Preliminary Exploration of the Initial Diagnostic Prediction Model of Moderate Coronavirus Disease 2019 (2019-nCoV) Based on Clinical Data

Objective: To explore those differences and relationships of the initial diagnostic clinical data between confirmed cases of 2019-nCoV and suspected cases of COVID-19, and then to establish prediction models for predicting the probability of the first diagnosis of 2019-nCoV. Methods: A total of 81 suspected cases and 87 confirmed cases of moderate 2019-nCoV diagnosed initially in the isolation wards of the First People’s Hospital of Wuhu and the People’s Hospital of Wuwei and Wuhan Caidian Module Hospital with the help of our hospital doctors were gathered, and retrospectively analyzed. Results: The most common symptoms were fever (76.79%) and cough (64.29%) in the total of 168 cases. The median age was 45 (35 - 56) years old in the 87 confirmed cases of moderate 2019-nCoV, older than the median age 36 (29 - 50) in the 81 suspected cases. There were significant more in the former than in the latter in the incidence of myalgia, ground-glass opacity (GGO), invasions of lesion in the peripheral lobes, vascular thickening and bronchial wall thickening, interlobular septal thicking, and small pulmonary nodules. On the contrary, there were less in the former than in the latter in the total number of leukocytes and neutrophils in blood routine examination and the levels of procalcitonin (PCT). Two groups were statistically significantly different (P < 0.05). Multivariate logistic regression analysis showed that age, fever, myalgia, GGO, vascular thickening and bronchial wall thickening, invasions of lesion in the peripheral lobes were independent factors for identification of 2019-nCoV, and the total number of leukocytes, cough, pharyngalgia and headache were negatively related. The established mathematical equation for predicting model for predicting the probability of the first diagnosis of 2019-nCoV is: P = ex/(1 + ex), x = −6.226 + (0.071 × ages) + (1.720 × fever) + (2.858 × myalgia) + (2.131 × GGO) + (3.000 × vascular thickening and bron-chial wall thickening) + (3.438 × invasions of lesion in the peripheral lobes) + (−0.304 × the number of leukocytes) + (−1.478 × cough) + (−1.830 × pharyngalgia) + (−2.413 × headache), where e is a natural logarithm. The area under the ROC curve (AUC) of this model was calculated to be 0.945 (0.915 - 0.976). The sensitivity is 0.920 and the specificity is 0.827 when the appropriate critical point is 0.360. Conclusions: A mathematical equation prediction model for predicting the probability of the first diagnosis of 2019-nCoV can be established based on the initial diagnostic clinical data of moderate 2019-nCoV. The prediction model is a good assistant diagnostic method for its high accurateness.

AB047.Quebec database of clinical data and biological material for research on uveal melanoma

Background:Sharing biological material and clinical data from patients with uveal melanoma.Methods:Uveal melanoma is the most common intraocular malignancy in the adult population.Because uveal melanoma is primarily a sporadic cancer and familial cases are rare,it is difficult to prevent or detect it.Despite effective treatment of ocular tumors,more than 50%of patients develop incurable liver metastases mainly in the 5-10 years following the detection of the primary tumor.This cancer is relatively rare and the obtained biopsies are very small.About 20 samples are taken each year in Quebec.This provincial infrastructure is made of biological material from donors with uveal melanoma and a large clinical database.Collected tumor biopsies are used for culturing cell lines and the creation of a DNA/RNA library used for genomic and genetic studies.Results:This infrastructure plays an important role in the achievement of various research programs for a better understanding of genetic and environmental factors involved in the development of melanoma and the spread of metastasis.It allows collaboration with other researchers at a provincial,national and international level in order to make progress in basic and clinical research on uveal melanoma.Conclusions:The biological material and clinical data of this infrastructure are available upon request to VHRN members whose research project was approved by the ethics committee of the institution.

Traditional Chinese Medicine Constitution and Clinical Data Association with Machine Learning for Prediction of Spontaneous Abortion

Background:Early prevention of Spontaneous Abortion(SA)is essential for the treatment of recurrent spontaneous abortion.Objective:In this retrospective study,a variety of machine learning methods were used to develop predictive models and diagnose the potential risk of SA.Methods:A total of 663 pregnant women participated in the case-control study,586 of which were SA patients and 77 were normal parturition women.The research data included 25 features of Traditional Chinese Medicine(TCM)constitution and clinical data related to SA.This work utilized 8 machine learning techniques including logistic regression,gradient boosting decision tree,k-nearest neighbor,classification and r-egression tree,multilayer perceptron,support vector machine,random forest and XG-Boost to predict SA.The performances of the applied models were evaluated by using the method of 10-fold cross-validation and by computing the diagnostic test characteristics,including accuracy,precision,recall,𝐹1 score,and the AUC of ROC curve.Results:The𝐹1 scores of these eight machine learning techniques were all above 97.5%.Among them,gradient boosting decision tree had the best prediction result on SA.The accuracy,precision,recall,𝐹1 score,and the AUC of ROC curve of gradient boosting decision tree were 97.9%,99%,98.6%,98.8%,and 97.3%,respectively.Conclusion:The paper has accurately predicted the risk of SA combined with TCM constitution and clinical data.

