency in patients with MMand hypercalcemia, severe anemia, high serum M-protein concentration, especially use of nephrotoxicdrugs should be alert.

Risk Factors, Clinical Features and Prognosis for Subtypes of Ischemic Stroke in a Chinese Population

It is unanimously accepted that stroke is a highly heterogeneous disorder. Different subtypes of ischemic stroke may have different risk factors, clinical features, and prognoses. The aim of this study was to evaluate the risk factors, clinical characteristics, and prognoses of different subtypes of ischemic stroke defined by the Trial of ORG10172 in Acute Stroke Treatment （TOAST） criteria. We prospectively analyzed the data from 530 consecutive patients who were admitted to our hospital with acute ischemic stroke within 7 days of stroke onset during the study period. Standardized data assessment was used and the cause of ischemic stroke was classified according to the TOAST criteria. Patients were followed up till 30 and 90 days after stroke onset. It was found that large-artery atherosclerosis was the most frequent etiology of stroke （37.4%）, and showed the highest male preponderance, the highest prevalence of previous transient ischemic attack, and the longest hospital stay among all subtypes. Small artery disease （36.4%） was associated with higher body mass index, higher plasma triglycerides, and lower plasma high-density lipoprotein cholesterol than cardioembolism. Cardioembolism （7.7%）, which was particularly common in the elderly （i.e., individuals aged 65 years and older）, showed the highest female preponderance, the highest prevalence of atrial fibrillation, the earliest presentation to hospital after stroke onset, the most severe symptoms on admission, the maximum complications associated with an adverse outcome, and the highest rate of stroke recurrence and mortality. Our results suggest that ischemic stroke should be regarded as a highly heterogeneous disorder. Studies involving risk factors, clinical features, and prognoses of ischemic stroke should differentiate between etiologic stroke subtypes.

Clinical and pathological features and risk factors for primary breast cancer patients

BACKGROUND Breast cancer is the most common malignancy in women all around the world.According to the latest statistics in 2018,there were more than 2.08 million new breast cancer cases all around the world and more than 620000 deaths;the proportion of breast cancer deaths in women with cancer is 15%.By studying age,clinicopathological characteristics and molecular classification,age at menarche,age at birth,number of births,number of miscarriages,lactation time,surgical history of benign breast lesions,history of gynecological diseases,and other factors,we retrospectively summarized and compared the disease history of patients with primary breast cancer and patients with benign thyroid tumors admitted to our hospital in the past 10 years to explore the clinicopathological characteristics and risk factors for primary breast cancer.AIM To investigate the clinical and pathological features and risk factors for primary breast cancer treated at our center in order to provide a reference for the prevention and treatment of breast cancer in the Zhuhai-Macao region.METHODS Through a retrospective case-control study,149 patients with primary breast cancer diagnosed and treated at Zhuhai Hospital of Guangdong Provincial Hospital of Traditional Chinese Medicine from January 2013 to March 2020 were included as a case group,and 165 patients with benign breast tumors diagnosed and treated from January 2019 to March 2020 were included as a control group.The data collected included age,age at menarche,age at first birth,number of births,number of miscarriages,lactation time,history of surgery for benign breast lesions,history of familial malignant tumors,history of gynecological diseases,history of thyroid diseases,and the tumor characteristics of the patients in the case group including pathological diagnosis,pathological type,tumor size,lymph node metastasis,distant metastasis,stage,and molecular classification,among others.In the case group,the chi-square test was used to analyze the clinical and pathological features of patients in three age groups(<40,40-59,and≥60 years).A multifactor logistic regression analysis was used to analyze correlations between the two groups.RESULTS Among 149 patients with primary breast cancer,the average age was 48.20±12.06 years,and the proportion of patients at 40-59 years old was the highest,accounting for 61.8%of cases.The molecular type was mainly luminal B type,accounting for 69.2%of cases,and at the time of diagnosis,the tumor stage was mainly stage I/II,accounting for 62.4%of cases.There were no statistically significant differences in the distributions of tumor location,pathological type,tumor size,lymph node metastasis,stage,or molecular classification among the three age groups(<40,40-59,and≥60 years)(P≥0.05).The differences in the distribution of distant metastasis among the three age groups(<40,40-59,and≥60 years)were statistically significant(P<0.01).The differences in lactation time,history of familial malignant tumors,history of gynecological diseases,and history of thyroid diseases between the two groups were not statistically significant(P≥0.05).The differences in age at disease diagnosis,age at menarche,and history of surgery for benign breast lesions were statistically significant(P<0.01).The difference in age at first birth was also statistically significant(P<0.05).CONCLUSION The highest incidence of breast cancer in the Zhuhai-Macao region is present among women aged 40-59 years.There is a larger proportion of stage I/II patients,and the luminal B type is the most common molecular subtype.Distant metastasis occurs mainly in the≥60-year-old group at the first diagnosis;increased age,late age at menarche,and late age at first birth may be risk factors for primary breast cancer,and a history of surgery for benign breast lesions may be a protective factor for primary breast cancer.

