Impact of cladribine, cytarabine, and G-CSF (CLAG) as a bridging therapy prior to allogeneic hematopoietic stem cell transplantation in relapsed or refractory acute myeloid leukemia.

The combination of cladribine, cytarabine, and G-CSF (CLAG) has exhibited robust synergistic anti-leukemia activity as an induction therapy (IT) in acute myeloid leukemia (AML). However, the impact of CLAG as a bridging therapy (BT) administered between IT and allogeneic hematopoietic stem cell transplantation (allo-HSCT) for patients with relapsed or refractory (R/R) AML remains uncertain. In this retrospective study, we examined the efficacy of CLAG as a transitional strategy prior to allo-HSCT in R/R AML. We included 234 patients with R/R AML who received the modified busulfan plus cyclophosphamide conditioning regimen for allo-HSCT in our center during the past 6 years, performed a propensity-score matching analysis, partitioned them into four distinct cohorts, and further integrated them into the CLAG group and non-CLAG group based on response to IT and utilization of CLAG. Our cohorts encompassed 12 patients in Cohort A (modified composite complete remission (mCRc) after IT, CLAG), 31 in Cohort B (mCRc after IT, non-CLAG), 35 in Cohort C (non-complete remission (non-CR) after IT, CLAG), and 80 in Cohort D (non-CR after IT, non-CLAG). Intriguingly, among patients with non-CR status, the administration of CLAG correlated with a notably statistically diminished risk of relapse and improved survival at 2-year follow-up (Cohort C vs. Cohort D). Employing CLAG as a BT prior to allo-HSCT demonstrates substantial effectiveness, a relative degree of safety, and manageable toxicity in selected R/R AML cases.

A novel non-negative Bayesian stacking modeling method for Cancer survival prediction using high-dimensional omics data.

BACKGROUND: Survival prediction using high-dimensional molecular data is a hot topic in the field of genomics and precision medicine, especially for cancer studies. Considering that carcinogenesis has a pathway-based pathogenesis, developing models using such group structures is a closer mimic of disease progression and prognosis. Many approaches can be used to integrate group information; however, most of them are single-model methods, which may account for unstable prediction. METHODS: We introduced a novel survival stacking method that modeled using group structure information to improve the robustness of cancer survival prediction in the context of high-dimensional omics data. With a super learner, survival stacking combines the prediction from multiple sub-models that are independently trained using the features in pre-grouped biological pathways. In addition to a non-negative linear combination of sub-models, we extended the super learner to non-negative Bayesian hierarchical generalized linear model and artificial neural network. We compared the proposed modeling strategy with the widely used survival penalized method Lasso Cox and several group penalized methods, e.g., group Lasso Cox, via simulation study and real-world data application. RESULTS: The proposed survival stacking method showed superior and robust performance in terms of discrimination compared with single-model methods in case of high-noise simulated data and real-world data. The non-negative Bayesian stacking method can identify important biological signal pathways and genes that are associated with the prognosis of cancer. CONCLUSIONS: This study proposed a novel survival stacking strategy incorporating biological group information into the cancer prognosis models. Additionally, this study extended the super learner to non-negative Bayesian model and ANN, enriching the combination of sub-models. The proposed Bayesian stacking strategy exhibited favorable properties in the prediction and interpretation of complex survival data, which may aid in discovering cancer targets.

Association of altitude and frailty in Chinese older adults: using a cumulative frailty index model.

Objective: The population is aging exponentially and the resulting frailty is becoming increasingly evident. We aimed to explore the association between altitude and frailty, and to identify associated factors for frailty. Methods: This is a community-based cross-sectional survey. 1,298 participants aged ≥60 years from three different altitudes were included in the study. To quantify frailty, we constructed a frailty index (FI) and a frailty score (FS). The FI was divided into non-frailty, prefrailty, and frailty. The Odds Ratios and confidence intervals (ORs, 95%CIs) were used to evaluate the association between altitude and FI and FS in multivariate ordinal logistic regression and linear regression. Results: There were 560 (53.1%) participants in the prefrailty and 488 (37.6%) in the frailty group. The FS increased with higher altitude (P for trend <0.001). Multivariate ordinal logistic regression analysis revealed an association between altitude and frailty, OR = 1.91 (95% CI: 1.38-2.64) in mid-high altitude and 2.49 (95% CI:1.40-4.45) in high altitude. The same trend of association was found in the univariate analysis. The FS increased by 1.69 (95% CI: 0.78-2.60) at mid-high altitude and 3.24 (95%CI:1.66-4.81) at high altitude compared to medium altitude. Conclusion: The study indicates that high altitude exposure is an associated factor for frailty in older adults. This association become stronger with higher altitudes. As a result, it is essential to conduct early frailty screening for residents living at high altitudes.

