BACKGROUND:The aim of this study is to assess the comparability of three commercial procalcitonin (PCT) immunoassays, including Kryptor BRAHMS PCT sensitive, Beckman Coulter DXI 800 Access PCT and Roche BRAHMS PCT, and to evaluate the commutability of four types of External Quality Assessment/Proficiency Tests (EQA/PT) materials among the three assays. METHODS:The limits of quantitation or functional sensitivity (LoQ/FS) for the three PCT assays were validated according to the CLSI EP17 guidelines. Precision was verified at 0.5 μg/L and 2 μg/L according to CLSI EP15 document. A method comparison study was conducted using 506 clinical serum samples from individual patients. Seventeen EQA/PT materials, including frozen pooled human serum (FFS), lyophilized materials (Lyo), bovine serum albumin solution (BSA) with recombinant PCT, and human serum (HS) with recombinant PCT, were tested together with 50 clinical individual serum samples. The commutability of EQA/PT materials was evaluated using the difference in bias approach. RESULTS:The LoQ/FS and imprecision of all three assays met the claimed standards. Pairwise comparisons demonstrated high correlations ranging from 0.994 to 0.998 (all p < 0.0001), with relative biases between -13.9% and 23.3%. Clinical consistency at thresholds of 0.1 μg/L, 0.25 μg/L, 0.5 μg/L, 2.0 μg/L, and 10 μg/L exceeded 95% across all methods. The commutability of the four EQA/PT material types was generally poor. The proportions of commutable results were 6/18 for Lyo, 1/15 for BSA, 5/15 for HS, and 7/12 for FFS, with FFS showing the highest commutability among the materials tested. CONCLUSIONS:The assays meet clinical requirements for PCT testing. Among the four EQA/PT materials evaluated, the HS, Lyo, and BSA materials demonstrated limited commutability. Although FFS does not exhibit full commutability, it is considered the most suitable EQA/PT material among those tested. Some residual non-commutability still exists and warrants further investigation.

Metastasis-associated protein 2 (MTA2), a master transcriptional regulator, through multiple target genes and interacting proteins, has been demonstrated to play a vital role in the regulation of proliferation, replication, apoptosis, autophagy, DNA damage repair, preimplantation, embryonic development and immune cell differentiation. Despite extensive research, the physiological role and pathogenic mechanisms of MTA2 remain poorly understood. Here, we mainly review in the current research the status of MTA2 and its implications in normal development and various tumor biology. Accumulating evidence suggests that MTA2 is frequently amplify in several types of cancers, closely associates with tumor cells migration and invasion, relates to the malignant characteristics and poor prognosis, which therefore has been considered as playing tumor oncogenic roles. Substantial evidence indicates that MTA2 functions by modulating downstream targets including cell growth, invasion as well as angiogenesis related genes. Confusingly, the proliferation effect of MTA2 remains elusive and even conflicting in the development of several solid tumors. Furthermore, we discuss the upstream regulation of MTA2 by transcription factors, microRNAs and lncRNAs in specific physiology and pathology conditions, which results in the abnormal MTA2 expression in various aspects of cancer. In this context, we summarize linked function of MTA2 directly to oncogenesis and might provide a significant avenue for the treatment of diseases. We hope that this review will help tumor molecular biologists further understand the molecular mechanism of MTA2 in normal development and cancer.