Adaptive target and jamming recognition for the pulse doppler radar fuze based on a time-frequency joint feature and an online-updated naive bayesian classifier with minimal risk

This paper considers the problem of target and jamming recognition for the pulse Doppler radar fuze(PDRF).To solve the problem,the matched filter outputs of the PDRF under the action of target and jamming are analyzed.Then,the frequency entropy and peak-to-peak ratio are extracted from the matched filter output of the PDRF,and the time-frequency joint feature is constructed.Based on the time-frequency joint feature,the naive Bayesian classifier(NBC)with minimal risk is established for target and jamming recognition.To improve the adaptability of the proposed method in complex environments,an online update process that adaptively modifies the classifier in the duration of the work of the PDRF is proposed.The experiments show that the PDRF can maintain high recognition accuracy when the signal-to-noise ratio(SNR)decreases and the jamming-to-signal ratio(JSR)increases.Moreover,the applicable analysis shows that he ONBCMR method has low computational complexity and can fully meet the real-time requirements of PDRF.

Association of insertion-deletions polymorphisms with colorectal cancer risk and clinical features

AIM To investigate the association between 16 insertiondeletions(INDEL) polymorphisms, colorectal cancer(CRC) risk and clinical features in an admixed population.METHODS O n e h u n d re d a n d fo r ty p a t i e n t s w i t h C R C a n d 140 cancer-free subjects were examined. Genomic DNA was extracted from peripheral blood samples. Polymorphisms and genomic ancestry distribution were assayed by Multiplex-PCR reaction, separated by capillary electrophoresis on the ABI 3130 Genetic Analyzer instrument and analyzed on Gene Mapper ID v3.2. Clinicopathological data were obtained by consulting the patients' clinical charts, intra-operative documentation, and pathology scoring.RESULTS Logistic regression analysis showed that polymorphism variations in IL4 gene was associated with increased CRC risk, while TYMS and UCP2 genes were associated with decreased risk. Reference to anatomical localization of tumor Del allele of NFKB1 and CASP8 were associated with more colon related incidents than rectosigmoid. In relation to the INDEL association with tumor node metastasis(TNM) stage risk, the Ins alleles of ACE, HLAG and TP53(6 bp INDEL) were associated with higher TNM stage. Furthermore, regarding INDEL association with relapse risk, the Ins alleles of ACE, HLAG, and UGT1A1 were associated with early relapse risk, as well as the Del allele of TYMS. Regarding INDEL association with death risk before 10 years, the Ins allele of SGSM3 and UGT1A1 were associated with death risk.CONCLUSION The INDEL variations in ACE, UCP2, TYMS, IL4, NFKB1, CASP8, TP53, HLAG, UGT1A1, and SGSM3 were associated with CRC risk and clinical features in an admixed population. These data suggest that this cancer panel might be useful as a complementary tool for better clinical management, and more studies need to be conducted to confirm these findings.

