Biological variation in the estimated glomerular filtration rate of healthy individuals within 24 h calculated using 2021CKD-EPI equations.

BACKGROUND AND AIMS: Use the MDRD (Modification of Diet in Renal Disease) and 2021 CKD-EPI (Chronic Kidney Disease Epidemiology Collaboration) equation void of race coefficients (CKD-EPICrea, CKD-EPICys-C, and CKD-EPICrea+Cys-C) to estimate the BV (Biological variation) of eGFR (estimated glomerular filtration rate) within 24 h in a healthy population to help explain future studies using eGFR in the context of a known BV. METHODS: Blood samples were collected from 30 healthy subjects at six time points within 24 h. Serum creatinine (S-Crea) and serum cystatin C (S-Cys-C) were measured, and the BV of eGFR was calculated. Outlier and variance homogeneity analyses were performed, followed by CV-ANOVA on trend-corrected data. RESULTS: The eGFR CVI for the four equations (MDRD, CKD-EPICrea, CKD-EPICys-C, and CKD-EPICrea+Cys-C) were 8.39% (7.50-9.51%), 3.90% (3.49-4.42%), 6.58% (5.88-7.46%), and 5.03% (4.50-5.71%), respectively. The corresponding II and RCVpos/neg values were 0.69, 0.48, 0.51, and 0.31, and (29.30%, - 22.66%), (12.69%, - 11.2 6%), (20.97%, - 17.33%), and (15.88%, - 13.70%), respectively; RCVpos /neg of eGFR was highest in the MDRD equation and lowest in the CKD-EPI Crea equation. Additionally, the RCVpos/neg values of the individual was highest in the MDRD equation and lowest in the CKD-EPICrea+Cys-C equation; they are (56.51%, - 36.11%) and (5.01%, - 4.77%), respectively. CONCLUSIONS: We present data on the 24 h BV eGFR of the 2021 CKD-EPI equations. The presence of BV has impact on the interpretation of GFR results, affecting CKD disease grading. The RCVpos/neg differences were large among the individuals. When using eGFRs based on the MDRD and CKD-EPI equations, it is necessary to combine RCVpos/neg values before interpreting the results.

Intraday Changes and Clinical Applications of Thyroid Function Biomarkers in Healthy Subjects.

OBJECTIVE: We evaluated the intraday changes of thyroid function biomarkers in healthy subjects to help clinicians diagnose thyroid diseases in appropriate timing. METHODS: Blood samples were collected from 31 subjects at 0:00, 4:00, 8:00, 12:00, 16:00 and 20:00 on the sampling day and analyzed for thyroid-stimulating hormone (TSH), triiodothyronine (T3), thyroxine (T4), free T3 (FT3), and free T4 (FT4). The intraday concentration changes were analyzed using Friedman's 2-way analysis of variance by ranks. RESULTS: The concentrations of TSH, T3, T4, FT3, and FT4 in males were significantly higher than those in females (P < .01). The obvious peak circadian rhythm of TSH was observed at 0:00 AM with gradual decline thereafter, whereas other biomarkers showed no rhythmic changes. CONCLUSION: Sex differences should be considered in interpreting thyroid function tests. It is important to select the sampling time according to the clinician's diagnostic needs, especially at night when TSH secretion peaks.

Development and validation of a machine learning-derived radiomics model for diagnosis of osteoporosis and osteopenia using quantitative computed tomography.