In recent years, the accelerating emergence of antibiotic-resistant bacterial pathogens has posed profound challenges to the therapeutic management, prophylactic strategies, and epidemiological control of infectious diseases caused by these microorganisms. Consequently, identifying resistant bacteria is essential for therapeutic decisions and epidemiological studies. However, conventional approaches for the detection of antibiotic resistance are frequently constrained by lengthy protocols, substantial costs, and operational intricacies, thereby impeding the rapid and precise identification of resistance phenotypes. Raman optical tweezers have proven useful for classifying different bacterial species and isolates. This study establishes a fast, reliable, and cost-effective method to differentiate between Escherichia coli strains with antibiotic resistance (extended spectrum β-lactam resistant, ESBL) and susceptible strains using Raman optical tweezers and deep learning techniques. High-quality single-cell Raman spectra were collected from antibiotic-resistant and susceptible strains without exposure to antibiotics, revealing a higher nucleic acid/protein ratio in resistant strains. We propose a new network, RamanU-Net, for the accurate classification of these Raman spectra. The model achieved 99.5% average accuracy in identifying antibiotic-resistant and sensitive strains of Escherichia coli. Our results demonstrate that combining Raman optical tweezers with deep learning models can enable rapid identification of bacterial antibiotic resistance while significantly reducing the associated time, cost, and workload.

Background:: Clear cell renal carcinoma (ccRCC) is one of the most common urological tumors worldwide and metabolic reprogramming is its distinguishing feature. A systematic study on the role of the metabolism-related genes in ccRCC cancer stem cells (CSCs) is still lacking. Moreover, an effective metabolism-related prediction signature is urgently needed to assess the prognosis of ccRCC patients. Methods:: Gene expression profiles of GSE48550 and GSE84546 were analyzed for the role of metabolism-related gene in ccRCC-CSCs. The GSE22541 dataset were used to construct and validate an effective metabolism-related prediction signature to assess the prognosis of ccRCC patients. Results:: For glycolytic metabolism, we found that HKDC1, PFKM and LDHB were significantly upregulated in ccRCC-CSCs in GSE84546. For TCA cycle, ACO1, SDHA and MDH1 were significantly downregulated in ccRCC-CSCs in both GSE48550 and GSE84546. For fatty acid metabolism, CPT1A and ACACB were significantly upregulated in ccRCC-CSCs in GSE84546. It is worth noting that SCD was significantly downregulated in both GSE48550 and GSE84546. For glutamine metabolism, SLC1A5, GLS and GOT1 were significantly upregulated in GSE84546. An eight-gene CSCs metabolism-related risk signature including HKDC1, PFKM, LDHB, IDH1, OGDH, SDHA, GLS and GLUL were constructed to predict the overall survival (OS) of ccRCC patients. Patients could be separated into two groups, and the patients with lower risk scores had longer survival time. Conclusion:: Our study indicated that metabolic reprogramming, including glycolytic metabolism, TCA cycle, fatty acid metabolism and glutamine metabolism, is more obvious in CD105+ renal cells (GSE84546) than CD133+ renal cells (GSE48550). An eight-gene metabolismrelated risk signature including HKDC1, PFKM, LDHB, IDH1, OGDH, SDHA, GLS and GLUL can effectively predict OS in ccRCC.

BackgroudRoutine metabolic assessments for methylmalonic acidemia (MMA), propionic acidemia (PA), and homocysteinemia involve detecting metabolites in dried blood spots (DBS) and analyzing specific biomarkers in serum and urine. This study aimed to establish a liquid chromatography–tandem mass spectrometry (LC–MS/MS) method for the simultaneous detection of three specific biomarkers (methylmalonic acid, methylcitric acid, and homocysteine) in DBS, as well as to appraise the applicability of these three DBS metabolites in monitoring patients with MMA, PA, and homocysteinemia during follow-up.MethodsA total of 140 healthy controls and 228 participants were enrolled, including 205 patients with MMA, 17 patients with PA, and 6 patients with homocysteinemia. Clinical data and DBS samples were collected during follow-up visits.ResultsThe reference ranges (25th–95th percentile) for DBS methylmalonic acid, methylcitric acid, and homocysteine were estimated as 0.04–1.02 μmol/L, 0.02–0.27 μmol/L and 1.05–8.22 μmol/L, respectively. Following treatment, some patients achieved normal metabolite concentrations, but the majority still exhibited characteristic biochemical patterns. The concentrations of methylmalonic acid, methylcitric acid, and homocysteine in DBS showed positive correlations with urine methylmalonic acid (r = 0.849, p < 0.001), urine methylcitric acid (r = 0.693, p < 0.001), and serum homocysteine (r = 0.721, p < 0.001) concentrations, respectively. Additionally, higher levels of DBS methylmalonic acid and methylcitric acid may be associated with increased cumulative complication scores.ConclusionThe LC–MS/MS method established in this study reliably detects methylmalonic acid, methylcitric acid, and homocysteine in DBS. These three DBS metabolites can be valuable for monitoring patients with MMA, PA, and homocysteinemia during follow-up. Further investigation is required to determine the significance of these DBS biomarkers in assessing disease burden over time.