A non-negative spike-and-slab lasso generalized linear stacking prediction modeling method for high-dimensional omics data.

BACKGROUND: High-dimensional omics data are increasingly utilized in clinical and public health research for disease risk prediction. Many previous sparse methods have been proposed that using prior knowledge, e.g., biological group structure information, to guide the model-building process. However, these methods are still based on a single model, offen leading to overconfident inferences and inferior generalization. RESULTS: We proposed a novel stacking strategy based on a non-negative spike-and-slab Lasso (nsslasso) generalized linear model (GLM) for disease risk prediction in the context of high-dimensional omics data. Briefly, we used prior biological knowledge to segment omics data into a set of sub-data. Each sub-model was trained separately using the features from the group via a proper base learner. Then, the predictions of sub-models were ensembled by a super learner using nsslasso GLM. The proposed method was compared to several competitors, such as the Lasso, grlasso, and gsslasso, using simulated data and two open-access breast cancer data. As a result, the proposed method showed robustly superior prediction performance to the optimal single-model method in high-noise simulated data and real-world data. Furthermore, compared to the traditional stacking method, the proposed nsslasso stacking method can efficiently handle redundant sub-models and identify important sub-models. CONCLUSIONS: The proposed nsslasso method demonstrated favorable predictive accuracy, stability, and biological interpretability. Additionally, the proposed method can also be used to detect new biomarkers and key group structures.

Loneliness, Living Alone, and Risk of Cardiovascular Disease Among Older Adults in China.

BACKGROUND: Older adults are prone to live alone and feel lonely. The main objective of this study was to assess the associations of loneliness and living alone with cardiovascular disease (CVD) among community-dwelling older individuals in China. METHODS: We conducted a longitudinal analysis on 3 661 participants aged older than 65 years from the latest 2014 and 2018 waves of the Chinese Longitudinal Healthy Longevity Survey. Cox proportional hazards models were used to assess the associations of loneliness and living alone with CVD risk, with adjustment for confounding factors. RESULTS: A total of 616 incident CVD cases were identified during follow-up. Participants who reported feeling lonely experienced a 28% increased risk of developing CVD after adjustment for sociodemographic characteristics, lifestyle factors, and baseline health status (adjusted hazard ratio [HR]: 1.28, 95% confidence interval [CI]: 1.01-1.62; ptrend = .046). In contrast, no significant association was observed between living alone and CVD risk. Subgroup analyses showed that among those individuals who lived alone, often feeling lonely doubled the risk of CVD compared to never being lonely (HR: 2.17, 95% CI: 1.20-3.93; ptrend = .007). CONCLUSIONS: Loneliness was an independent risk factor for CVD among Chinese older adults. Our findings underscore the importance of addressing loneliness in the prevention of CVD among older individuals, especially those who live alone.

Development and validation of a cuproptosis-related prognostic model for acute myeloid leukemia patients using machine learning with stacking.

Our objective is to develop a prognostic model focused on cuproptosis, aimed at predicting overall survival (OS) outcomes among Acute myeloid leukemia (AML) patients. The model utilized machine learning algorithms incorporating stacking. The GSE37642 dataset was used as the training data, and the GSE12417 and TCGA-LAML cohorts were used as the validation data. Stacking was used to merge the three prediction models, subsequently using a random survival forests algorithm to refit the final model using the stacking linear predictor and clinical factors. The prediction model, featuring stacking linear predictor and clinical factors, achieved AUC values of 0.840, 0.876 and 0.892 at 1, 2 and 3 years within the GSE37642 dataset. In external validation dataset, the corresponding AUCs were 0.741, 0.754 and 0.783. The predictive performance of the model in the external dataset surpasses that of the model simply incorporates all predictors. Additionally, the final model exhibited good calibration accuracy. In conclusion, our findings indicate that the novel prediction model refines the prognostic prediction for AML patients, while the stacking strategy displays potential for model integration.