Data mining in clinical big data:the frequently used databases,steps,and methodological models

Many high quality studies have emerged from public databases,such as Surveillance,Epidemiology,and End Results(SEER),National Health and Nutrition Examination Survey(NHANES),The Cancer Genome Atlas(TCGA),and Medical Information Mart for Intensive Care(MIMIC);however,these data are often characterized by a high degree of dimensional heterogeneity,timeliness,scarcity,irregularity,and other characteristics,resulting in the value of these data not being fully utilized.Data-mining technology has been a frontier field in medical research,as it demonstrates excellent performance in evaluating patient risks and assisting clinical decision-making in building disease-prediction models.Therefore,data mining has unique advantages in clinical big-data research,especially in large-scale medical public databases.This article introduced the main medical public database and described the steps,tasks,and models of data mining in simple language.Additionally,we described data-mining methods along with their practical applications.The goal of this work was to aid clinical researchers in gaining a clear and intuitive understanding of the application of data-mining technology on clinical big-data in order to promote the production of research results that are beneficial to doctors and patients.

Association of serum lipids and severity of epithelial ovarian cancer：an observational cohort study of 349 Chinese patients

While obesity and fat intake have been associated with the risk and prognosis of epithelial ovarian cancer, the association between the lipid levels and epithelial ovarian cancer phenotype remains controversial. We conducted a retrospective study of 349 epithelial ovarian cancer patients who received treatment at Jiangsu Cancer Hospital, China between 2011 and 2017. We analyzed age at diagnosis, blood pressure, plasma glucose content, body mass index（BMI）, lipid levels and clinical parameters. Severity of epithelial ovarian cancer was classified according to the International Federation of Gynecology and Obstetrics（FIGO） grading system. Univariate analysis of the clinical factors according to the severity of epithelial ovarian cancer was followed by logistic regression analysis to identify clinical factors significantly associated with epithelial ovarian cancer severity. Univariate analysis indicated that age,BMI, triglyceride（TG）, and high density lipoproteins（HDL） differed significantly among different stages of epithelial ovarian cancer（P〈0.05）. In the logistic regression model, elevated TG（OR： 1.883; 95% CI= 1.207-2.937）, and low HDL（OR： 0.497; 95% CI = 0.298-0.829） levels were significantly associated with the high severity epithelial ovarian cancer. Our data indicate that high TG and low HDL levels correlate with a high severity of epithelial ovarian cancer. These data provide important insight into the potential relationship between the lipid pathway and epithelial ovarian cancer phenotype and development.

The Scientific Evidence That Artificial Intelligence (AI) Will Continue to Fail, Until We Deploy ‘Intent’

Electronic machines in the guise of digital computers have transformed our world―social,family,commerce,and politics―although not yet health.Each iteration spawns expectations of yet more astonishing wonders.We wait for the next unbelievable invention to fall into our lap,possibly without limit.How realistic is this?What are the limits,and have we now reached them?A recent survey in The Economist suggests that we have.It describes cycles of misery,where inflated expectations are inevitably followed,a few years later,by disillusion.Yet another Artificial Intelligence(AI)winter is coming―“After years of hype,many people feel AI has failed to deliver”.The current paper not only explains why this was bound to happen,but offers a clear and simple pathway as to how to avoid it happening again.Costly investments in time and effort can only show solid,reliable benefits when full weight is given to the fundamental binary nature of the digital machine,and to the equally unique human faculty of‘intent’.‘Intent’is not easy to define;it suffers acutely from verbal fuzziness―a point made extensively in two earlier papers:“The scientific evidence that‘intent’is vital for healthcare”and“Why Quakerism is more scientific than Einstein”.This paper argues that by putting‘intent’centre stage,first healthcare,and then democracy can be rescued.Suppose every medical consultation were supported by realistic data usage?What if,using only your existing smartphone,your entire medical history were scanned,and instantly compared,within microseconds,with up-to-the-minute information on contraindications and efficacy,from around the globe,for the actual drug you were about to receive,before you actually received it?This is real-time retrieval of clinical data―it increases the security of both doctor and patient,in a way that is otherwise unachievable.My 1980 Ph.D.thesis extolled the merits of digitising the medical record―and,just as digitisation has changed our use of audio and video beyond recognition,so a data-rich medical consultation is unprecedented―prepare to be surprised.This paper has four sections:(1)where binaries help;(2)where binaries ensure extinction;(3)computers in healthcare and civilisation;and(4)data-rich doctoring.Health is vital for economic success,as the current pandemic demonstrates,inescapably.Politics,too,is routinely corrupted―unless we rectify both,failures in AI will be the least of our troubles.

