Correlation of single nucleotide polymorphisms in the AGT gene with susceptibility to systemic lupus erythematosus in Northeast China.

To investigate the correlation between susceptibility to systemic lupus erythematosus (SLE) and single nucleotide polymorphisms (SNPs) rs699, rs4762 and rs1926723 in the AGT gene in the population of Northeast China, while also introducing a new method for early detection of SLE. A total of 856 cases of SLE patients and healthy volunteers who attended the First Affiliated Hospital of Harbin Medical University from January 2020 to December 2022 were recruited. Clinical information and biood samples were collected from particpants in this study. SNaPshot sequencing technology was used to sequence the bases of the rs699, rs4762 and rs1926723 in the AGT gene. The genetic stability of SNPs was analysed by means of Hardy-Weinberg (HWE) genetic equilibrium. The study examined the correlation between genetically stable SNPs and susceptibility to SLE using logistic regression analysis. Rs699 did not adhere to the principles of the HWE genetic equilibrium (p < .01). Conversely, both rs4762 and rs1926723 conformed to the HWE genetic equilibrium (p > .05). However, no significant differences in genotypes and alleles frequencies of the rs4762 were observed between the two groups (p > .05). Furthermore, there was a significant difference in the distribution of AG, GG genotypes frequency and G allele frequency at the rs1926723 between the two groups (p < .001). Individuals with AG and GG genotypes and the G allele had a significantly lower frequency of SLE, indicating a potential genetic protective factor against susceptibility to the SLE. The SNPs rs1926723 may be linked to the susceptibility to SLE, and the AG, GG genotypes and the G allele may be important protective factors for the development of SLE in Northeast China.

Identification and Development of a 4-Gene Ferroptosis Signature Predicting Overall Survival for Diffuse Large B-Cell Lymphoma.

Background: Diffuse large B-cell lymphoma (DLBCL) is a well-differentiated disease, which makes the diagnosis and therapeutic strategy a difficult problem. While ferroptosis, as an iron-dependent form of regulated cell death, it plays an important role in causing several types of cancer. This study is aimed at exploring the prognostic value of ferroptosis-related genes in DLBCL. Methods: In our study, mRNA expression and matching clinical data of DLBCL patients were derived from Gene Expression Omnibus (GEO) database. First, multivariate cox regression model and nomogram which can predict the DLBCL patients' prognosis were built and validated. The multigene signature was constructed and optimized by the least absolute shrinkage and selection operator (LASSO) cox regression model. Also, ferroptosis-related subtypes were developed by consistent cluster. Last but not least, we explored the association between categories of infiltrating immune cells and model genes' expression. Results: Our results showed that 27 gene expressions were correlated with overall survival (OS) in the univariate cox regression analysis. A 4-gene signature was constructed through these genes to stratify patients into high-low risk groups using risk score derived from model (model 1:gene expression model). The OS of patients in the high-risk group was shorter than that of patients in the low-risk group in the TNM stage and clinically distinct subtypes (activated B cell [ABC], germinal center B cell [GCB]) (P < .001). Furthermore, it was shown that the risk score was an independent factor in clinical cox regression model for OS (model 2:clinical model) (HR>1, P < .010). Besides, in consistent cluster analysis, ferroptosis prognosis status was different among 3 subtypes. Moreover, the correlation analysis between 4-gene with immune cells showed dendritic cells may be significantly associated with DLBCL. Conclusion: This research constructed an innovative ferroptosis-related gene signature for prognostic estimation of DLBCL patients. Solutions targeting ferroptosis could be an important therapeutic intervention for DLBCL.

A tool for predicting overall survival in patients with Ewing sarcoma: a multicenter retrospective study.

OBJECTIVE: The aim of this study was to establish and validate a clinical prediction model for assessing the risk of metastasis and patient survival in Ewing's sarcoma (ES). METHODS: Patients diagnosed with ES from the Surveillance, Epidemiology and End Results (SEER) database for the period 2010-2016 were extracted, and the data after exclusion of vacant terms was used as the training set (n=767). Prediction models predicting patients' overall survival (OS) at 1 and 3 years were created by cox regression analysis and visualized using Nomogram and web calculator. Multicenter data from four medical institutions were used as the validation set (n=51), and the model consistency was verified using calibration plots, and receiver operating characteristic (ROC) verified the predictive ability of the model. Finally, a clinical decision curve was used to demonstrate the clinical utility of the model. RESULTS: The results of multivariate cox regression showed that age, , bone metastasis, tumor size, and chemotherapy were independent prognostic factors of ES patients. Internal and external validation results: calibration plots showed that the model had a good agreement for patient survival at 1 and 3 years; ROC showed that it possessed a good predictive ability and clinical decision curve proved that it possessed good clinical utility. CONCLUSIONS: The tool built in this paper to predict 1- and 3-year survival in ES patients ( https://drwenleli0910.shinyapps.io/EwingApp/ ) has a good identification and predictive power.