A clinical predictive model for pre-transplantation Klebsiella pneumoniae colonization and relevance for clinical outcomes in patients receiving allogeneic hematopoietic stem cell transplantation.

The purpose of this study is to establish a clinical prediction model to discriminate patients at high risk of Klebsiella pneumoniae (KP) colonization before allogeneic hematopoietic stem cell transplantation (allo-HSCT) and evaluate the impact of KP colonization on clinical outcomes after allo-HSCT. We retrospectively collected data from 2,157 consecutive patients receiving allo-HSCT between January 2018 and March 2022. KP colonization was defined as a positive test for KP from a pharyngeal or anal swab before allo-HSCT. Logistic regression was used to build a clinical prediction model. Cox regression analyses were performed to explore the effect of KP colonization on clinical outcomes. Among all the inpatients, 166 patients had KP colonization and 581 with no positive pathogenic finding before transplantation. Seven candidate predictors were entered into the final prediction model. The prediction model had an area under the curve of 0.775 (95% CI 0.723-0.828) in the derivation cohort and 0.846 (95% CI: 0.790-0.902) in the validation cohort. Statistically significantly different incidence rates were observed among patient groups with clinically predicted low, medium, and high risk for KP infection (P < 0.001). The presence of KP colonization delayed platelet engraftment (P < 0.001) and patients with KP colonization were more likely to develop KP bloodstream infections within 100 days after allo-HSCT (P < 0.0001). Patients with KP colonization had higher non-relapse mortality (P = 0.032), worse progression-free survival (P = 0.0027), and worse overall survival within 100 days after allo-HSCT (P = 0.013). Our findings suggest that increased awareness of risks associated with pre-transplantation bacterial colonization is warranted.IMPORTANCESeveral studies have identified that Klebsiella pneumoniae (KP) is among the most common and deadly pathogens for patients in hospital intensive care units and those receiving transplantation. However, there are currently no studies that evaluate the impact of KP colonization to patients undergoing allogeneic hematopoietic stem cell transplantation. Our results confirm that pre-existing KP colonization is relatively common in a hematology transplant ward setting and negatively affects post-transplantation prognosis. Our clinical prediction model for KP colonization can support early intervention in patients at high risk to avoid subsequent bloodstream infections and improve survival outcomes. Altogether, our data suggest that increased awareness of risks associated with pre-transplantation bacterial colonization is warranted. Future studies are needed to confirm these findings and to test early intervention strategies for patients at risk of complications from KP infection.

Adverse event profile of CGRP monoclonal antibodies: findings from the FDA adverse event reporting database.

BACKGROUND: Four CGRP Monoclonal Antibodies (mAbs) have been approved for migraine prophylaxis by the Food and Drug Administration (FDA) since 2018. However, there are concerns about the safety of these four drugs for real-world use. OBJECTIVE: To compare the adverse event profiles of four CGRP-mAbs with FAERS data. METHODS: The study was based on records from the FAERS database. Only reports containing one of the active ingredients with CGRP-mAbs were included in this study. Disproportionality analyses including but not limited to reporting odds ratio (ROR) and information components (IC) were conducted to identify drug-AE associations. RESULTS: In total, 58110 reports were identified for CGRP-mAbs. 80 overlapping signals were disproportionately reported. They affected a range of organs and systems, including the gastrointestinal and cardiovascular systems, skin, and hair. Additionally, the rare cardiovascular adverse events were significantly different among the four CGRP-mAbs. CONCLUSION: We identified numerous shared underlying signals (overlapping signals) for CGRP-mAbs as suspected drugs in multiple systems and organs. The unlabeled common signals may indicate potential safety issues. In addition, the underlying safety signals varied among the four CGRP-mAbs, particularly in the cardiovascular system, and further studies are needed to confirm these associations and the potential clinical implications.

Association of spontaneous abortion with bipolar disorder and major depression based on inverse probability treatment weighting of multigroup propensity scores: Evidence from the UK Biobank.

