Exosomes derived from umbilical cord-derived mesenchymal stem cells exposed to diabetic microenvironment enhance M2 macrophage polarization and protect against diabetic nephropathy.

The role of mesenchymal-stem-cell-derived exosomes (MSCs-Exo) in the regulation of macrophage polarization has been recognized in several diseases. There is emerging evidence that MSCs-Exo partially prevent the progression of diabetic nephropathy (DN). This study aimed to investigate whether exosomes secreted by MSCs pre-treated with a diabetic environment (Exo-pre) have a more pronounced protective effect against DN by regulating the balance of macrophages. Exo-pre and Exo-Con were isolated from the culture medium of UC-MSCs pre-treated with a diabetic mimic environment and natural UC-MSCs, respectively. Exo-pre and Exo-Con were injected into the tail veins of db/db mice three times a week for 6 weeks. Serum creatinine and serum urea nitrogen levels, the urinary protein/creatinine ratio, and histological staining were used to determine renal function and morphology. Macrophage phenotypes were analyzed by immunofluorescence, western blotting, and quantitative reverse transcription polymerase chain reaction. In vitro, lipopolysaccharide-induced M1 macrophages were incubated separately with Exo-Con and Exo-pre. We performed microRNA (miRNA) sequencing to identify candidate miRNAs and predict their target genes. An miRNA inhibitor was used to confirm the role of miRNAs in macrophage modulation. Exo-pre were more potent than Exo-Con at alleviating DN. Exo-pre administration significantly reduced the number of M1 macrophages and increased the number of M2 macrophages in the kidney compared to Exo-Con administration. Parallel outcomes were observed in the co-culture experiments. Moreover, miR-486-5p was distinctly expressed in Exo-Con and Exo-pre groups, and it played an important role in macrophage polarization by targeting PIK3R1 through the PI3K/Akt pathway. Reducing miR-486-5p levels in Exo-pre abolished macrophage polarization modulation. Exo-pre administration exhibited a superior effect on DN by remodeling the macrophage balance by shuttling miR-486-5p, which targets PIK3R1.

Construction and validation of a nomogram prediction model for the progression to septic shock in elderly patients with urosepsis.

Background: Septic shock is a clinical syndrome characterized by the progression of sepsis to a severe stage. Elderly patients with urosepsis in the intensive care unit (ICU) are more likely to progress to septic shock. This study aimed to establish and validate a nomogram model for predicting the risk of progression to septic shock in elderly patients with urosepsis. Methods: We extracted data from the Medical Information Mart for Intensive Care (MIMIC-IV) and the eICU Collaborative Research Database (eICU-CRD). The MIMIC-IV dataset was split into a training set for model development and an internal validation set to assess model performance. Further external validation was performed using a distinct dataset sourced from the eICU-CRD. Predictors were screened using least absolute shrinkage and selection operator (LASSO) regression and multivariable logistic regression analyses. The evaluation of model performance included discrimination, calibration, and clinical usefulness. Results: The study demonstrated that the Glasgow Coma Scale (GCS), white blood count (WBC), platelet, blood urea nitrogen (BUN), calcium, albumin, congestive heart failure (CHF), and invasive ventilation were closely associated with septic shock in the training cohort. Nomogram prediction, utilizing eight parameters, demonstrated strong predictive accuracy with area under the curve (AUC) values of 0.809 (95 % CI 0.786-0.834), 0.794 (95 % CI 0.756-0.831), and 0.723 (95 % CI 0.647-0.801) in the training, internal validation, and external validation sets, respectively. Additionally, the nomogram demonstrated a promising calibration performance and significant clinical usefulness in both the training and validation sets. Conclusion: The constructed nomogram is a reliable and practical tool for predicting the risk of progression to septic shock in elderly patients with urosepsis. Its implementation in clinical practice may enhance the early identification of high-risk patients, facilitate timely and targeted interventions to mitigate the risk of septic shock, and improve patient outcomes.

Cutaneous amyloidosis mimicking basal cell carcinoma: a case series and literature review.