Interpretable clinical visualization model for prediction of prognosis in osteosarcoma: a large cohort data study.

Background: Currently, the clinical prediction model for patients with osteosarcoma was almost developed from single-center data, lacking external validation. Due to their low reliability and low predictive power, there were few clinical applications. Our study aimed to set up a clinical prediction model with stronger predictive ability, credibility, and clinical application value for osteosarcoma. Methods: Clinical information related to osteosarcoma patients from 2010 to 2016 was collected in the SEER database and four different Chinese medical centers. Factors were screened using three models (full subset regression, univariate Cox, and LASSO) via minimum AIC and maximum AUC values in the SEER database. The model was selected by the strongest predictive power and visualized by three statistical methods: nomogram, web calculator, and decision tree. The model was further externally validated and evaluated for its clinical utility in data from four medical centers. Results: Eight predicting factors, namely, age, grade, laterality, stage M, surgery, bone metastases, lung metastases, and tumor size, were selected from the model based on the minimum AIC and maximum AUC value. The internal and external validation results showed that the model possessed good consistency. ROC curves revealed good predictive ability (AUC > 0.8 in both internal and external validation). The DCA results demonstrated that the model had an excellent clinical predicted utility in 3 years and 5 years for North American and Chinese patients. Conclusions: The clinical prediction model was built and visualized in this study, including a nomogram and a web calculator (https://dr-lee.shinyapps.io/osteosarcoma/), which indicated very good consistency, predictive power, and clinical application value.

Risk analysis of pulmonary metastasis of chondrosarcoma by establishing and validating a new clinical prediction model: a clinical study based on SEER database.

BACKGROUND: The prognosis of lung metastasis (LM) in patients with chondrosarcoma was poor. The aim of this study was to construct a prognostic nomogram to predict the risk of LM, which was imperative and helpful for clinical diagnosis and treatment. METHODS: Data of all chondrosarcoma patients diagnosed between 2010 and 2016 was queried from the Surveillance, Epidemiology, and End Results (SEER) database. In this retrospective study, a total of 944 patients were enrolled and randomly splitting into training sets (n = 644) and validation cohorts(n = 280) at a ratio of 7:3. Univariate and multivariable logistic regression analyses were performed to identify the prognostic nomogram. The predictive ability of the nomogram model was assessed by calibration plots and receiver operating characteristics (ROCs) curve, while decision curve analysis (DCA) and clinical impact curve (CIC) were applied to measure predictive accuracy and clinical practice. Moreover, the nomogram was validated by the internal cohort. RESULTS: Five independent risk factors including age, sex, marital, tumor size, and lymph node involvement were identified by univariate and multivariable logistic regression. Calibration plots indicated great discrimination power of nomogram, while DCA and CIC presented that the nomogram had great clinical utility. In addition, receiver operating characteristics (ROCs) curve provided a predictive ability in the training sets (AUC = 0.789, 95% confidence interval [CI] 0.789-0.808) and the validation cohorts (AUC = 0.796, 95% confidence interval [CI] 0.744-0.841). CONCLUSION: In our study, the nomogram accurately predicted risk factors of LM in patients with chondrosarcoma, which may guide surgeons and oncologists to optimize individual treatment and make a better clinical decisions. TRIAL REGISTRATION: JOSR-D-20-02045, 29 Dec 2020.

Attention-RefNet: Interactive Attention Refinement Network for Infected Area Segmentation of COVID-19.

COVID-19 pneumonia is a disease that causes an existential health crisis in many people by directly affecting and damaging lung cells. The segmentation of infected areas from computed tomography (CT) images can be used to assist and provide useful information for COVID-19 diagnosis. Although several deep learning-based segmentation methods have been proposed for COVID-19 segmentation and have achieved state-of-the-art results, the segmentation accuracy is still not high enough (approximately 85%) due to the variations of COVID-19 infected areas (such as shape and size variations) and the similarities between COVID-19 and non-COVID-infected areas. To improve the segmentation accuracy of COVID-19 infected areas, we propose an interactive attention refinement network (Attention RefNet). The interactive attention refinement network can be connected with any segmentation network and trained with the segmentation network in an end-to-end fashion. We propose a skip connection attention module to improve the important features in both segmentation and refinement networks and a seed point module to enhance the important seeds (positions) for interactive refinement. The effectiveness of the proposed method was demonstrated on public datasets (COVID-19CTSeg and MICCAI) and our private multicenter dataset. The segmentation accuracy was improved to more than 90%. We also confirmed the generalizability of the proposed network on our multicenter dataset. The proposed method can still achieve high segmentation accuracy.