BACKGROUND: To develop and validate a quantitative computed tomography (QCT) based radiomics model for discriminating osteoporosis and osteopenia. METHODS: A total of 635 patients underwent QCT were retrospectively included from November 2016 to November 2019. The patients with osteopenia or osteoporosis (N = 590) were divided into a training cohort (N = 414) and a test cohort (N = 176). Radiomics features were extracted from the QCT images of the third lumbar vertebra. Minimum redundancy and maximum relevance and least absolute shrinkage and selection operator were used for data dimensional reduction, features selection and radiomics model building. Multivariable logistic regression was applied to construct the combined clinical-radiomic model that incorporated radiomics signatures and clinical characteristics. The performance of the combined clinical-radiomic model was evaluated by the area under the curve of receiver operator characteristic curve (ROC-AUC), accuracy, specificity, sensitivity, positive predictive value, and negative predictive value. RESULTS: The patients with osteopenia or osteoporosis were randomly divided into training and test cohort with a ratio of 7:3. Six more predictive radiomics signatures, age, alkaline phosphatase and homocysteine were selected to construct the combined clinical-radiomic model for diagnosis of osteoporosis and osteopenia. The AUC of the combined clinical-radiomic model was 0.96 (95% confidence interval (CI), 0.95 to 0.98) in the training cohort and 0.96 (95% CI 0.92 to 1.00) in the test cohort, which were superior to the clinical model alone (training-AUC = 0.81, test-AUC = 0.79). The calibration curve demonstrated that the radiomics nomogram had good agreement between prediction and observation and decision curve analysis confirmed clinically useful. CONCLUSIONS: The combined clinical-radiomic model that incorporates the radiomics score and clinical risk factors, can serve as a reliable and powerful tool for discriminating osteoporosis and osteopenia.

Identification of upper GI diseases during screening gastroscopy using a deep convolutional neural network algorithm.

BACKGROUND AND AIMS: The clinical application of GI endoscopy for the diagnosis of multiple diseases using artificial intelligence (AI) has been limited by its high false-positive rates. There is an unmet need to develop a GI endoscopy AI-assisted diagnosis system (GEADS) to improve diagnostic accuracy and clinical utility. METHODS: In this retrospective, multicenter study, a convolutional neural network was trained to assess upper GI diseases based on 26,228 endoscopic images from Dazhou Central Hospital that were randomly assigned (3:1:1) to a training dataset, validation dataset, and test dataset, respectively. To validate the model, 6 external independent datasets comprising 51,372 images of upper GI diseases were collected. In addition, 1 prospective dataset comprising 27,975 images was collected. The performance of GEADS was compared with endoscopists with 2 professional degrees of expertise: expert and novice. Eight endoscopists were in the expert group with >5 years of experience, whereas 3 endoscopists were in the novice group with 1 to 5 years of experience. RESULTS: The GEADS model achieved an accuracy of .918 (95% confidence interval [CI], .914-.922), with an F1 score of .884 (95% CI, .879-.889), recall of .873 (95% CI, .868-.878), and precision of .890 (95% CI, .885-.895) in the internal validation dataset. In the external validation datasets and 1 prospective validation dataset, the diagnostic accuracy of the GEADS ranged from .841 (95% CI, .834-.848) to .949 (95% CI, .935-.963). With the help of the GEADS, the diagnosing accuracies of novice and expert endoscopists were significantly improved (P < .001). CONCLUSIONS: The AI system can assist endoscopists in improving the accuracy of diagnosing upper GI diseases.

Biological variation in the serum and urine kidney injury markers of a healthy population measured within 24 hours.

BACKGROUND AND AIMS: To explore the biological variation (BV) of kidney injury markers in serum and urine of healthy subjects within 24 hours to assist with interpretation of future studies using these biomarkers in the context of known BV. MATERIALS AND METHODS: Serum and urine samples were collected every 4 hours (0, 4, 8, 12, 16 and 20 hours) from 31 healthy subjects within 24 hours and serum creatinine (s-Crea), serum ß2-microglobin (s-ß2MG), serum cystatin C (s-CYSC), serum neutrophil gelatinase-associated lipoprotein (s-NGAL), urine creatinine (u-Crea), urine ß2-microglobin (u-ß2MG), urine cystatin C (u-CYSC), urine neutrophil gelatinase-associated lipoprotein (u-NGAL) were measured. Outlier and variance homogeneity analyses were performed, followed by CV-ANOVA analysis on trend-corrected data (if relevant), and analytical (CVA), within-subject (CVI), and between-subject (CVG) biological variation were calculated. RESULTS: The concentration of kidney injury markers in male was higher than that in female, except for u-CYSC and u-NGAL. There were no significant difference in serum and urine kidney injury markers concentration at different time points. Serum CVI was lower than urine CVI, serum CVG was higher than CVI, and urine CVG was lower than CVI. The individual index (II) of serum kidney injury markers was less than 0.6, while the II of urinary kidney injury markers was more than 1.0. CONCLUSIONS: This study provides new short-term BV data for kidney injury markers in healthy subjects within 24 hours, which are of great significance in explaining other AKI / CKD studies.

