Clinical laboratory最新文献

Background: This study aimed to investigate the protective effects of Dl-3-n-butylphthalide (NBP) on sepsis-associated acute kidney injury (SA-AKI) and its underlying mechanisms.

Methods: A total of 32 mice were randomly divided into 4 groups (n = 8 per group): the control group (control group), lipopolysaccharide treatment group (LPS group), Dl-3-n-butylphthalide group (LPS + NBP group), and ferrostatin-1 group (LPS + Fer-1 group). Renal function was assessed by measuring serum creatinine (SCr) and blood urea nitrogen (BUN) levels. Oxidative stress markers, including malondialdehyde (MDA), superoxide dis-mutase (SOD), total antioxidant capacity (T-AOC), and glutathione (GSH), were quantified in renal tissues. Histopathological evaluation of renal injury was performed using hematoxylin and eosin (HE) staining. The protein expression levels of nuclear factor erythroid 2-related factor 2 (Nrf2), heme oxygenase-1 (HO-1), solute carrier family 7 member 11 (SLC7A11), and glutathione peroxidase 4 (GPX4) were analyzed using immunohistochemistry and western blot.

Results: Compared with the control group, the LPS group exhibited significantly higher levels of SCr and BUN, decreased renal tissue levels of SOD, T-AOC, and GSH, as well as increased MDA levels and renal injury scores (p < 0.05). Furthermore, the LPS group exhibited significant downregulation of Nrf2, HO-1, GPX4, and SLC7A11 protein expression (p < 0.05). Compared with the LPS group, drug intervention significantly reversed the above-mentioned changes in both the LPS + NBP and LPS + Fer-1 groups (p < 0.05).

Conclusions: NBP can attenuate SA-AKI by activating the Nrf2/HO-1 pathway.

Background: Light-transmission aggregometry is the gold standard for assessing platelet function. The scoring system, designed based on the results obtained using two different concentrations of agonists on semi-automated analyzers, is commonly used to confirm the effects of antiplatelet drugs in Japan. Given the time-intensive and laborious nature of LTA, along with the lack of standardization across laboratories and devices, automated and consistent methods to monitor platelet function are imperative. Recently, we developed a new parameter and equipped an automated coagulation analyzer with it. In this study, a new parameter, "collagen-induced platelet aggregation level (CPAL)," was developed, and its basic performance was evaluated and compared with the maximum aggregation rate of 1.0 mM arachidonic acid (AA-MA) and the result of the VerifyNow aspirin assay, ex-pressed in aspirin reaction units (ARU), performed on patients on antiplatelet therapy.

Methods: An automated coagulation analyzer was equipped with CPAL. CPAL is calculated as a score from 0.0 to 10.0 based on platelet aggregation patterns with 1.0 and 5.0 µg/mL collagen. Within-run precision was calculated by conducting five replicate analyses of the platelet-rich plasma (PRP) from healthy volunteers and 1.0 mM aspirin-spiked PRP. The dose-response effect of aspirin was evaluated using several concentrations of aspirin and PRP obtained from healthy volunteers. A comparative study was conducted using 62 PRP samples obtained from patients receiving antiplatelet therapy.

Results: The coefficient of variation in within-run precision was within 5% for CPAL. Aspirin treatment affected CPAL expression in a concentration-dependent manner. A significant correlation was observed between CPAL and AA-MA (r = 0.70, p < 0.001). However, a very weak or no correlation was observed between CPAL and ARU (r = 0.17, p = 0.179).

Conclusions: CPAL exhibits acceptable performance. It showed good correlation with AA-MA but not with the ARU of VerifyNow, which changed with slight differences in aspirin concentration. CPAL is a new platelet aggregation scoring system that may be used to monitor the effects of aspirin using an automated coagulation analyzer.

Background: Recent evidence suggests a significant connection between acute pancreatitis and acute lung injury. This study employs bibliometric methods to visually analyze research papers on these topics from the last two decades, establishing a scientific basis for future research directions and key issues in the field.

Methods: The paper on acute pancreatitis and acute lung injury from 2004 to 2024 was sourced from the SCI-Expanded of Web of Science. Only English articles were analyzed, using VOSviewer and CiteSpace for data visualization.

Results: A total of 257 publications underwent analysis. Dalian Medical University was recognized as the top institution based on the number of publications, citation counts, and H-index scores. The most productive author identified was Chen HL, while the journal featuring the highest volume of publications was the World Journal of Gastroenterology. Key terms that appeared most frequently included "acute lung injury," "acute pancreatitis," "severe acute pancreatitis," and "inflammation".

Conclusions: The research highlights ARDS, mortality rates, mechanisms, Atlanta classification, organ failure, and protective measures as central themes in ongoing studies. These insights are crucial for upcoming research. Nonetheless, investigating the causes of lung injury due to acute pancreatitis remains in its preliminary stages and necessitates further exploration.