Clinical features and risk factors of acute hepatitis E with severe jaundice

AIM:To compares the clinical features of patients infected with hepatitis E virus(HEV) with or without severe jaundice.In addition,the risk factors for HEV infection with severe jaundice were investigated.METHODS:We enrolled 235 patients with HEV into a cross-sectional study using multi-stage sampling to select the study group.Patients with possible acute hepatitis E showing elevated liver enzyme levels were screened for HEV infection using serologic and molecular tools.HEV infection was documented by HEV antibodies and by the detection of HEV-RNA in serum.We used χ2 analysis,Fisher's exact test,and Student's t test where appropriate in this study.Significant predictors in the univariate analysis were then included in a forward,stepwise multiple logistic regression model.RESULTS:No significant differences in symptoms,alanine aminotransferase,aspartate aminotransferase,al-kaline phosphatase,or hepatitis B virus surface antigen between the two groups were observed.HEV infected patients with severe jaundice had significantly lower peak serum levels of γ-glutamyl-transpeptidase(GGT)(median:170.31 U/L vs 237.96 U/L,P = 0.007),significantly lower ALB levels(33.84 g/L vs 36.89 g/L,P = 0.000),significantly lower acetylcholine esterase(CHE) levels(4500.93 U/L vs 5815.28 U/L,P = 0.000) and significantly higher total bile acid(TBA) levels(275.56 μmol/L vs 147.03 μmol/L,P = 0.000) than those without severe jaundice.The median of the lowest point time tended to be lower in patients with severe jaundice(81.64% vs 96.12%,P = 0.000).HEV infected patients with severe jaundice had a significantly higher viral load(median:134 vs 112,P = 0.025) than those without severe jaundice.HEV infected patients with severe jaundice showed a trend toward longer median hospital stay(38.17 d vs 18.36 d,P = 0.073).Multivariate logistic regression indicated that there were significant differences in age,sex,viral load,GGT,albumin,TBA,CHE,prothrombin index,alcohol overconsumption,and duration of admission between patients infected with acute hepatitis E with and without severe jaundice.CONCLUSION:Acute hepatitis E patients may naturally present with severe jaundice.

Intelligent Feature Selection with Deep Learning Based Financial Risk Assessment Model

Due to global financial crisis,risk management has received significant attention to avoid loss and maximize profit in any business.Since the financial crisis prediction(FCP)process is mainly based on data driven decision making and intelligent models,artificial intelligence(AI)and machine learning(ML)models are widely utilized.This article introduces an intelligent feature selection with deep learning based financial risk assessment model(IFSDL-FRA).The proposed IFSDL-FRA technique aims to determine the financial crisis of a company or enterprise.In addition,the IFSDL-FRA technique involves the design of new water strider optimization algorithm based feature selection(WSOA-FS)manner to an optimum selection of feature subsets.Moreover,Deep Random Vector Functional Link network(DRVFLN)classification technique was applied to properly allot the class labels to the financial data.Furthermore,improved fruit fly optimization algorithm(IFFOA)based hyperparameter tuning process is carried out to optimally tune the hyperparameters of the DRVFLN model.For enhancing the better performance of the IFSDL-FRA technique,an extensive set of simulations are implemented on benchmark financial datasets and the obtained outcomes determine the betterment of IFSDL-FRA technique on the recent state of art approaches.

Stroke Risk Assessment Decision-Making Using a Machine Learning Model:Logistic-AdaBoost

Stroke is a chronic cerebrovascular disease that carries a high risk.Stroke risk assessment is of great significance in preventing,reversing and reducing the spread and the health hazards caused by stroke.Aiming to objectively predict and identify strokes,this paper proposes a new stroke risk assessment decision-making model named Logistic-AdaBoost(Logistic-AB)based on machine learning.First,the categorical boosting(CatBoost)method is used to perform feature selection for all features of stroke,and 8 main features are selected to form a new index evaluation system to predict the risk of stroke.Second,the borderline synthetic minority oversampling technique(SMOTE)algorithm is applied to transform the unbalanced stroke dataset into a balanced dataset.Finally,the stroke risk assessment decision-makingmodel Logistic-AB is constructed,and the overall prediction performance of this new model is evaluated by comparing it with ten other similar models.The comparison results show that the new model proposed in this paper performs better than the two single algorithms(logistic regression and AdaBoost)on the four indicators of recall,precision,F1 score,and accuracy,and the overall performance of the proposed model is better than that of common machine learning algorithms.The Logistic-AB model presented in this paper can more accurately predict patients’stroke risk.