Optimized precooling combined with SO2-released paper treatment improves the storability of longan (Dimocarpus longan Lour.) fruits stored at room temperature.

Precooling and sulfur dioxide fumigation were proved as effective methods for the preservation of longan (Dimocarpus longan Lour.) fruits. However, inadequate precooling and sulfur dioxide fumigation resulted in unexpected losses and short shelf life. A L9(34) orthogonal test was conducted to screen out ideal dosage of sodium metabisulfite (factor A), precooling method (factor B), and precooling duration (factor C) to improve the storability of longan fruit stored for 48 hr at room temperature (RT) (25℃). The overall qualities of all of the treated longan fruits after a 48-hr storage (OQST) and during the 5-day shelf at 25℃ (OQSF) were better than those of the control fruits. The treated fruits showed brighter fresh color (higher L*, b*, C*, and h° values but lower a* value), higher flavonoid, and chlorophyll contents. Moreover, the SO2 residue was concentrated in pericarp but little in aril for any of the 12 treatments. The multivariate variance analysis showed that factor A was dominant to determine both of the OQST and OQSF, while factor B affected the OQST, and factor C affected the OQSF. In total, "0.22% sodium metabisulfite + 4 hr precooling + uncovered precooling" was considered to be an ideal treatment. These results would contribute to improving longan postharvest preservation technology.

Camrelizumab Plus Gemcitabine, Vinorelbine, and Pegylated Liposomal Doxorubicin in Relapsed/Refractory Primary Mediastinal B-Cell Lymphoma: A Single-Arm, Open-Label, Phase II Trial.

PURPOSE: Patients with relapsed/refractory primary mediastinal B-cell lymphoma (rrPMBCL) represent a particularly challenging population to treat, with few life-saving treatment options in the context of a dismal prognosis. PATIENTS AND METHODS: In this open-label, single-arm, phase II study, the safety and efficacy of combined regimen of chemotherapy consisting of gemcitabine, vinorelbine, and pegylated liposomal doxorubicin (GVD) plus anti-PD-1 antibody camrelizumab was assessed in rrPMBCL. Patients received chemo-immunotherapy every 3 weeks until the second confirmed complete response (CR) or up to 12 cycles, followed by camrelizumab monotherapy for up to 1 year. The primary endpoints were objective response rate (ORR) and safety. RESULTS: Twenty-seven response evaluable patients were enrolled, who received a median of three first-line therapies, 59% with bulky disease. The ORR was 74%, including 56% with a CR. A median time of 1.7 months to response was observed, with 78% exhibiting tumor shrinkage at the first evaluation. After 24.8 months median follow-up, the median duration of response was not reached, with a 65% 2-year estimated response rate. Thirteen responders remained in sustained complete remission. Estimated 24-month progression-free survival and overall survival rates were 48.2% and 81.5%, respectively. Any grade and grade 3 treatment-related adverse events (AE) occurred in 93% and 33% of patients, respectively; with no grade 4 or 5 AEs. Baseline levels of IL10, IFNγ, and soluble Fas were associated with objective response. CONCLUSIONS: Camrelizumab plus GVD chemotherapy offers a potent option as life-saving chemo-immunotherapy with promising efficacy and a manageable safety profile for patients with rrPMBCL, especially with bulky aggressive disease.

Prediction of fatal adverse prognosis in patients with fever-related diseases based on machine learning: A retrospective study.