RIPK1 is a negative mediator in Aquaporin 1-driven triple-negative breast carcinoma progression and metastasis.

The triple-negative breast carcinoma (TNBC) is the most aggressive subtype of breast cancer. In TNBC, Aquaporin 1 (AQP1), a water-transporting transmembrane protein, is aberrantly enriched in cytoplasm and causes tumor cell death evasion. However, the carcinogenetic bioactivities of cytoplasmic AQP1 cannot be attributed to the canonical "osmotic engine model". In the present study, the receptor-interacting protein kinase 1 (RIPK1), a cell death regulator, was identified to negatively mediate AQP1-driven TNBC progression and metastasis. AQP1 overabundance and RIPK1 depletion occurred in TNBC, which were correlated with aggressive oncological features and poor prognosis. AQP1 bound with RIPK1, resulting in the inhibition of RIPK1/RIPK3/MLKL-mediated necroptosis and RIPK1/caspase-8/caspase-3-mediated apoptosis. Genetic inhibition of RIPK1 significantly exacerbated the pro-tumor effect of AQP1, while ectopic expression of RIPK1 notably blunted AQP1 signaling. Mechanistically, AQP1 binds to the D324 site of RIPK1, and facilitates RIPK1 cleavage and inactivation by excessively activating the caspase-8/RIPK1 negative feedback loop. RIPK1D324K overexpression significantly prevented RIPK1 cleavage and weakened the aggressiveness of AQP1-enriched TNBC cells. Overall, our findings clarify the underlying mechanism of cytoplasmic AQP1-driven TNBC progression and metastasis, in which RIPK1 exerts an essential role as a negative mediator and exhibits the potential as a therapeutic target for TNBC.

AI based colorectal disease detection using real-time screening colonoscopy.

Colonoscopy is an effective tool for early screening of colorectal diseases. However, the application of colonoscopy in distinguishing different intestinal diseases still faces great challenges of efficiency and accuracy. Here we constructed and evaluated a deep convolution neural network (CNN) model based on 117 055 images from 16 004 individuals, which achieved a high accuracy of 0.933 in the validation dataset in identifying patients with polyp, colitis, colorectal cancer (CRC) from normal. The proposed approach was further validated on multi-center real-time colonoscopy videos and images, which achieved accurate diagnostic performance on detecting colorectal diseases with high accuracy and precision to generalize across external validation datasets. The diagnostic performance of the model was further compared to the skilled endoscopists and the novices. In addition, our model has potential in diagnosis of adenomatous polyp and hyperplastic polyp with an area under the receiver operating characteristic curve of 0.975. Our proposed CNN models have potential in assisting clinicians in making clinical decisions with efficiency during application.

Time Difference of Arrival on Contrast-Enhanced Ultrasound in Distinguishing Benign Inflammation From Malignant Peripheral Pulmonary Lesions.