Study on the Features and Risk Assessment of Drought in Guilin

Based on the precipitation data of all counties in Guilin from 1957 to 2010, the analysis has been made on the features of spatial and temporal distribution, the probability of occurrence and the periodic change of drought in Guilin. Afterwards, by using the method of disaster risk assessment, the disaster-causing factors, breed disasters environment and fragility of hazard-bearing body of Guilin drought have been analyzed, and the comprehensive evaluation on drought disaster has been made. The results show that above medium drought in Guilin mainly appeared in au- tumn, followed by winter, while Guilin only suffered from slight drought in spring; the principal period of drought occurrence in Guilin was six years, while its secondary period was two years; on the whole, drought risk was high in the southeast and low in the northwest.

Developing global image feature analysis models to predict cancer risk and prognosis

In order to develop precision or personalized medicine,identifying new quantitative imaging markers and building machine learning models to predict cancer risk and prognosis has been attracting broad research interest recently.Most of these research approaches use the similar concepts of the conventional computer-aided detection schemes of medical images,which include steps in detecting and segmenting suspicious regions or tumors,followed by training machine learning models based on the fusion of multiple image features computed from the segmented regions or tumors.However,due to the heterogeneity and boundary fuzziness of the suspicious regions or tumors,segmenting subtle regions is often difficult and unreliable.Additionally,ignoring global and/or background parenchymal tissue characteristics may also be a limitation of the conventional approaches.In our recent studies,we investigated the feasibility of developing new computer-aided schemes implemented with the machine learning models that are trained by global image features to predict cancer risk and prognosis.We trained and tested several models using images obtained from full-field digital mammography,magnetic resonance imaging,and computed tomography of breast,lung,and ovarian cancers.Study results showed that many of these new models yielded higher performance than other approaches used in current clinical practice.Furthermore,the computed global image features also contain complementary information from the features computed from the segmented regions or tumors in predicting cancer prognosis.Therefore,the global image features can be used alone to develop new case-based prediction models or can be added to current tumor-based models to increase their discriminatory power.

Development of a machine learning-based model for predicting risk of early postoperative recurrence of hepatocellular carcinoma

BACKGROUND Surgical resection is the primary treatment for hepatocellular carcinoma(HCC).However,studies indicate that nearly 70%of patients experience HCC recurrence within five years following hepatectomy.The earlier the recurrence,the worse the prognosis.Current studies on postoperative recurrence primarily rely on postoperative pathology and patient clinical data,which are lagging.Hence,developing a new pre-operative prediction model for postoperative recurrence is crucial for guiding individualized treatment of HCC patients and enhancing their prognosis.AIM To identify key variables in pre-operative clinical and imaging data using machine learning algorithms to construct multiple risk prediction models for early postoperative recurrence of HCC.METHODS The demographic and clinical data of 371 HCC patients were collected for this retrospective study.These data were randomly divided into training and test sets at a ratio of 8:2.The training set was analyzed,and key feature variables with predictive value for early HCC recurrence were selected to construct six different machine learning prediction models.Each model was evaluated,and the bestperforming model was selected for interpreting the importance of each variable.Finally,an online calculator based on the model was generated for daily clinical practice.RESULTS Following machine learning analysis,eight key feature variables(age,intratumoral arteries,alpha-fetoprotein,preoperative blood glucose,number of tumors,glucose-to-lymphocyte ratio,liver cirrhosis,and pre-operative platelets)were selected to construct six different prediction models.The XGBoost model outperformed other models,with the area under the receiver operating characteristic curve in the training,validation,and test datasets being 0.993(95%confidence interval:0.982-1.000),0.734(0.601-0.867),and 0.706(0.585-0.827),respectively.Calibration curve and decision curve analysis indicated that the XGBoost model also had good predictive performance and clinical application value.CONCLUSION The XGBoost model exhibits superior performance and is a reliable tool for predicting early postoperative HCC recurrence.This model may guide surgical strategies and postoperative individualized medicine.