BACKGROUND: Amyloidosis is characterized by extracellular amyloid protein deposition. When amyloidosis intersects with basal cell carcinoma (BCC), it introduces complex diagnostic challenges. This study explored the overlap between primary localized cutaneous amyloidosis (PLCA) and BCC, examining amyloid deposits in BCC, systemic amyloidosis risk in PLCA, and various treatment methods. METHODS: Two case studies were discussed, followed by a literature review, in which PubMed, Web of Science, EMBASE, and the Cochrane Library databases were utilized. The search, covering studies from infinity up to January 2024, focused on "cutaneous amyloidosis," "basal cell carcinoma," and related terms. Articles in English detailing the clinical presentation, diagnostic methods, treatment, and outcomes of cutaneous amyloidosis mimicking BCC were included. Data extraction and synthesis were performed by two independent reviewers. CASE SERIES: This study highlighted two cases exemplifying the complexity of diagnosing BCC and PLCA. The first case (a 64-year-old with a nodule on the cheek) and the second (a 67-year-old with a nodular lesion on the upper lip cheek) were initially suspected as BCC and were later identified as PLCA upon histopathological examination. DISCUSSION: The diagnosis of amyloidosis within BCC nodules remains a diagnostic challenge. Although their coexistence is relatively prevalent, their local recurrence rates remain debatable. Various diagnostic and therapeutic approaches have been suggested, such as topical creams and phototherapy. However, none have garnered conclusive and consistent evidence to establish reliable clinical application. CONCLUSION: The findings emphasized the importance of considering alternative pathologies in differential diagnoses. Future research should focus on understanding systemic amyloidosis risks and optimizing care for both conditions.

Association between admission pan-immune-inflammation value and short-term mortality in septic patients: a retrospective cohort study.

Pan-Immune-Inflammation Value (PIV) has recently received more attention as a novel indicator of inflammation. We aimed to evaluate the association between PIV and prognosis in septic patients. Data were extracted from the Medical Information Mart for Intensive Care IV database. The primary and secondary outcomes were 28-day and 90-day mortality. The association between PIV and outcomes was assessed by Kaplan-Meier curves, Cox regression analysis, restricted cubic spline curves and subgroup analysis. A total of 11,331 septic patients were included. Kaplan-Meier curves showed that septic patients with higher PIV had lower 28-day survival rate. In multivariable Cox regression analysis, log2-PIV was positively associated with the risk of 28-day mortality [HR (95% CI) 1.06 (1.03, 1.09), P < 0.001]. The relationship between log2-PIV and 28-day mortality was non-linear with a predicted inflection point at 8. To the right of the inflection point, high log2-PIV was associated with an increased 28-day mortality risk [HR (95% CI) 1.13 (1.09, 1.18), P < 0.001]. However, to the left of this point, this association was non-significant [HR (95% CI) 1.01 (0.94, 1.08), P = 0.791]. Similar results were found for 90-day mortality. Our study showed a non-linear relationship between PIV and 28-day and 90-day mortality risk in septic patients.

Association between advanced lung cancer inflammation index and in-hospital mortality in ICU patients with community-acquired pneumonia: A retrospective analysis of the MIMIC-IV database.

Objective: The objective of the present study was to explore the correlation between the advanced lung cancer inflammation index (ALI) and in-hospital mortality among patients diagnosed with community-acquired pneumonia (CAP). Methods: Data from the Medical Information Mart for Intensive Care-IV database were adopted to analyze the in-hospital mortality of ICU patients with CAP. Upon admission to the ICU, fundamental data including vital signs, critical illness scores, comorbidities, and laboratory results, were collected. The in-hospital mortality of all CAP patients was documented. Multivariate logistic regression (MLR) models and restricted cubic spline (RCS) analysis together with subgroup analyses were conducted. Results: This study includes 311 CAP individuals, involving 218 survivors as well as 93 nonsurvivors. The participants had an average age of 63.57 years, and the females accounted for approximately 45.33%. The in-hospital mortality was documented to be 29.90%. MLR analysis found that ALI was identified as an independent predictor for in-hospital mortality among patients with CAP solely in the Q1 group with ALI ≤ 39.38 (HR: 2.227, 95% CI: 1.026-4.831, P = 0.043). RCS analysis showed a nonlinear relationship between the ALI and in-hospital mortality, with a turning point at 81, and on the left side of the inflection point, a negative correlation was observed between ALI and in-hospital mortality (HR: 0.984, 95% CI: 0.975-0.994, P = 0.002). The subgroup with high blood pressure showed significant interaction with the ALI. Conclusion: The present study demonstrated a nonlinear correlation of the ALI with in-hospital mortality among individuals with CAP. Additional confirmation of these findings requires conducting larger prospective investigations.