BACKGROUND: Few studies have explored the association between the number of SAs and bipolar disorder and major depression (BDMD). This study aims to investigate the association between SA and BDMD, and the possible dose-response relationship between them. METHODS: We conducted a cross-sectional study of 13,200 female UK Biobank participants. Participants were classified into BDMD and no-BDMD groups based on their BDMD status. The number of SAs was grouped into non-SA, occasional SA (OSA), and recurrent SA (RSA). Baseline characteristics of the three groups were balanced using inverse probability treatment weighting (IPTW) based on propensity scores. The three-knots restricted cubic spline regression model was utilized to assess the dose-response relationship between the number of SAs and BDMD. RESULTS: The IPTW-adjusted multivariate logistic regression revealed that SA was an independent risk factor for BDMD, with adjusted OR of 1.12 (95 % CI: 1.07-1.19) and 1.32 (95 % CI: 1.25-1.40) in the OSA and RSA groups, respectively. The strength of this association amplified as the number of SAs (P for trend <0.001). There was a nonlinear relationship between the number of SAs and the risk of BDMD, with an approximately inverted L-shaped curve. LIMITATIONS: The information of the SA and BDMD status relied on self-reported by volunteers, and the study sample was mostly of European descent. CONCLUSIONS: Women who reported experiencing multiple SAs are more likely to have BDMD. Therefore, it is imperative to provide psychological care and interventions for women in the postpartum period.

Sex-specific association between carotid atherosclerosis and fundus arteriosclerosis in a Chinese population: a retrospective cross-sectional study.

OBJECTIVES: Vascular stiffening is highly predictive of major adverse cardiovascular events. It is not clear whether microangiopathy, such as fundus arteriosclerosis, is related to carotid atherosclerosis. Hence, this study was designed to investigate the relationship between carotid atherosclerosis and fundus arteriosclerosis among individuals of different sexes in the Chinese health-examination population. METHODS: This retrospective cross-sectional study involved 20,836 participants, including 13050 males and 7786 females. All participants underwent a detailed health examination, including medical history assessment, physical examination, assessment of lifestyle factors, fundus photography, Doppler ultrasound examination of the neck, and laboratory examinations. Two trained ophthalmologists analysed fundus arteriosclerosis based on fundus photographs, while carotid atherosclerosis was diagnosed using colour Doppler sonography of the neck. Binary logistic regression was used to analyse the relationship between carotid atherosclerosis and fundus arteriosclerosis. RESULTS: In participants with fundus arteriosclerosis, the incidence of carotid atherosclerosis was higher than that of participants without fundus arteriosclerosis (52.94% vs. 47.06%). After adjustments for potential confounding factors, fundus arteriosclerosis was significantly associated with the risk of carotid atherosclerosis. The OR with 95% CI for fundus arteriosclerosis was 1.17 (1.02, 1.34) with p = 0.0262, and individuals who did not have fundus arteriosclerosis were used as a reference in the total population. Fundus arteriosclerosis was associated with the incidence of carotid atherosclerosis in males (p = 0.0005) but not in females (p = 0.0746). CONCLUSIONS: Fundus arteriosclerosis was closely associated with carotid atherosclerosis in the Chinese population. This association was found in males but not in females.

Risk Factors for Carbapenem-Resistant Enterobacteriaceae Colonization and the Effect on Clinical Outcomes and Prognosis in Allogeneic Hematopoietic Stem Cell Transplanted Patients.

Purpose: The current study assesses which are the main risk factors, clinical outcome and prognosis following the colonization of CRE in patients that underwent allo-HSCT. Patients and Methods: A total of 343 patients subjected to allo-HSCT in the period comprised between June 2021 and June 2022 were enrolled in this retrospective study. The CRE colonization was diagnosed by clinical history and routine microbial culture of perirectal swab. In this regard, a clinical prediction model was designed based on independent risk factors underlying the pre-transplantation CRE colonization using a backward stepwise logistic regression, followed by the evaluation of its discrimination and calibration efficacies, along with clinical usefulness. Furthermore, univariate and multivariate Cox regression analyses were then conducted to assess the risk factors for post-transplantation clinical outcomes. Results: Out of 343 patients enrolled in this study, 135 (39.3%) reported CRE colonization. The independent risk factor variables for CRE colonization were incorporated into the nomogram to build a prediction model, which showed an area under the curve of 0.767 (95% CI: 0.716-0.818), and well-fitted calibration curves (χ2 = 1.737, P = 0.9788). The patients with CRE colonization reported a significantly lower platelet engraftment rate with a higher risk of post-transplantation BSI when compared with the non-CRE colonization group (P = 0.02 and P < 0.001; respectively). The non-relapse mortality (NRM) value was higher in the CRE patients (P < 0.05), consistently with a survival probability that was thus significantly lower for the same timeframe (P < 0.05). Conclusion: A reliable clinical prediction model for pre-transplantation CRE colonization was developed that demonstrated that the CRE colonization negatively affects platelet engraftment and survival outcomes following allo-HSCT.