Safety Risk Assessment of Overturning Construction of Towering Structure Based on Cloud Matter–Element Coupled Model

Rapid urbanization has led to a surge in the number of towering structures,and overturning is widely used because it can better accommodate the construction of shaped structures such as variable sections.The complexity of the construction process makes the construction risk have certain randomness,so this paper proposes a cloudbased coupled matter-element model to address the ambiguity and randomness in the safety risk assessment of overturning construction of towering structures.In the pretended model,the digital eigenvalues of the cloud model are used to replace the eigenvalues in the matter–element basic element,and calculate the cloud correlation of the risk assessment metrics through the correlation algorithm of the cloud model to build the computational model.Meanwhile,the improved hierarchical analysis method based on the cloud model is used to determine the weight of the index.The comprehensive evaluation scores of the evaluation event are then obtained through the weighted average method,and the safety risk level is determined accordingly.Through empirical analysis,(1)the improved hierarchical analysis method based on the cloud model can incorporate the data of multiple decisionmakers into the calculation formula to determine theweights,which makes the assessment resultsmore credible;(2)the evaluation results of the cloud-basedmatter-element coupledmodelmethod are basically consistent with those of the other two commonly used methods,and the confidence factor is less than 0.05,indicating that the cloudbased physical element coupled model method is reasonable and practical for towering structure overturning;(3)the cloud-based coupled element model method,which confirms the reliability of risk level by performing Spearman correlation on comprehensive assessment scores,can provide more comprehensive information of instances compared with other methods,and more comprehensively reflects the fuzzy uncertainty relationship between assessment indexes,which makes the assessment results more realistic,scientific and reliable.

Analysis of clinical characteristics and risk factors between elderly patients with severe and nonsevere Omicron variant infection

BACKGROUND Coronavirus disease 2019(COVID-19),caused by severe acute respiratory syndrome coronavirus 2(SARS-CoV-2),has led to millions of confirmed cases and deaths worldwide.Elderly patients are at high risk of developing and dying from COVID-19 due to advanced age,decreased immune function,intense inflammatory response,and comorbidities.Shanghai has experienced a wave of infection with Omicron,a new variant of SARS-CoV-2,since March 2022.There is a pressing need to identify clinical features and risk factors for disease progression among elderly patients with Omicron infection to provide solid evidence for clinical policy-makers,public health officials,researchers,and the general public.AIM To investigate clinical characteristic differences and risk factors between elderly patients with severe and nonsevere Omicron SARS-CoV-2 variant infection.METHODS A total of 328 elderly patients with COVID-19 admitted to the Ninth People’s Hospital Affiliated to Shanghai Jiao Tong University School of Medicine from April 2022 to June 2022 were enrolled and divided into a severe group(82 patients)and a nonsevere group(246 patients)according to the diagnosis and treatment protocol of COVID-19(version 7).The clinical data and laboratory results of both groups were collected and compared.A chi-square test,t test,Mann-Whitney U test,hierarchical log-rank test,univariate and multivariate logistic regression,and hierarchical analyses were used to determine significant differences.RESULTS The severe group was older(84 vs 74 years,P<0.001),included more males(57.3%vs 43.9%,P=0.037),had a lower vaccination rate(P<0.001),and had a higher proportion of comorbidities,including chronic respiratory disease(P=0.001),cerebral infarction(P<0.001),chronic kidney disease(P=0.002),and neurodegenerative disease(P<0.001),than the nonsevere group.In addition,severe disease patients had a higher inflammatory index(P<0.001),greater need for symptomatic treatment(P<0.001),longer hospital stay(P=0.011),extended viral shedding time(P=0.014),and higher mortality than nonsevere disease patients(P<0.001).No difference was observed in the application of Paxlovid in the severe and nonsevere groups(P=0.817).Oxygen saturation,cerebral infarction,and D-dimer were predictive factors for developing severe disease in patients with COVID-19,with D-dimer having an excellent role(area under the curve:90.1%,95%CI:86.1-94.0%).In addition,D-dimer was a risk factor for developing severe COVID-19 according to multivariate stratified analysis.CONCLUSION The clinical course of severe COVID-19 is complex,with a higher need for symptomatic treatment.D-dimer is a suitable biomarker for identifying patients at risk for developing severe COVID-19.