BACKGROUND: Fever is the most common chief complaint of emergency patients. Early identification of patients at an increasing risk of death may avert adverse outcomes. The aim of this study was to establish an early prediction model of fatal adverse prognosis of fever patients by extracting key indicators using big data technology. METHODS: A retrospective study of patients' data was conducted using the Emergency Rescue Database of Chinese People's Liberation Army General Hospital. Patients were divided into the fatal adverse prognosis group and the good prognosis group. The commonly used clinical indicators were compared. Recursive feature elimination (RFE) method was used to determine the optimal number of the included variables. In the training model, logistic regression, random forest, adaboost and bagging were selected. We also collected the emergency room data from December 2018 to December 2019 with the same inclusion and exclusion criterion. The performance of the model was evaluated by accuracy, F1-score, precision, sensitivity and the areas under receiver operator characteristic curves (ROC-AUC). RESULTS: The accuracy of logistic regression, decision tree, adaboost and bagging was 0.951, 0.928, 0.924, and 0.924, F1-scores were 0.938, 0.933, 0.930, and 0.930, the precision was 0.943, 0.938, 0.937, and 0.937, ROC-AUC were 0.808, 0.738, 0.736, and 0.885, respectively. ROC-AUC of ten-fold cross-validation in logistic and bagging models were 0.80 and 0.87, respectively. The top six coefficients and odds ratio (OR) values of the variables in the Logistic regression were cardiac troponin T (CTnT) (coefficient=0.346, OR = 1.413), temperature (T) (coefficient=0.235, OR = 1.265), respiratory rate (RR) (coefficient= -0.206,OR = 0.814), serum kalium (K) (coefficient=0.137, OR = 1.146), pulse oxygen saturation (SPO2) (coefficient= -0.101, OR = 0.904), and albumin (ALB) (coefficient= -0.043, OR = 0.958). The weights of the top six variables in the bagging model were: CTnT, RR, lactate dehydrogenase, serum amylase, heartrate, and systolic blood pressure. CONCLUSIONS: The main clinical indicators of concern included CTnT, RR, SPO2, T, ALB and K. The bagging model and logistic regression model had better diagnostic performance comprehesively. Those may be conducive to the early identification of critical patients with fever by physicians.

A Machine Learning-Based Model to Predict Acute Traumatic Coagulopathy in Trauma Patients Upon Emergency Hospitalization.

Acute traumatic coagulopathy (ATC) is an extremely common but silent murderer; this condition presents early after trauma and impacts approximately 30% of severely injured patients who are admitted to emergency departments (EDs). Given that conventional coagulation indicators usually require more than 1 hour after admission to yield results-a limitation that frequently prevents the ability for clinicians to make appropriate interventions during the optimal therapeutic window-it is clearly of vital importance to develop prediction models that can rapidly identify ATC; such models would also facilitate ancillary resource management and clinical decision support. Using the critical care Emergency Rescue Database and further collected data in ED, a total of 1385 patients were analyzed and cases with initial international normalized ratio (INR) values >1.5 upon admission to the ED met the defined diagnostic criteria for ATC; nontraumatic conditions with potentially disordered coagulation systems were excluded. A total of 818 individuals were collected from Emergency Rescue Database as derivation cohorts, then were split 7:3 into training and test data sets. A Pearson correlation matrix was used to initially identify likely key clinical features associated with ATC, and analysis of data distributions was undertaken prior to the selection of suitable modeling tools. Both machine learning (random forest) and traditional logistic regression were deployed for prediction modeling of ATC. After the model was built, another 587 patients were further collected in ED as validation cohorts. The ATC prediction models incorporated red blood cell count, Shock Index, base excess, lactate, diastolic blood pressure, and potential of hydrogen. Of 818 trauma patients filtered from the database, 747 (91.3%) patients did not present ATC (INR ≤ 1.5) and 71 (8.7%) patients had ATC (INR > 1.5) upon admission to the ED. Compared to the logistic regression model, the model based on the random forest algorithm showed better accuracy (94.0%, 95% confidence interval [CI]: 0.922-0.954 to 93.5%, 95% CI: 0.916-0.95), precision (93.3%, 95% CI: 0.914-0.948 to 93.1%, 95% CI: 0.912-0.946), F1 score (93.4%, 95% CI: 0.915-0.949 to 92%, 95% CI: 0.9-0.937), and recall score (94.0%, 95% CI: 0.922-0.954 to 93.5%, 95% CI: 0.916-0.95) but yielded lower area under the receiver operating characteristic curve (AU-ROC) (0.810, 95% CI: 0.673-0.918 to 0.849, 95% CI: 0.732-0.944) for predicting ATC in the trauma patients. The result is similar in the validation cohort. The values for classification accuracy, precision, F1 score, and recall score of random forest model were 0.916, 0.907, 0.901, and 0.917, while the AU-ROC was 0.830. The values for classification accuracy, precision, F1 score, and recall score of logistic regression model were 0.905, 0.887, 0.883, and 0.905, while the AU-ROC was 0.858. We developed and validated a prediction model based on objective and rapidly accessible clinical data that very confidently identify trauma patients at risk for ATC upon their arrival to the ED. Beyond highlighting the value of ED initial laboratory tests and vital signs when used in combination with data analysis and modeling, our study illustrates a practical method that should greatly facilitates both warning and guided target intervention for ATC.

Detection of Influenza and Other Respiratory Viruses in Air Sampled From a University Campus: A Longitudinal Study.