OBJECTIVES:Systemic lupus erythematosus (SLE) is a heterogeneous autoimmune disease. Its diagnosis poses significant challenges especially at early stages and in atypical cases. The aim of this study was to develop a machine learning model based on common laboratory tests that can aid SLE diagnosis.METHODS:A standard protocol was developed to collect data of SLE and control immune diseases. A 10-fold cross-validation was performed in the modeling dataset (n=862), and an external dataset (n=198) was used for model validation. Machine learning algorithms were applied to construct a diagnostic model. Performance was evaluated based on area under the curve (AUC) values, F1-score, negative predictive value, positive predictive value, accuracy, sensitivity, and specificity.RESULTS:The optimal model was based on a random forest algorithm with 10 clinical features. Thrombin time, prothrombin activity, and uric acid contributed most to the diagnostic model. The SLE diagnostic model showed sufficient predictive accuracy, with AUC values of 0.8286 in the validation dataset.CONCLUSIONS:Our diagnostic model based on 10 common laboratory tests identified the patients with SLE with high accuracy. An online version of the model can potentially be applied in clinical settings for the differential diagnosis of SLE.

Purpose: Hospital-acquired pneumonia (HAP) caused by carbapenem-resistant K. pneumoniae (CRKP), especially in elderly patients, results in high morbidity and mortality. Studies on risk factors, mortality, and antimicrobial susceptibility of CRKP pulmonary infection among elderly patients are lacking. Patients andMethods: A retrospective case-control study was conducted from January 2019 to December 2021. The elderly inpatients (>= 65 years) who were diagnosed with HAP caused by K. pneumoniae were enrolled. Clinical data were collected. Univariate and multivariate logistic regression analyses were used to identify risk factors. Propensity score matching was used to minimize the effect of potential confounding variables. Kaplan-Meier analysis was used to compare survival.Results: A total of 115 patients with CRKP infection and 78 patients with carbapenem-susceptible K. pneumoniae (CSKP) infection were recruited. There were four independent risk factors for CRKP infection: history of intensive care unit (ICU) stays from hospital admission to positive respiratory specimen culture for K. pneumoniae (odds ratio (OR)=2.530), Charlson comorbidity index score >3 (OR = 2.420), prior exposure to carbapenems (OR = 5.280), and prior K. pneumoniae infection or colonization in the preceding 3 years (OR = 18.529). The all-cause 30-day mortality was 22.3%, the mortality of CRKP and CSKP infection was 28.7% and 12.8%, respectively. Independent risk factors for mortality included: older age (OR = 1.107), immunocompromised patients (OR = 8.632), severe pneumonia (OR = 51.244), quick Sepsis-related Organ Failure Assessment (qSOFA) score >= 2 (OR = 6.187), exposure to tigecycline before infection (OR = 24.702), and prolonged ICU stay (OR = 0.987). Thirty-day mortality was significantly lower in patients receiving ceftazidime-avibactam (CAZ-AVI) containing regimens than patients receiving polymyxin B sulfate (PB) containing regimens (P = 0.048). qSOFA score had a good prognostic effect [area under receiver operating characteristic curve (AUROC) of 0.838].Conclusion: Active screening of CRKP for the high-risk populations, especially elderly patients, is significant for early detection and successful management of CRKP infection.