Background: Colorectal cancer (CRC) is associated with a high mortality rate. Previous studies have shown that FOXQ1, MMP11, and CST1 play significant roles in various cancers, influencing the invasion and metastasis of tumors. However, their effects on colorectal cancer have not been fully investigated. The purpose of this research was to examine the expression of FOXQ1, MMP11, and CST1 in colorectal cancer (CRC) and to systematically as-sess how these factors relate to clinicopathological characteristics and patient survival outcomes.

Methods: This study retrospectively gathered paraffin-embedded samples from 110 CRC patients who underwent surgery between 2017 and 2018. Meanwhile, relevant data were obtained from public databases to analyze expression differences of FOXQ1, MMP11, and CST1 between tumor tissues and normal lung tissues. We examined the expression of FOXQ1, MMP11, and CST1 using immunohistochemistry. Furthermore, the associations among FOXQ1, MMP11, CST1, clinical-pathological parameters, and prognosis were systematically analyzed. Further verification of the in vitro results was conducted through qRT-PCR.

Results: Expression of FOXQ1, MMP11, and CST1 in patients was high, with 83.6%, 67%, and 74.5%, respectively. Through rigorous quantitative analysis of clinical-pathological parameters, the study confirmed that these biomarkers have a close and clinically significant correlation with the progression of TNM staging and the occurrence of lymph node metastasis (p < 0.05). Bioinformatics analysis and qRT-PCR verification both indicated that the expression levels of FOXQ1, MMP11, and CST1 in colorectal cancer (CRC) tissues were significantly higher than those in adjacent non-cancerous tissues.

Conclusions: The research data indicate that the abnormal overexpression of FOXQ1, MMP11, and CST1 in CRC tissues is significantly correlated with poor clinical prognosis in patients. There may be a synergistic effect influencing the invasion and metastasis of tumor cells, positioning them as potential novel therapeutic targets for patients with CRC.

Background: Multiple myeloma is a plasma cell malignancy characterized by monoclonal immunoglobulin production. Daratumumab has improved therapeutic outcomes but can interfere with laboratory assessments.

Methods: A 73-year-old woman with IgG kappa multiple myeloma achieved remission after initial treatment, then relapsed and received DRD. A monoclonal IgG kappa spike was observed on follow-up SPEP and IFE.

Results: Daratumumab, due to its IgG1 kappa structure, may mimic disease-related monoclonal proteins, potentially leading to false detection of residual disease and misclassification of complete response as very good partial response.

Conclusions: Recognizing such interference and ensuring strong clinician-biologist collaboration is essential for accurate response interpretation.

Background: Artificial intelligence (AI), particularly deep learning (DL), is transforming parasitic disease diagnosis by addressing challenges in accuracy and accessibility. Convolutional neural networks (CNNs) and machine learning (ML) offer rapid detection of pathogens causing malaria, leishmaniasis, and schistosomiasis, promising significant advancements in global health.

Methods: This letter reviewed AI applications, focusing on CNNs and ML for detecting parasitic pathogens in clinical samples, imaging, and epidemiological data. The analysis highlights model efficacy, challenges such as data variability and bias, and the potential of AI integration with portable diagnostics in resource-constrained settings.

Results: AI-driven diagnostics demonstrate superior sensitivity and specificity in identifying malaria, leishmaniasis, and schistosomiasis compared to conventional methods. However, data heterogeneity and algorithmic bias pose challenges. Combining AI with portable tools shows potential for improving diagnosis in endemic regions.

Conclusions: AI, particularly DL, holds transformative potential for parasitic disease diagnosis. Overcoming data and bias challenges is essential for ethical and equitable implementation. Collaborative efforts to integrate AI with portable diagnostics can enhance global health outcomes in endemic areas.

Background: Acute promyelocytic leukemia (APL) is a subtype of acute myeloid leukemia characterized by the t(15;17)(q22;q21) translocation. Although it typically responds well to therapy, certain genetic aberrations, including ider(17)(q10)t(15;17)(q22;q21) and FLT3-ITD mutations, have unclear prognostic implications.

Methods: A 61-year-old female patient presented with dizziness and persistent bruising. Laboratory and imaging studies revealed coagulopathy and intracranial hemorrhage. Morphological, immunophenotypic, cytogenetic, molecular, and FISH analyses confirmed APL with both ider(17)(q10)t(15;17)(q22;q21) and FLT3-ITD mutation.

Results: Despite standard ATRA and idarubicin induction therapy, there was no improvement in leukemic burden, and the patient succumbed to worsening hemorrhage one week after emergency surgery.

Conclusions: This case of APL with coexisting ider(17) and FLT3-ITD mutations exhibited an aggressive course and resistance to standard treatment. These findings suggest that such patients may require intensified therapeutic strategies and closer monitoring.