Influence of random uncertainties of anisotropic fibrous model parameters on arterial pressure estimation

This paper deals with a stochastic approach based on the principle of the maximum entropy to investigate the effect of the parameter random uncertainties on the arterial pressure. Motivated by a hyperelastic, anisotropic, and incompressible constitutive law with fiber families, the uncertain parameters describing the mechanical behavior are considered. Based on the available information, the probability density functions are attributed to every random variable to describe the dispersion of the model parameters. Numerous realizations are carried out, and the corresponding arterial pressure results are compared with the human non-invasive clinical data recorded over a mean cardiac cycle. Furthermore, the Monte Carlo simulations are performed, the convergence of the probabilistic model is proven. The different realizations are useful to define a reliable confidence region, in which the probability to have a realization is equM to 95%. It is shown through the obtained results that the error in the estimation of the arterial pressure can reach 35% when the estimation of the model parameters is subjected to an uncertainty ratio of 5%. Finally, a sensitivity analysis is performed to identify the constitutive law relevant parameters for better understanding and characterization of the arterial wall mechanical behaviors.

Sharing Clinical Trial Data： A Proposal from the International Committee of Medical Journal Editors

The International Committee of Medical Journal Editors （ICMJE） believes that there is an ethical obligation to responsibly share data generated by interventional clinical trials because participants have put themselves at risk. In a growing consensus, many funders around the world - foundations, government agencies, and industry - now mandate data sharing. Here, we outline ICMJE＇s proposed requirements to help meet this obligation. We encourage feedback on the proposed requirements. Anyone can provide feedback at www. icmje.org by April 18, 2016.

Expression level of myocardial enzymes in patients with schizophrenia: Predictive value in the occurrence of violence

BACKGROUND Schizophrenic patients are prone to violence,frequent recurrence,and difficult to predict.Emotional and behavioral abnormalities during the onset of the disease,resulting in active myocardial enzyme spectrum.AIM To explored the expression level of myocardial enzymes in patients with schizo-phrenia and its predictive value in the occurrence of violence.METHODS A total of 288 patients with schizophrenia in our hospital from February 2023 to January 2024 were selected as the research object,and 100 healthy people were selected as the control group.Participants’information,clinical data,and labo-ratory examination data were collected.According to Modified Overt Aggression Scale score,patients were further divided into the violent(123 cases)and non-violent group(165 cases).RESULTS The comparative analysis revealed significant differences in serum myocardial enzyme levels between patients with schizophrenia and healthy individuals.In the schizophrenia group,the violent and non-violent groups also exhibited different levels of serum myocardial enzymes.The levels of myocardial enzymes in the non-violent group were lower than those in the violent group,and the patients in the latter also displayed aggressive behavior in the past.CONCLUSION Previous aggressive behavior and the level of myocardial enzymes are of great significance for the diagnosis and prognosis analysis of violent behavior in patients with schizophrenia.By detecting changes in these indicators,we can gain a more comprehensive understanding of a patient’s condition and treatment.

Building on the backbone of CD47-based therapy in cancer:Combination strategies,mechanisms,and future perspectives

Described as a“don't eat me”signal,CD47 becomes a vital immune checkpoint in cancer.Its interaction with signal regulatory protein alpha(SIRPa)prevents macrophage phagocytosis.In recent years,a growing body of evidences have unveiled that CD47-based combination therapy exhibits a superior anti-cancer effect.Latest clinical trials about CD47 have adopted the regimen of collaborating with other therapies or developing CD47-directed bispecific antibodies,indicating the combination strategy as a general trend of the future.In this review,clinical and preclinical cases about the current combination strategies targeting CD47 are collected,their underlying mechanisms of action are discussed,and ideas from future perspectives are shared.