Association between frailty index based on laboratory tests and all-cause mortality in critically ill patients with heart failure.

BACKGROUND: The frailty index based on laboratory tests (FI-lab) can identify individuals at increased risk for adverse health outcomes. The association between the FI-lab and all-cause mortality in patients with heart failure (HF) in the intensive care unit (ICU) remains unknown. This study aimed to determine the correlation between FI-lab and all-cause mortality to evaluate the impact of FI-lab on the prognosis of critically ill patients with HF. METHODS: This retrospective observational study utilized data extracted from the Medical Information Mart for Intensive Care IV database. The FI-lab, which consists of 33 laboratory tests, was constructed. Patients were then grouped into quartiles (Q1-Q4) based on their FI-lab scores. Kaplan-Meier analysis was used to compare all-cause mortality among the four groups. A Cox proportional hazard analysis was conducted to examine the association between the FI-lab score and all-cause mortality. The incremental predictive value of adding FI-lab to classical disease severity scores was assessed using Harrell's C statistic, integrated discrimination improvement (IDI) and net reclassification improvement (NRI). RESULTS: Among 3021 patients, 838 (27.74%) died within 28 days, and 1400 (46.34%) died within a 360 day follow-up period. Kaplan-Meier analysis indicated that patients with higher FI-lab scores had significantly higher risks of all-cause mortality (log-rank P < 0.001). Multivariable Cox regression suggested that FI-lab, evaluated as a continuous variable (for each 0.01 increase), was associated with increased 28 day mortality [hazard ratio (HR) 1.02, 95% confidence interval (CI) (1.01-1.03), P < 0.001] and 360 day mortality [HR 1.02, 95% CI (1.01-1.02), P < 0.001]. When assessed in quartiles, the 28 day mortality risk [HR 1.66, 95% CI (1.28-2.15), P < 0.001] and 360 day mortality risk [HR 1.48, 95% CI (1.23-1.8), P < 0.001] were significantly higher for FI-lab Q4 compared with FI-lab Q1. FI-lab significantly improved the predictive capability of classical disease severity scores for 28 and 360 day mortality. CONCLUSIONS: In ICU patients diagnosed with HF, the FI-lab is a potent predictor of short-term and long-term mortality in critically ill patients with HF. The active use of FI-lab to identify high-risk groups among critically ill HF patients and initiate timely interventions may have significant value in improving the prognosis of critically ill patients with HF.

Early versus delayed enteral nutrition in ICU patients with sepsis: a propensity score-matched analysis based on the MIMIC-IV database.

Background: Early enteral nutrition (EN) is recommended for sepsis management, but its optimal timing and clinical benefits remain uncertain. This study evaluates whether early EN improves outcomes compared to delayed EN in patients with sepsis. Methods: We analyzed data of septic patients from the MIMIC-IV 2.2 database, focusing on those in the Medical Intensive Care Unit (MICU) and Surgical Intensive Care Unit (SICU). Patients who initiated EN within 3 days were classified into the early EN group, while those who started EN between 3 and 7 days were classified into the delayed EN group. Propensity score matching was used to compare outcomes between the groups. Results: Among 1,111 patients, 786 (70.7%) were in the early EN group and 325 (29.3%) were in the delayed EN group. Before propensity score matching, the early EN group demonstrated lower mortality (crude OR = 0.694; 95% CI: 0.514-0.936; p = 0.018) and shorter ICU stays (8.3 [5.2, 12.3] vs. 10.0 [7.5, 14.2] days; p < 0.001). After matching, no significant difference in mortality was observed. However, the early EN group had shorter ICU stays (8.3 [5.2, 12.4] vs. 10.1 [7.5, 14.2] days; p < 0.001) and a lower incidence of AKI stage 3 (49.3% vs. 55.5%; p = 0.030). Subgroup analysis revealed that early EN significantly reduced the 28-day mortality rate in sepsis patients with lactate levels ≤4 mmol/L, with an adjusted odds ratio (aOR) of 0.579 (95% CI: 0.361, 0.930; p = 0.024). Conclusion: Early enteral nutrition may not significantly reduce overall mortality in sepsis patients but may shorten ICU stays and decrease the incidence of AKI stage 3. Further research is needed to identify specific patient characteristics that benefit most from early EN.