Association between total bilirubin and gender-specific incidence of fundus arteriosclerosis in a Chinese population: a retrospective cohort study.

To investigate the gender-specific relationship between total bilirubin (TBIL) and fundus arteriosclerosis in the general population, and to explore whether there is a dose-response relationship between them. In a retrospective cohort study, 27,477 participants were enrolled from 2006 to 2019. The TBIL was divided into four groups according to the quartile. The Cox proportional hazards model was used to estimate the HRs with 95% CIs of different TBIL level and fundus arteriosclerosis in men and women. The dose-response relationship between TBIL and fundus arteriosclerosis was estimated using restricted cubic splines method. In males, after adjusting for potential confounders, the Q2 to Q4 level of TBIL were significantly associated with the risk of fundus arteriosclerosis. The HRs with 95% CIs were 1.217 (1.095-1.354), 1.255 (1.128-1.396) and 1.396 (1.254-1.555), respectively. For females, TBIL level was not associated with the incidence of fundus arteriosclerosis. In addition, a linear relationship between TBIL and fundus arteriosclerosis in both genders (P < 0.0001 and P = 0.0047, respectively). In conclusion, the incidence of fundus arteriosclerosis is positively correlated with serum TBIL level in males, but not in females. In addition, there was a linear dose-response relationship between TBIL and incidence of fundus arteriosclerosis.

High FAAP24 expression reveals poor prognosis and an immunosuppressive microenvironment shaping in AML.

BACKGROUND: As a core member of the FA complex, in the Fanconi anemia pathway, FAAP24 plays an important role in DNA damage repair. However, the association between FAAP24 and patient prognosis in AML and immune infiltration remains unclear. The purpose of this study was to explore its expression characteristics, immune infiltration pattern, prognostic value and biological function using TCGA-AML and to verify it in the Beat AML cohort. METHODS: In this study, we examined the expression and prognostic value of FAAP24 across cancers using data from TCGA, TARGET, GTEx, and GEPIA2. To further investigate the prognosis in AML, development and validation of a nomogram containing FAAP24 were performed. GO/KEGG, ssGSEA, GSVA and xCell were utilized to explore the functional enrichment and immunological features of FAAP24 in AML. Drug sensitivity analysis used data from the CellMiner website, and the results were confirmed in vitro. RESULTS: Integrated analysis of the TCGA, TARGET and GTEx databases showed that FAAP24 is upregulated in AML; meanwhile, high FAAP24 expression was associated with poor prognosis according to GEPIA2. Gene set enrichment analysis revealed that FAAP24 is implicated in pathways involved in DNA damage repair, the cell cycle and cancer. Components of the immune microenvironment using xCell indicate that FAAP24 shapes an immunosuppressive tumor microenvironment (TME) in AML, which helps to promote AML progression. Drug sensitivity analysis showed a significant correlation between high FAAP24 expression and chelerythrine resistance. In conclusion, FAAP24 could serve as a novel prognostic biomarker and play an immunomodulatory role in AML. CONCLUSIONS: In summary, FAAP24 is a promising prognostic biomarker in AML that requires further exploration and confirmation.

Correlation between meteorological factors and vitamin D status under different season.

Pregnant women with low vitamin D levels tend to have poor clinical outcomes. Meteorological factors were associated with vitamin D. Here, we aimed to study the current status of 25-Hydroxy vitamin D (25(OH)D) concentrations in pregnant women in Kunshan city and investigate the meteorological factors associated with 25(OH)D levels under different seasons. The correlation between meteorological factors and 25(OH)D levels was estimated by cross-correlation analysis and multivariate logistic regression. A restrictive cubic spline method was used to estimate the non-linear relationship. From 2015 to 2020, a total of 22,090 pregnant women were enrolled in this study. Pregnant women with 25(OH)D concentrations below 50 nmol/l represent 65.85% of the total study population. There is a positive correlation between temperature and 25(OH)D. And there is a protective effect of the higher temperature on vitamin D deficiency. However, in the subgroup analysis, we found that in autumn, high temperatures above 30 °C may lead to a decrease in 25(OH)D levels. This study shows that vitamin D deficiency in pregnant women may widespread in eastern China. There is a potential inverted U-shaped relationship between temperature and 25(OH)D levels, which has implications for understanding of vitamin D changes under different seasons.