Altered Iron-Mediated Metabolic Homeostasis Governs the Efficacy and Toxicity of Tripterygium Glycosides Tablets Against Rheumatoid Arthritis

Rheumatoid arthritis(RA),a globally increasing autoimmune disorder,is associated with increased disability rates due to the disruption of iron metabolism.Tripterygium glycoside tablets(TGTs),a Tripterygium wilfordii Hook.f.(TwHF)-based therapy,exhibit satisfactory clinical efficacy for RA treatment.However,drug-induced liver injury(DILI)remains a critical issue that hinders the clinical application of TGTs,and the molecular mechanisms underlying the efficacy and toxicity of TGTs in RA have not been fully elucidated.To address this problem,we integrated clinical multi-omics data associated with the anti-RA efficacy and DILI of TGTs with the chemical and target profiling of TGTs to perform a systematic network analysis.Subsequently,we identified effective and toxic targets following experimental validation in a collagen-induced arthritis(CIA)mouse model.Significantly different transcriptome–protein–metabolite profiles distinguishing patients with favorable TGTs responses from those with poor outcomes were identified.Intriguingly,the clinical efficacy and DILI of TGTs against RA were associated with metabolic homeostasis between iron and bone and between iron and lipids,respectively.Particularly,the signal transducer and activator of transcription 3(STAT3)–hepcidin(HAMP)/lipocalin 2(LCN2)–tartrate-resis tant acid phosphatase type 5(ACP5)and STAT3–HAMP–acyl-CoA synthetase long-chain family member 4(ACSL4)–lysophosphatidylcholine acyltransferase 3(LPCAT3)axes were identified as key drivers of the efficacy and toxicity of TGTs.TGTs play dual roles in ameliorating CIA-induced pathology and in inducing hepatic dysfunction,disruption of lipid metabolism,and hepatic lipid peroxidation.Notably,TGTs effectively reversed“iron–bone”disruptions in the inflamed joint tissues of CIA mice by inhibiting the STAT3–HAMP/LCN2–ACP5 axis,subsequently leading to“iron–lipid”disturbances in the liver tissues via modulation of the STAT3–HAMP–ACSL4–LPCAT3 axis.Additional bidirectional validation experiments were conducted using MH7A and AML12 cells to confirm the bidirectional regulatory effects of TGTs on key targets.Collectively,our data highlight the association between iron-mediated metabolic homeostasis and the clinical efficacy and toxicity of TGT in RA therapy,offering guidance for the rational clinical use of TwHF-based therapy with dual therapeutic and toxic potential.

Individual Patient Data Meta-Analysis Is Needed in Chinese Medical Research

Publication biases and collection limitations are the main disadvantages of a traditional meta-analysis based on aggregate patient data（APD）from published articles.Individual patient data（IPD）meta-analysis,as the gold standard of systematic review,is a possible alternative in this context.However,the publications relative to IPD meta-analyses are still rare compared with the traditional ones,especially in the research oriented to Chinese medicine（CM）.In this article,the strengths and detailed functioning of IPD meta-analysis are described.Furthermore,the need for IPD meta-analysis to assess the treatments based on CM was also discussed.Compared with the traditional APD meta-analysis,the IPD meta-analysis might give a more accurate and unbiased assessment and is worth to be recommended to CM researchers.

Future Strategies on Glioma Research:From Big Data to the Clinic

Glioma, as the most common and aggressive malignant central nervous system （CNS） tumor with generally poor prognosis, has been attracting much attention in the last decade [1]. Temozolomide was firstly available in the United States in 1999 as a chemotherapy drug for treating brain cancers and remains as the first-line treatment for glioma. The World Health Organization （WHO） classified glioma into four main grades according to the degree of malignancy in 2007, which were updated in 2016 with the introduction of significant molecular alternations. Also in 2016, the Chinese Glioma Cooperative Group （CGCG） published the first guideline for adult diffuse gliomas [2], representing the only national consensus for the diagnosis and treatment of adult gliomas up till nOW.

Enterprise service bus and standard based hospital integration platform and its application research

For healthcare organizations, there is increasing needs to share data among applications to deliver qualitypatient care. It is a key for successful diagnosis and treatment to view accurate and up-to-date patient data in a single information dashboard in real time. But the fact is that many hospitals and healthcare providers today are struggling with legacy system or internally developed systems that cannot easily scale to support new interfaces; the plethora of inflexible point-to-point interfaces make changing in one system deleteriously impact other systems; some systems could not support information sharing; and the standards followed by different systems are not compatible to each other. This is making it increasingly difficult to meet the rapidly changing and demanding of healthcare service.

This work was supported in part by US Department of Energy Los Alamos National Laboratory contract 47145 and UT-Battelle LLC contract 4000159447 program manager Laura Biven.

There is a growing trend of applying machine learning methods to medical datasets in order to predict patients’future status.Although some of these methods achieve high performance,challenges still exist in comparing and evaluating different models through their interpretable information.Such analytics can help clinicians improve evidence-based medical decision making.In this work,we develop a visual analytics system that compares multiple models’prediction criteria and evaluates their consistency.With our system,users can generate knowledge on different models’inner criteria and how confidently we can rely on each model’s prediction for a certain patient.Through a case study of a publicly available clinical dataset,we demonstrate the effectiveness of our visual analytics system to assist clinicians and researchers in comparing and quantitatively evaluating different machine learning methods.