甘肃临夏积石山县6.2级地震地质灾害发育特征及危险性评价

2023年12月18日23时59分,甘肃省临夏州积石山县发生Ms6.2级地震,诱发了大量崩滑地质灾害,严重威胁人民生命财产安全。基于震区现场实地调查成果,开展地震诱发加剧地质灾害的发育特征、危险性评价研究,提出防灾减灾措施建议。根据实地调查统计,此次地震后新增地质灾害隐患点64处,加剧的地质灾害隐患点有63处。新增和变形加剧的隐患点以崩塌为主,滑坡次之;规模等级以小型为主,中型次之。崩滑地质灾害主要集中在黄土丘陵区,以黄土陡坎地带切坡建房、切坡修路为主。利用GIS技术的加权信息量法评价积石山县域地质灾害易发性。结果显示,高易发区、中易发区、低易发区、非易发区占比分别为5.45%、9.83%、32.70%和52.02%,其中高易发区主要分布在积石山东部黄土丘陵区的山梁地带。基于区域地质灾害易发性评价,开展地震活动断裂、地震动分布以及不同降雨工况条件(10年、20年、50年、100年一遇)下积石山县域地质灾害危险性评价。结果显示:100年一遇极高危险区较10年一遇极高危险区的增幅最大为18.26%,说明未来区内遭遇极端降雨会显著提高区内地质灾害危险程度。研究认为,积石山地震地质灾害后效应将增强,未来崩塌、滑坡发生频次升高,地质灾害易在降水、冻融条件下形成,需针对性地采取防控措施,有效降低其威胁程度。

泰安地区急性肺栓塞患者临床特征及相关危险因素分析

目的分析泰安地区急性肺栓塞(APE)住院患者的流行病学特点和临床特征,探讨APE患者住院死亡的相关危险因素。方法收集自2016年10月1日至2021年10月1日期间于泰安市中心医院收治的APE患者,根据肺栓塞简化危险分层评估表(sPESI)进行分组,回顾性比较高危组与非高危组患者的流行病学特点、临床特征、治疗及预后情况。将APE研究组与对照组(同期健康查体者)比较,分析白细胞计数、D-二聚体、超敏C反应蛋白(hs-CRP)、载脂蛋白A1(apoA1)等生化指标以及超声心动图等数据进行统计学及多元logistic回归分析,研究其与APE发病及预后的相关性。结果本研究纳入467例APE患者,其中男性212例,女性255例。近年来APE发病率逐年增加,以冬季发病为主。初诊患者主要症状为呼吸困难及气促(憋喘),其次为咳嗽、心悸、胸痛、晕厥和咯血等。泰安市APE患者危险因素占比最高的为下肢静脉血栓及静脉曲张。计算机体层成像肺血管造影(CTPA)结合静态超声心动图(UCG)可提高疾病的临床诊断率。高危组肺动脉高压比例及右室增大比例高于非高危组。所有确诊患者治疗初期均接受抗凝治疗(除外无法抗凝者),好转392例,死亡11例,自动出院29例。吸烟、年龄、低血压或休克、呼吸困难、胸腔积液、肺部感染、sPESI为APE死亡的独立危险因素。结论尽早抗凝是APE患者的有效治疗方案,进一步延长患者生存期,降低死亡率。

63例儿童腐蚀性食管炎内镜特征及危险因素分析

目的分析63例儿童腐蚀性食管炎的内镜特征及危险因素。方法回顾性分析2018年3月至2023年10月郑州大学附属儿童医院消化内科收治的63例腐蚀性食管炎的患者资料,根据内镜诊断分级分为0~1级组(n=16)和2~3级组(n=47),采用单因素分析及多因素logistic回归分析其影响因素。结果63例患者中,最常见的症状是口唇红肿糜烂,占52.38%(33/63);食管炎损伤程度处于0级4例,1级12例,2a级27例,2b级18例,3级2例。单因素分析结果显示,两组在年龄、居住地、误服腐蚀性物质性质、误服量及误服腐蚀性物质浓度方面比较,差异具有统计学意义(P<0.05)。Logistic回归分析结果显示,年龄≤3岁(OR=1.635,P=0.029)、居住地为城市(OR=2.302,P=0.001)、误服碱性腐蚀性物质(OR=7.265,P=0.001)、误服量>5 mL(OR=3.274,P=0.035)、误服高浓度腐蚀性物质(OR=4.552,P=0.017)是儿童腐蚀性食管炎内镜分级的危险因素。结论儿童腐蚀性食管炎以食管浅表溃疡形成为主要表现;年龄≤3岁、居住地为城市、误服碱性腐蚀性物质、误服量>5 mL以及误服高浓度腐蚀性物质是儿童腐蚀性食管炎的危险因素。