Background Primary Sjögren’s syndrome (pSS) is a common chronic systemic autoimmune disorder which primarily affects the exocrine glands. Patients may have extraglandular disease involving multiple organs, including the kidneys. This study aimed at investigating the clinical data and laboratory markers which were associated with renal function damage or renal involvement. Method One thousand two hundred eighty-eight adult pSS patients from the Department of Rheumatology and Clinical Immunology were enrolled in this retrospective cohort study. And there were 334 patients of them followed up for more than two years for analyzing demographic, clinical data and laboratory markers. Statistical analysis was performed by R software (Version 3.6.2). Result Nearly 95% of 1288 pSS patients were women, and the positive rates of anti-SSA (Sjögren's syndrome A) and anti-SSB were 63% and 27% respectively. 12% of the pSS patients presented renal involvement with eGFR < 60 mL/min/1.73 m 2 , and the mean age of hospital presentation, serum creatinine and urea were the highest ( P < 0.001), and ANA (antinuclear antibody)-positive, anti-SSB-positive and anti-scl-70-positive were more prevalent in this group. Multivariate analyses showed that age, urea, chlorine and anti-SSA indicate a significant association with renal dysfunction. Potassium, sodium and Jo-1 were also confirmed to be related with decreased renal function. The receiver operating characteristic (ROC) analysis including the above factors showed a good performance on the evaluation of renal injury including eGFR < 60 mL/min/1.73 m 2 and eGFR 60 -90 mL/min/1.73 m 2 in pSS, with area under curve (AUC) values of 0.957 and 0.821, and high sensitivity (71.1% and 84.4%) and specificity (95.5% and 70.5%). After a more than two years follow-up of anti-SSA positive patients, 34.14% of them developed decreased renal function, and 13.58% of them experienced a progression of renal injury with a 23.64% decrease in eGFR. Conclusion Age, urea, chlorine, and anti-SSA were highly associated with renal injury in pSS. Early screening for autoantibodies would be meaningful for evaluation and prevention of renal injury in pSS.

Simple summary Somatic and germline aberrations in homologous recombinant repair (HHR) genes are associated with increased incidence and poor prognosis for prostate cancer. Through next-generation sequencing of prostate cancer patients across all clinical states from north China, here the authors identified a somatic mutational rate of 3% and a germline mutational rate of 3.9% for HRR genes using 200 tumor tissues and 714 blood specimens. Thus, mutational rates in HRR genes were lower compared with previous studies. Background Homologous recombination repair deficiency is associated with higher risk and poorer prognosis for prostate cancer. However, the landscapes of somatic and germline mutations in these genes remain poorly defined in Chinese patients, especially for those with localized disease and those from north part of China. In this study, we explore the genomic profiles of these patients. Methods We performed next-generation sequencing with 200 tumor tissues and 714 blood samples from prostate cancer patients at Peking University First Hospital, using a 32 gene panel including 19 homologous recombination repair genes. Results TP53, PTEN, KRAS were the most common somatic aberrations; BRCA2, NBN, ATM were the most common germline aberrations. In terms of HRR genes, 3% (6/200) patients harbored somatic aberrations, and 3.8% (28/714) patients harbored germline aberrations. 98.0% (196/200) somatic-tested and 72.7% (519/714) germline tested patients underwent prostatectomy, of which 28.6% and 42.0% had Gleason scores ≥8 respectively. Gleason scores at either biopsy or prostatectomy were predictive for somatic aberrations in general and in TP53; while age of onset <60 years old, PSA at diagnosis, and Gleason scores at biopsy were clinical factors associated with positive germline aberrations in BRCA2/ATM. Conclusions Our results showed a distinct genomic profile in homologous recombination repair genes for patients with prostate cancer across all clinical states from north China. Clinicians may consider to expand the prostate cancer patients receiving genetic tests to include more individuals due to the weak guiding role by the clinical factors currently available.