Background: Matrix-assisted laser desorption-ionization-time of flight mass spectrometry (MALDI-TOF) has been widely used in clinical microbiology laboratories as a rapid and reliable tool for pathogen identification. The aim of this study was to evaluate the diagnostic performance of the newly-developed Autof MS1000 in comparison with the Vitek MS.

Methods: A total of 578 clinical isolates consisting of 136 Enterobacterales, 76 non-fermenting Gram-negative bacilli, 17 other Gram-negative bacilli, 240 Gram-positive cocci, 30 Gram-positive bacilli, 52 anaerobic bacteria, and 27 yeasts were tested simultaneously by the two systems. The direct smear method was performed for bacteria and the formic acid extraction method for yeasts. Discrepant results were confirmed by 16S rRNA or ITS region sequencing.

Results: The Autof MS1000 and Vitek MS identified 93.3% and 95.5% of the strains at the species level, respectively. Three isolates (0.3%) yielded "no identification" results with Vitek MS, and no "unreliable" results (0%) were obtained with Autof MS1000. The Autof MS1000 and Vitek MS misidentified 1.5% and 1.4% of the isolates, respectively. Overall, there was significant agreement between the two systems (p < 0.001). In terms of identification times, the Autof MS1000 was approximately three times faster than the Vitek MS.

Conclusions: Our results demonstrate that the Autof MS1000 provides comparable results to the Vitek MS and can be used for the rapid identification of microorganisms. Furthermore, this study highlights the need for any MALDITOF MS system to implement regular database expansion for the identification of rarely encountered microorganisms.

Background: Gestational diabetes mellitus (GDM) affects millions of people worldwide. Patients often turn to the internet and artificial intelligence (AI)-based conversational models for information. The CLEAR tool evaluates the quality of health-related content produced by AI-based models. This study assessed the responses provided by medical guidelines, ChatGPT, and Google Bard to the ten most frequently asked online questions about GDM, uti-lizing the CLEAR tool for evaluation.

Methods: The most common online questions about GDM were identified using Google Trends, and the top 10 questions were selected. Answers were then gathered from two experienced physicians, ChatGPT 4.0o-mini, and Google Bard, with responses categorized into 'Guide,' 'ChatGPT,' and 'Bard' groups. Answers from the AI models were obtained using two computers and two separate sessions to ensure consistency and minimize bias.

Results: ChatGPT received higher scores than the medical guidelines, while Bard scored lower than ChatGPT. The medical guidelines provided more accessible answers for the general audience, while ChatGPT and Bard required higher literacy levels. Good reliability (0.781) was observed between the two reviewers. Regarding readability, the medical guidelines were the easiest to read, while Bard provided the most challenging text.

Conclusions: ChatGPT and Google Bard perform well in content completeness and relevance but face challenges in readability and misinformation. Future research should improve accuracy and readability, integrate AI with peer-reviewed sources, and ensure healthcare professionals guide patients to reliable AI information.

Background: This study aimed to analyze the effect of different storage conditions and different detection time on the results of programmed cell death protein 1 (PD-1) in clinical peripheral blood samples by flow cytometry (FCM) after the completion of all procedure steps.

Methods: 68 inpatients were randomly selected at the First Affiliated Hospital of Anhui Medical University in March 2025, and 68 peripheral blood specimens were collected. After all laboratory procedure steps were completed, cell suspension in PBS was prepared and stored both at 4℃ and at room temperature away from light, which were then divided into four different detection time groups: 0 hours, 4 hours, 8 hours, and 24 hours groups. The percentage results of seven cell populations, CD45+ Lym cell population (CD45+ Lym cells), CD3+ T cell popu-lation (CD3+ T cells), CD3+ CD4+ T cell population (CD3+ CD4+ T cells), CD3+ CD8+ T cell population (CD3+ CD8+ T cells), CD3+PD-1+ T cell population (CD3+ PD-1+ T cells), CD3+ CD4+ PD-1+ T cell population (CD3+ CD4+ PD-1+ T cells), and CD3+ CD8+ PD-1+ T cell population (CD3+ CD8+ PD-1+ T cells), were detected using FCM. Statistical software was used to compare and analyze the results.

Results: When stored at 4℃ away from light, compared with 0 hours group, the results of seven cell populations in the 4 hours and 8 hours groups did not change significantly and the results of the 24 hours group decreased significantly (p < 0.05). At room temperature away from light, the results of seven cell populations had no significant difference between the 0 hours and 4 hours groups. The results of seven cell populations in the 8 hours and 24 hours groups decreased significantly, with significant difference (p < 0.05).

Conclusions: The detection of PD-1 should not be carried out too long after all of the laboratory procedure steps have been completed. If samples are stored at 4℃ away from light, the detection time should be limited to 8 hours; if stored at room temperature away from light, the detection time should be limited to 4 hours. This is to ensure the accuracy and stability of the detection results.