INTRODUCTION: Worldwide, the incidence and mortality of lung cancer are at the highest levels, and the most lesions are located in the lung periphery. Despite extensive screening and diagnosis, the pathologic types of peripheral pulmonary lesions (PPLs) are difficult to diagnose by noninvasive examination. This study aimed to identify a novel index-time difference of arrival (TDOA)-to discriminate between benign inflammation and malignant PPLs. METHODS: Using contrast-enhanced ultrasound (CEUS), we retrospectively analyzed 96 patients with PPLs who had undergone biopsy to confirm the pathologic types. All data were collected from Dazhou Central Hospital between December 2012 and July 2019. The parameters of CEUS were analyzed by two assistant chief physicians of ultrasound diagnosis. Area under the receiver operating characteristic curve analysis, sensitivity, specificity, positive predictive value, and negative predictive value were calculated to assess the diagnostic ability of different indices. RESULTS: We found that the TDOA significantly distinguished benign inflammation from malignant lesions. The TDOA was markedly increased in patients with malignant lesions than benign inflammation lesions (P < 0.001). Compared with conventional time-intensity curve (TIC) indices, TDOA showed high diagnostic accuracy (area under the curve = 0.894). Moreover, conventional diagnostic indices did not affect the diagnostic performance of TDOA by adjusting the receiver operating characteristic curve. CONCLUSION: TDOA is feasible for the diagnosis of benign inflammation and malignant PPLs.

The Role of Vitamin D in Combination Treatment for Patients With Rheumatoid Arthritis.

Background: The aim of this study is to evaluate the clinical efficacy of vitamin D (VitD) supplementation in terms of response to treatment and improvement of disease activity in rheumatoid arthritis (RA). Methods: This study analyzed 1180 RA patients' records treated at Mianyang Central Hospital from February 2015 to July 2019. The patients were allocated into VitD group and control group based on their medical regimens. The outcome measures were primary efficacy, defined as treatment response-based EULAR response criteria in RA, and secondary efficacy, defined as improvement in disease activity indicators. Safety was evaluated according to the incidence of all-cause infections. Results: At month 6, the primary efficacy revealed that there were 22.8% good responders and 19.0% moderate responders in the VitD group, and 22.3% good responders and 22.3% moderate responders in the control group; there were no differences between the two groups (p = 0.754). The similar primary efficacy outcomes were observed at months 3, 12, and >12. The secondary efficacy indicated that there were no differences in most indexes between the two groups at months 1, 3, 6, 12, and >12. The subgroups (based on baseline DAS28 (CRP), glucocorticoids use and disease duration) analysis results suggested that VitD group didn't have the advantage for treating RA. The incidence of infections was similar in the two groups. Conclusion: VitD supplementation did not provide additional benefit for anti-rheumatic treatment. These data supported the need for prospective, randomized, controlled trials to evaluate the role of VitD supplementation in treating RA.

Transcriptional Profiling Reveals the Regulatory Role of CXCL8 in Promoting Colorectal Cancer.

C-X-C motif chemokine ligand 8 (CXCL8) is involved in tumor proliferation, migration, and invasion. However, the function of CXCL8 in colorectal cancer (CRC) is controversial. Here, we analyzed RNA-sequencing (RNA-seq) data to identify differentially expressed genes and pathways according to gene ontology (GO) enrichment and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways associated with CRC. The levels of the mRNA encoding CXCL8 were significantly increased in early and advanced stages of CRC, as well as in metastases and nonmetastasis cases using RNA-seq analysis (n = 91). These findings were consistent with immunohistochemical analysis of CXCL8 expression (n = 87). Protein-protein interaction (PPI) prediction combined with transcriptional profiling data revealed that CXCL8 levels positively correlated with cAMP responsive element binding protein 1 (CREB1)/ribosomal protein S6 kinase B1 (RPS6KB1) expression, which promotes cell proliferation and differentiation in high expression, while inversely correlated with the expression of Bcl2 associated agonist of cell death (BAD) protein to inhibit apoptosis during the progression of CRC. These findings provide compelling clinical and molecular evidence to support the conclusion that CXCL8 contributes to the genesis and progression of CRC.