BackgroundMost people are infected with COVID-19 during pandemics at the end of 2022. Older patients were more vulnerable. However, the incidence of secondary bacterial, fungal or viral pulmonary infection and co-infection is not well described in elderly hospitalized COVID-19 patients.MethodsWe retrospectively reviewed the medical records of all elderly (≥65 years) hospitalized patients with laboratory-confirmed COVID-19 from December 1, 2022 to January 31, 2023. Demographics, underlying diseases, treatments, and laboratory data were collected. Univariate and multivariate logistic regression models were used to explore the risk factors associated with secondary bacterial, fungal or viral pulmonary infection and co-infection.ResultsA total of 322 older patients with COVID-19 were enrolled. The incidence of secondary bacterial, fungal or viral pulmonary infection and co-infection was 27.3% (88/322) and 7.5% (24/322), respectively. The overall in-hospital mortality of all patients was 32.9% (106/322), and the in-hospital mortality among patients who acquired with secondary pulmonary infection and co-infection was 57.0% (57/100). A total of 23.9% (77/322) of patients were admitted to ICU within 48 h of hospitalization. The incidence of secondary pulmonary infection and co-infection among patients admitted to the ICU was 50.6% (39/77) and 13.0% (10/77), respectively. The overall in-hospital mortality of ICU patients was 48.1% (37/77), and the in-hospital mortality of ICU patients acquired with secondary pulmonary infection and co-infection was 61.4% (27/44). A total of 83.5% (269/322) of the included patients received empirical antibiotic therapy before positive Clinical Microbiology results. Influenza A virus (the vast majority were the H3N2 subtype) was the most common community acquired pathogen for co-infection. While A. baumannii, K. pneumoniae, and P. aeruginosa were the common hospital acquired pathogens for co-infection and secondary pulmonary infection. The incidence of Carbapenem-resistant Gram-negative bacilli (CR-GNB) infections was high, and the mortality reached 76.9%. Predictors of secondary pulmonary infection and co-infection were ICU admission within 48 h of hospitalization, cerebrovascular diseases, critical COVID-19, and PCT > 0.5 ng/mL.ConclusionThe prognosis for elderly hospitalized COVID-19 patients with secondary pulmonary infection or co-infection is poor. The inflammatory biomarker PCT > 0.5 ng/mL played an important role in the early prediction of secondary pulmonary infection and co-infection in COVID-19 patients.

Background: Aspiration has become an increasingly significant health issue worldwide due to high mortality and morbidity of aspiration-related lung diseases. Aspiration, however, is difficult to diagnose accurately due to lack of effective diagnostic biomarkers. Aims: To explore the diagnostic value of bronchoalveolar lavage fluid (BALF) amylase levels for aspiration. Methods: We conducted a multi-center cross-section study in 6 hospitals from January 2018 to December 2022. Participants were classified into two groups: aspiration group and control group. BALF amylase levels were measured by biochemical analyzer in clinical laboratory. The levels of amylase in BALF were compared by the Wilcoxon test. Results: Of 222 enrolled participants (151 cases and 71 controls) , the mean age was (68.7±13.7) years and (50.8±16.9) years, and there were 109 (72.7%) and 33 (46.5%) males for cases and controls, respectively. The median level of amylase in cases was significantly higher than those without aspiration [6795.6 (IQR, 537.0-8645.3) vs 53.7 (IQR,30.0-55.6) IU/L, P<0.001]. BALF amylase is predictive for diagnosing the patients with aspiration (AUC:0.956) with a high sensitivity (98.6%) and specificity (87.4%). BALF amylase specified at 179.1 IU/L was determined as the optimal threshold for distinguishing aspiration. Subgroup analysis stratified by age and sex showed that the specificity and sensitivity remained above 85.0%. Conclusions: BALF amylase level shows remarkable diagnostic value (cut-off value: 179.1IU/L) and might be a promising biomarker for distinguishing aspiration. This work was supported by National Key R&B Program of China (2020YFC2005401).