Distinguishing Cerebral Infarction With Neck Pain and Hemiparesis From Cervical Spinal Epidural Hematoma Without MRI: A Case Report.

In patients presenting neck pain and hemiparesis, differentiation between cerebral infarction and cervical spinal epidural hematoma is vital yet challenging, particularly when magnetic resonance imaging (MRI) is not feasible. A 59-year-old woman presented with a sudden onset of left-sided hemiparesis and neck pain. MRI was contraindicated because the patient underwent embolization in childhood. Head computed tomography (CT) revealed no evidence of hemorrhage or early ischemic signs. Cervical CT revealed no evidence of hematoma within the spinal canal. Myelography and CT myelography revealed no significant cervical spine abnormalities. The diagnosis was cerebral infarction. Cervical spine MRI is the gold standard examination for diagnosing cervical spinal epidural hematoma, but cervical spine CT, myelography, and CT myelography may be useful when MRI is contraindicated.

The prognostic value of the combined neutrophil-to-lymphocyte ratio (NLR) and neutrophil-to-platelet ratio (NPR) in sepsis.

Sepsis is a severe disease characterized by high mortality rates. Our aim was to develop an early prognostic indicator of adverse outcomes in sepsis, utilizing easily accessible routine blood tests. A retrospective analysis of sepsis patients from the MIMIC-IV database was conducted. We performed univariate and multivariate regression analyses to identify independent risk factors associated with in-hospital mortality within 28 days. Logistic regression was utilized to combine the neutrophil-to-lymphocyte ratio (NLR) and the neutrophil-to-platelet ratio (NPR) into a composite score, denoted as NLR_NPR. We used ROC curves to compare the prognostic performance of the models and Kaplan-Meier survival curves to assess the 28 day survival rate. Subgroup analysis was performed to evaluate the applicability of NLR_NPR in different subpopulations based on specific characteristics. This study included a total of 1263 sepsis patients, of whom 179 died within 28 days of hospitalization, while 1084 survived beyond 28 days. Multivariate regression analysis identified age, respiratory rate, neutrophil-to-lymphocyte ratio (NLR), neutrophil-to-platelet ratio (NPR), hypertension, and sequential organ failure assessment (SOFA) score as independent risk factors for 28 day mortality in septic patients (P < 0.05). Additionally, in the prediction model based on blood cell-related parameters, the combined NLR_NPR score exhibited the highest predictive value for 28 day mortality (AUC = 0.6666), followed by NLR (AUC = 0.6456) and NPR (AUC = 0.6284). Importantly, the performance of the NLR_NPR score was superior to that of the commonly used SOFA score (AUC = 0.5613). Subgroup analysis showed that NLR_NPR remained an independent risk factor for 28 day in-hospital mortality in the subgroups of age, respiratory rate, and SOFA, although not in the hypertension subgroup. The combined use of NLR and NPR from routine blood tests represents a readily available and reliable predictive marker for 28 day mortality in sepsis patients. These results imply that clinicians should prioritize patients with higher NLR_NPR scores for closer monitoring to reduce mortality rates.

Predictive Model for Extended-Spectrum ß-Lactamase-Producing Bacterial Infections Using Natural Language Processing Technique and Open Data in Intensive Care Unit Environment: Retrospective Observational Study.