Sex-specific association of serum uric acid trajectories with risk of incident retinal arteriosclerosis in Chinese population: A population-based longitudinal study.

Background: The impact of serum uric acid (SUA) trajectories on the development of retinal arteriosclerosis is uncertain. The purpose of this study was to identify adult SUA trajectories by sex and determine their association with risk of retinal arteriosclerosis. Methods: In this longitudinal study, 4,324 participants who were aged between 18 and 60 years without retinal arteriosclerosis at or before baseline (from January 1, 2010, through December 31, 2010) were included. Group-based trajectory modeling was used to identify SUA trajectories during the exposure period (from January 1, 2006, through December 31, 2010). Cox proportional-hazards models were applied to evaluate the associations between SUA trajectories and the risk of incident retinal arteriosclerosis during the outcome period (from January 1, 2011, through December 31, 2019). Results: 4 distinct SUA trajectories were identified in both women and men: low, moderate, moderate-high, and high. During a median follow-up of 9.54 years (IQR 9.53-9.56), 97 women and 295 men had developed retinal arteriosclerosis. In the fully adjusted model, a significant association between the moderate-high SUA trajectory group and incidence of retinal arteriosclerosis was observed only in men (HR: 1.76, 95% CI: 1.17-2.65) compared with the low trajectory group, but not in women (HR: 0.77, 95% CI: 0.39-1.52). Also, the high SUA trajectory group had the highest risk with an adjusted HR of 1.81 (95% CI, 1.04-3.17) in men. However, they did not exhibit a substantially increased risk in women. Conclusion: Higher SUA trajectory groups were significantly associated with an increased risk of incident retinal arteriosclerosis in men but not in women.

Predicting monthly hospital outpatient visits based on meteorological environmental factors using the ARIMA model.

Accurate forecasting of hospital outpatient visits is beneficial to the rational planning and allocation of medical resources to meet medical needs. Several studies have suggested that outpatient visits are related to meteorological environmental factors. We aimed to use the autoregressive integrated moving average (ARIMA) model to analyze the relationship between meteorological environmental factors and outpatient visits. Also, outpatient visits can be forecast for the future period. Monthly outpatient visits and meteorological environmental factors were collected from January 2015 to July 2021. An ARIMAX model was constructed by incorporating meteorological environmental factors as covariates to the ARIMA model, by evaluating the stationary [Formula: see text], coefficient of determination [Formula: see text], mean absolute percentage error (MAPE), and normalized Bayesian information criterion (BIC). The ARIMA [Formula: see text] model with the covariates of [Formula: see text], [Formula: see text], and [Formula: see text] was the optimal model. Monthly outpatient visits in 2019 can be predicted using average data from past years. The relative error between the predicted and actual values for 2019 was 2.77%. Our study suggests that [Formula: see text], [Formula: see text], and [Formula: see text] concentration have a significant impact on outpatient visits. The model built has excellent predictive performance and can provide some references for the scientific management of hospitals to allocate staff and resources.

Comprehensive analysis of ERCC3 prognosis value and ceRNA network in AML.

BACKGROUND: Acute myeloid leukemia (AML) is a hematological malignancy with high molecular and clinical heterogeneity, and is the most common type of acute leukemia in adults. Due to limited treatment options, AML is prone to relapse and has a poor prognosis. Excision repair cross-complementing 3 (ERCC3) is an important member of nucleotide excision repair (NER) that is overexpressed in types of solid cancers and potentially regarded as a prognostic factor. However, its role in AML remains unclear. The purpose of this study was to explore ERCC3 expression and functions in AML. METHODS: The Cancer Genome Atlas (TCGA) and GEO (Gene Expression Omnibus) were used to test the accuracy of ERCC3 expression levels for AML diagnosis. Using online databases and R packages, we also explored the signaling pathway, epigenetic regulation, infiltration of immune cells, clinical prognostic value, and ceRNA network in AML. RESULTS: Our results revealed that ERCC3 expression was increased in AML and that high ERCC3 expression had good value for disease-free survival and overall survival in AML patients who underwent allogeneic hematopoietic stem cell transplantation (allo-HSCT). We found that ERCC3 and co-expressed genes were mainly involved in chemical carcinogenesis/reactive oxygen species, ubiquitin-mediated protein degradation and oxidative phosphorylation. In addition, almost all the m6A-related coding genes (except GF2BP1) were positively associated with ERCC3 expression. We also constructed a ceRNA regulatory network containing ERCC3 in AML and identified 6 pairs of ceRNA networks, indicating that ERCC3 expression is regulated by a noncoding RNA system. CONCLUSION: This study demonstrated that ERCC3 was overexpressed in AML and that high ERCC3 expression can be considered a biomarker conducive to allo-HSCT in AML patients.