Purpose To evaluate the predictive performance of the prostate health index (PHI) and PHI density (PHID), for clinically significant prostate cancer (csPCa) in patients with a PI-RADS score ≤3. Materials and Methods Patients tested for total prostate-specific antigen (tPSA, ≤100 ng/mL), free PSA (fPSA), and p2PSA at Peking University First Hospital were prospectively enrolled. Possible predictive factors of csPCa were analyzed using the receiver operating characteristic (ROC) curve. Results were expressed as area under the curve (AUC) with 95% confidence intervals (CI). The cutoff values of PHI and PHID were determined. Results We enrolled 222 patients in this study. The prevalence of csPCa in the PI-RADS ≤3 subgroup (n=89) was 22.47% (20/89). Age, tPSA, F/T, prostate volume, PSA density, PHI, PHID, and PI-RADS score were significantly associated with csPCa. PHID (AUC: 0.829 [95% CI: 0.717–0.941]) was the best predictor of csPCa. PHID >0.956 was set as the threshold of suspicious csPCa with a sensitivity of 85.00% and a specificity of 73.91%, avoiding 94.44% of unnecessary biopsies but missing 15.00% csPCa. A threshold of PHI ≥52.83 showed the same sensitivity but a rather lower specificity of 65.22% that avoided 93.75% of unnecessary biopsies. Conclusions PHI and PHID have the best predictive performance of csPCa in patients with PI-RADS score ≤3. A threshold value of PHID ≥0.956 may be used as the criterion for biopsy in these patients.

Acute myeloid leukemia is the most common acute leukemia in adults, the barrier of refractory and drug resistance has yet to be conquered in the clinical. Abnormal gene expression and epigenetic changes play an important role in pathogenesis and treatment. A super-enhancer is an epigenetic modifier that promotes pro-tumor genes and drug resistance by activating oncogene transcription. Multi-omics integrative analysis identifies the super-enhancer-associated gene CAPG and its high expression level was correlated with poor prognosis in AML. CAPG is a cytoskeleton protein but has an unclear function in AML. Here we show the molecular function of CAPG in regulating NF-κB signaling pathway by proteomic and epigenomic analysis. Knockdown of Capg in the AML murine model resulted in exhausted AML cells and prolonged survival of AML mice. In conclusion, SEs-associated gene CAPG can contributes to AML progression through NF-κB.

In clinical practice, sera creatinine level is regarded as a crucial biomarker for the diagnosis, staging and monitoring of kidney disease. An amperometric biosensor is rapid, accurate, and cost-effective, with a portability and a simple operation. Herein, we report for the firsttime a disposable, printed amperometric biosensor for the clinical evaluation of creatinine in renal function detection. The sensor is constructed based on Prussian blue/ carbon-graphite paste as the working electrode and the immobilization of creatinine amidohydrolase, creatine amidinohydrolase and sarcosine oxidase. The creatinine biosensor shows a linear detection range from 0.05 to 1.4 mM with a detection time of about 3 min. In addition, the sensor shows a high stability that can maintain above 86% of the initial activity after being stored for over 4 months. Moreover, the sensor shows almost the same results as those with the Jaffe method for measuring the real blood samples. We anticipate that the creatinine biosensor could be widely used in the medical and healthcare areas, especially for at-home testing and onsite medical examinations.

Uric acid is produced via the purine metabolism pathway, and an abnormal level of uric acid could lead to serious diseases and complications, such as gout, renal, and cardiovascular diseases. A rapid, convenient, and accurate detection of uric acid is highly desired for healthcare applications. Here, we report a screen-printed amperometric biosensor for uric acid based on a composite ink consisting of graphene, Prussian blue (PB), and rhodium (Rh)/carbon catalyst. The working electrode is printed by a graphene/PB ink and an Rh/carbon (Rh/C) ink sequentially with drying in between. The linear detection range for uric acid is 0.04–0.8 mM with a response time of 30 s and a detection limit of $0.55 ~\mu \text{M}$ . The biosensor could be stored in a refrigerator at 4 °C for 80 days without a significant decrease in response. Furthermore, compared to the detection results from the clinical standard method, the relative error of the biosensor is within 20% for detecting uric acid in human blood samples. It is anticipated that this work could advance exciting fundamental studies for uric acid-sensing devices, as well as the clinical applications for the detection of uric acid.