BACKGROUND: Respiratory virus-laden particles are commonly detected in the exhaled breath of symptomatic patients or in air sampled from healthcare settings. However, the temporal relationship of detecting virus-laden particles at nonhealthcare locations vs surveillance data obtained by conventional means has not been fully assessed. METHODS: From October 2016 to June 2018, air was sampled weekly from a university campus in Hong Kong. Viral genomes were detected and quantified by real-time reverse-transcription polymerase chain reaction. Logistic regression models were fitted to examine the adjusted odds ratios (aORs) of ecological and environmental factors associated with the detection of virus-laden airborne particles. RESULTS: Influenza A (16.9% [117/694]) and influenza B (4.5% [31/694]) viruses were detected at higher frequencies in air than rhinovirus (2.2% [6/270]), respiratory syncytial virus (0.4% [1/270]), or human coronaviruses (0% [0/270]). Multivariate analyses showed that increased crowdedness (aOR, 2.3 [95% confidence interval {CI}, 1.5-3.8]; P < .001) and higher indoor temperature (aOR, 1.2 [95% CI, 1.1-1.3]; P < .001) were associated with detection of influenza airborne particles, but absolute humidity was not (aOR, 0.9 [95% CI, .7-1.1]; P = .213). Higher copies of influenza viral genome were detected from airborne particles >4 µm in spring and <1 µm in autumn. Influenza A(H3N2) and influenza B viruses that caused epidemics during the study period were detected in air prior to observing increased influenza activities in the community. CONCLUSIONS: Air sampling as a surveillance tool for monitoring influenza activity at public locations may provide early detection signals on influenza viruses that circulate in the community.

Role of physical performance measures for identifying functional disability among Chinese older adults: Data from the China Health and Retirement Longitudinal Study.

BACKGROUND: Functional disability is a common health burden in older adults and follows a hierarchical pattern. Physical performance measures are useful for the objective estimation of functional disability. This study primarily aimed to compare the validity of handgrip strength and gait speed, alone and in combination, for recognizing the functional disability among Chinese older adults. This study also aimed to stratify the functional disability according to the criterion-referenced values of handgrip strength and gait speed. METHODS: We selected 6127 respondents from the 2011 wave of the China Health and Retirement Longitudinal Study. Here, we defined functional disability as needing any help in any items of activities of daily living (ADL) and instrumental activities of daily living (IADL). To assess the validity of physical performance measures alone and in combination for the recognition of functional disability, we conducted the receiver operating characteristic analysis. RESULTS: Compared with handgrip strength, the gait speed could better discriminate ADL disability and showed a satisfactory discriminant validity (area under the curve ≥ 0.7) in men. However, this finding was not found in the recognition of IADL disability. When combining these two measures, the parallel test showed a high sensitivity with a poor specificity, whereas the serial test showed a perfect specificity with a poor sensitivity. CONCLUSION: We developed the hierarchical cut-off values of handgrip strength and gait speed for identifying and stratifying the functional disability among Chinese adults over 60 years old. The speed test was superior to handgrip strength in identifying ADL disability. The parallel tests of those with high sensitivity perhaps could help identify the functional disability. Further work on cost-utility analysis is warranted.

Preharvest zinc sulfate spray improves the storability of longan (Dimocarpus longan Lour.) fruits by protecting the cell wall components and antioxidants of pericarp.

BACKGROUND: Zinc (Zn) fertilization has been reported to improve the quality and storability of many fruits, but the mechanism had not been systematically studied. In this study, the effect of preharvest 0.2% zinc sulfate (ZnSO4 ) spray on the storability of longan fruits was investigated. RESULTS: The preharvest ZnSO4 spray did not significantly influence the quality but increased the Zn content in longan pericarp by 12.5-fold. More importantly, the treatment significantly reduced the rotting rate, pericarp browning, and aril breakdown of longan fruits stored at room temperature and low temperature. Physiological and biochemical results indicated that the treatment resulted in higher water retention capacity and inhibited the degradation of cellulose, pectin, flavonoid, and phenolics of longan pericarp at the late stages of storage. Consistent with these results, the activity of cellulase, polygalacturonase, polyphenol oxidase, and lipoxygenase was significantly inhibited in the ZnSO4 -treated longan pericarp at the late stages of storage. CONCLUSION: Higher Zn content in the ZnSO4 -treated longan pericarp might help to enhance the resistance against microbial infection, inhibit the hydrolysis of cell wall components, and thus effectively protected the cell wall components, maintained the cellular compartmentation, and prevented the phenolics and flavonoid from degradation by browning-related enzymes. © 2018 Society of Chemical Industry.