Diagnostic value of radiomics model based on gray-scale and contrast-enhanced ultrasound for inflammatory mass stage periductal mastitis/duct ectasia.

Objective: The present study aimed to investigate the clinical application value of the radiomics model based on gray-scale ultrasound (GSUS) and contrast-enhanced ultrasound (CEUS) images in the differentiation of inflammatory mass stage periductal mastitis/duct ectasia (IMSPDM/DE) and invasive ductal carcinoma (IDC). Methods: In this retrospective study, 254 patients (IMSPDM/DE: 129; IDC:125) were enrolled between January 2018 and December 2020 as a training cohort to develop the classification models. The radiomics features were extracted from the GSUS and CEUS images. The least absolute shrinkage and selection operator (LASSO) regression model was employed to select the corresponding features. Based on these selected features, logistic regression analysis was used to aid the construction of these three radiomics signatures (GSUS, CEUS and GSCEUS radiomics signature). In addition, 80 patients (IMSPDM/DE:40; IDC:40) were recruited between January 2021 and November 2021 and were used as the validation cohort. The best radiomics signature was selected. Based on the clinical parameters and the radiomics signature, a classification model was built. Finally, the classification model was assessed using nomogram and decision curve analyses. Results: Three radiomics signatures were able to differentiate IMSPDM/DE from IDC. The GSCEUS radiomics signature outperformed the other two radiomics signatures and the AUC, sensitivity, specificity, and accuracy were estimated to be 0.876, 0.756, 0.804, and 0.798 in the training cohort and 0.796, 0.675, 0.838 and 0.763 in the validation cohort, respectively. The lower patient age (p<0.001), higher neutrophil count (p<0.001), lack of pausimenia (p=0.023) and GSCEUS radiomics features (p<0.001) were independent risk factors of IMSPDM/DE. The classification model that included the clinical factors and the GSCEUS radiomics signature outperformed the GSCEUS radiomics signature alone (the AUC values of the training and validation cohorts were 0.962 and 0.891, respectively). The nomogram was applied to the validation cohort, reaching optimal discrimination, with an AUC value of 0.891, a sensitivity of 0.888, and a specificity of 0.750. Conclusions: The present study combined the clinical parameters with the GSCEUS radiomics signature and developed a nomogram. This GSCEUS radiomics-based classification model could be used to differentiate IMSPDM/DE from IDC in a non-invasive manner.

The development and validation of a predictive model for neonatal phototherapy outcome using admission indicators.

Delayed exchange transfusion therapy (ETT) after phototherapy failure for newborns with severe hyperbilirubinemia could lead to serious complications such as bilirubin encephalopathy (BE). In this current manuscript we developed and validated a model using admission data for early prediction of phototherapy failure. We retrospectively examined the medical records of 292 newborns with severe hyperbilirubinemia as the training cohort and another 52 neonates as the validation cohort. Logistic regression modeling was employed to create a predictive model with seven significant admission indicators, i.e., age, past medical history, presence of hemolysis, hemoglobin, neutrophil proportion, albumin (ALB), and total serum bilirubin (TSB). To validate the model, two other models with conventional indicators were created, one incorporating the admission indicators and phototherapy failure outcome and the other using TSB decrease after phototherapy failure as a variable and phototherapy outcome as an outcome indicator. The area under the curve (AUC) of the predictive model was 0.958 [95% confidence interval (CI): 0.924-0.993] and 0.961 (95% CI: 0.914-1.000) in the training and validation cohorts, respectively. Compared with the conventional models, the new model had better predictive power and greater value for clinical decision-making by providing a possibly earlier and more accurate prediction of phototherapy failure. More rapid clinical decision-making and interventions may potentially minimize occurrence of serious complications of severe neonatal hyperbilirubinemia.