Background Recurrent urinary tract infection (RUTI) is common and burdensome in women. Due to the low concentration or slow-growing of uropathogens in RUTI, standard urine cultures (SUCs) are often negative. Next-generation sequencing (NGS) of bacterial 16S rRNA gene is more sensitive and could be used to reveal the differential microbiota between patients with RUTI and asymptomatic controls. Methods Women (aged ≥ 18 years) with clinically diagnosed RUTI with negative SUC and age-matched women asymptomatic controls with normal urinalysis were enrolled. Their midstream voided urine specimens were collected and processed for NGS (Illumina MiSeq) targeting the bacterial 16S rRNA gene V3-V4 region. The dataset was clustered into operational taxonomic units (OTUs) using QIIME. Taxonomic analysis, alpha diversity, beta diversity, multivariate statistical analysis, and linear discriminant analysis effect size (LEfSe) for differential analysis were performed and compared between patients with RUTI and asymptomatic controls. Results A total of 90 patients with RUTI and 62 asymptomatic controls were enrolled in this study. Among them, 74.4% (67/90) and 71.0% (44/62) were successfully amplified and sequenced their bacterial 16S rRNA gene. In the alpha diversity analysis, the chao1 index and observed species index were significantly lower in the RUTI group than in the control group (P = 0.015 and 0.028, respectively). In the beta diversity analysis, there was a significant difference between the 2 groups [Analysis of similarities (ANOSIM), R = 0.209, P = 0.001]. The relative abundance of 36 bacterial taxa was significantly higher, and another 24 kinds of bacteria were significantly lower in the RUTI group compared with the control group [LEfSe analysis, P < 0.05, linear discriminative analysis (LDA) score > 3], suggesting that Ralstonia, Prevotella, Dialister, and Corynebacterium may play an important role in RUTI. Conclusion The urinary microbiota of women with clinically diagnosed RUTI were significantly different from age-matched asymptomatic controls.

Uric acid and creatinine are essential biomarkers for many diseases, such as gout, hyperuricemia, kidney diseases and heart diseases. Electrochemical biosensors are promising candidates for detecting uric acid and creatinine. However, the sensors always suffer from low stability that would limit their practical applications. In this work, we report a new multilayer enzyme matrix to enhance the room-temperature storage stabilities of uric acid and creatinine biosensors significantly. The enzymes are first dropped on the electrode surface directly, then a layer of glutaraldehyde was deposited on the surface of the enzyme layer, and after that, another layer of a polyvinyl alcohol (PVA)/poly(ethylene glycol) (PEG) composite was further placed on the surface of the glutaraldehyde layer, with drying in between. The stabilities of uric acid and creatinine biosensors were enhanced significantly, and the sensors can maintain highly stable sensing performance for over 4 months with a storage at room temperature. It is anticipated that this work could open new avenues for the practical applications of the uric acid and creatinine biosensors.

Systemic lupus erythematosus (SLE) is a heterogeneous autoimmune disease with variable clinical course and laboratory tests. The definition and diagnosis of SLE are still a difficult problem in clinic. Machine learning methods are flexible prediction algorithms with potential advantages. However, very few artificial intelligence-based approaches have been developed to diagnose SLE thus far. The aims of this study were to develop a machine learning approach by Extreme Gradient Boosting (XGBoost) based on the big data to identify SLE in hospitalized patient and to determine whether such model performs better than traditional prediction models.A standard protocol was developed to collect data from laboratory information systems (LIS) and electronic medical record (EMR) in Peaking University First Hospital between June 2008 and March 2019. All the patients with ≥ 1SLE ICD-10 codes were primarily included in this study. A XGBoost algorithm was used to select the important features and construct a diagnostic model. The receiver operating characteristic (ROC) curve, Kolmogorov-Smirnov (KS) curve, accuracy, sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) were used to measure the performance of the model.A total of 2065 SLE patients were included in this study and 28 laboratory tests were selected by the XGBoost algorithm for modeling. According to the variable ranking, prothrombin time, lactate dehydrogenase and immunoglobulin G contribute most to the diagnostic model. The area under curve (AUC) and KS value of the XGBoost model were 0.881 and 0.609. The accuracy, sensitivity, specificity, PPV and NPV of the XGBoost model were 96.4%, 35.3%, 98.7%, 49.7% and 97.7%. Additionally, the diagnostic efficiency of the model based on the 28 features selected by the XGBoost algorithm is better than that based on the 2012 Systemic Lupus International Collaborating Clinics (SLICC) classification criteria. The model performance of XGBoost was better than random forest and logistic regression.Our diagnostic model showed a higher accuracy to identify the SLE patients combined with laboratory tests and EMR. This machine learning model has a potential application in differential diagnosis and pathogenesis investigation of SLE.

BackgroundUrinary tract infections (UTIs) are among the most common infections worldwide. With continuing trends of antibiotic resistance, the etiological distribution and antibiotic susceptibility surveillance are of great importance for empirical antimicrobial therapy. The risk factors and clinical circumstances of UTI among different age categories varied; thus, the pathogens and antimicrobial susceptibilities of UTI may also change with age. The aim of this study was to compare the etiological profiles and antibiotic resistance patterns of UTIs sorted by different age categories from a tertiary general hospital during a 12-year period.MethodsAll positive urine culture results from non-repetitive UTI patients in our hospital from January 2009 to December 2020 were collected retrospectively. The microbial distribution and antibiotic resistance rates were analyzed by WHONET 5.6 software. The etiological profiles sorted by different age categories (newborn, pediatric, adult, and geriatric) and antibiotic resistance rates of the top five pathogens were analyzed.ResultsA total of 13,308 non-repetitive UTI patients were included in our study. Enterococcus faecium was dominant in newborn (45%, n = 105), and replaced by Escherichia coli in pediatric (34%, n = 362), adult (43%, n = 3,416), and geriatric (40%, n = 1,617), respectively. The etiological profiles of different age categories were divergent, sorted by genders (male and female) and ward types (outpatient, inpatient, ICU, and emergency). E. coli, Klebsiella pneumoniae, Enterococcus faecalis, E. faecium, and Pseudomonas aeruginosa were the top five pathogens in all age categories. The resistance rates of cefoperazone–sulbactam and piperacillin–tazobactam in E. coli were low in all age categories. The resistance rates of other cephalosporins, carbapenems, and fluoroqinolones in K. pneumoniae were higher in geriatric patients overall. E. faecium was more resistant than E. faecalis in all age categories. Multidrug resistance increased with age, which was more serious in geriatric patients.ConclusionThe UTI etiological profiles and antibiotic resistance patterns varied among different age categories, especially in pediatric and geriatric patients; thus, a different antibiotic therapy for various age categories should be considered when initiating empirical antimicrobial therapies.

Separator gels in blood collection tubes are used to separate serum from clotted whole blood or plasma from cells. Here we present a case of a patient with a contradictory phenomenon between the serum separator tube and the plasma tube. The serum separator tube showed mixed serum and separator gel and distinctly less serum. However, the plasma tube showed fewer cells. Laboratory study revealed an IgG level of 78.9 g/L. Serum immunofixation electrophoresis analysis identified the abnormal pattern as a dense IgG band with a corresponding dense light chain band of λ. Bone marrow smear showed 53% proplasmacytes. The patient was diagnosed with multiple myeloma. The marked hyperproteinemia, especially hyperimmunoglobulinemia, may have resulted in the density alteration of serum that was mixed or located above the separator gel. This phenomenon is also seen in patients injected with iodinated radiologic contrast media such as iohexol and in patients on hemodialysis with a concentrated sodium citrate solution.