BACKGROUND: Machine learning has advanced medical event prediction, mostly using private data. The public MIMIC-3 (Medical Information Mart for Intensive Care III) data set, which contains detailed data on over 40,000 intensive care unit patients, stands out as it can help develop better models including structured and textual data. OBJECTIVE: This study aimed to build and test a machine learning model using the MIMIC-3 data set to determine the effectiveness of information extracted from electronic medical record text using a named entity recognition, specifically QuickUMLS, for predicting important medical events. Using the prediction of extended-spectrum ß-lactamase (ESBL)-producing bacterial infections as an example, this study shows how open data sources and simple technology can be useful for making clinically meaningful predictions. METHODS: The MIMIC-3 data set, including demographics, vital signs, laboratory results, and textual data, such as discharge summaries, was used. This study specifically targeted patients diagnosed with Klebsiella pneumoniae or Escherichia coli infection. Predictions were based on ESBL-producing bacterial standards and the minimum inhibitory concentration criteria. Both the structured data and extracted patient histories were used as predictors. In total, 2 models, an L1-regularized logistic regression model and a LightGBM model, were evaluated using the receiver operating characteristic area under the curve (ROC-AUC) and the precision-recall curve area under the curve (PR-AUC). RESULTS: Of 46,520 MIMIC-3 patients, 4046 were identified with bacterial cultures, indicating the presence of K pneumoniae or E coli. After excluding patients who lacked discharge summary text, 3614 patients remained. The L1-penalized model, with variables from only the structured data, displayed a ROC-AUC of 0.646 and a PR-AUC of 0.307. The LightGBM model, combining structured and textual data, achieved a ROC-AUC of 0.707 and a PR-AUC of 0.369. Key contributors to the LightGBM model included patient age, duration since hospital admission, and specific medical history such as diabetes. The structured data-based model showed improved performance compared to the reference models. Performance was further improved when textual medical history was included. Compared to other models predicting drug-resistant bacteria, the results of this study ranked in the middle. Some misidentifications, potentially due to the limitations of QuickUMLS, may have affected the accuracy of the model. CONCLUSIONS: This study successfully developed a predictive model for ESBL-producing bacterial infections using the MIMIC-3 data set, yielding results consistent with existing literature. This model stands out for its transparency and reliance on open data and open-named entity recognition technology. The performance of the model was enhanced using textual information. With advancements in natural language processing tools such as BERT and GPT, the extraction of medical data from text holds substantial potential for future model optimization.

Normalized lactate load as an independent prognostic indicator in patients with cardiogenic shock.

BACKGROUND: Early prognosis evaluation is crucial for decision-making in cardiogenic shock (CS) patients. Dynamic lactate assessment, for example, normalized lactate load, has been a better prognosis predictor than single lactate value in septic shock. Our objective was to investigate the correlation between normalized lactate load and in-hospital mortality in patients with CS. METHODS: Data were extracted from the Medical Information Mart for Intensive Care (MIMIC)-IV database. The calculation of lactate load involved the determination of the cumulative area under the lactate curve, while normalized lactate load was computed by dividing the lactate load by the corresponding period. Receiver Operating Characteristic (ROC) curves were constructed, and the evaluation of areas under the curves (AUC) for various parameters was performed using the DeLong test. RESULTS: Our study involved a cohort of 1932 CS patients, with 687 individuals (36.1%) experiencing mortality during their hospitalization. The AUC for normalized lactate load demonstrated significant superiority compared to the first lactate (0.675 vs. 0.646, P < 0.001), maximum lactate (0.675 vs. 0.651, P < 0.001), and mean lactate (0.675 vs. 0.669, P = 0.003). Notably, the AUC for normalized lactate load showed comparability to that of the Sequential Organ Failure Assessment (SOFA) score (0.675 vs. 0.695, P = 0.175). CONCLUSION: The normalized lactate load was an independently associated with the in-hospital mortality among CS patients.

miRNAs: possible regulators of toll like receptors and inflammatory tumor microenvironment in colorectal cancer.

BACKGROUND: Colorectal cancer (CRC) is ranked as the third most commonly diagnosed cancer and the third cause of cancer related deaths. CRC is greatly attributed to genetic and epigenetic mutations and immune dysregulation. Tumor aberrant expression of Toll-like Receptors (TLRs) can contribute to tumorigenesis. Recent studies suggested that microRNAs act as direct ligands of TLRs altering their expression and signaling pathways. AIM: To prove our concept that specific miRNA mimics may act as antagonists of their specific toll like receptors inhibiting their expression that could limit the release of pro-inflammatory and pro-tumorigenic cytokines leading to apoptosis of tumor cells. METHODS: From public microarray databases, we retrieved TLRs and miRNAs related to CRC followed by in silico docking of the selected miRNA ligands into the TLRs. Clinical validation after co-immunoprecipitation of TLRs and their interacting miRNA ligands was done. Expression of TLRs 1, 7,8 was determined by ELISA while miRNAs was measured by RT-qPCR. In addition, microRNA mimics of the down regulated miRNAs were transfected into human CRC cell lines. RESULTS: Our data demonstrate that TLRs 1, 7, 8 are up regulated in CRC compared to controls. Further, three miRNAs (-122, -29b and -15b) are relatively downregulated, while 4 miRNAs (-202, miRNA-98, -21 and -let7i) are upregulated in CRC patients compared to those with benign tumor and healthy controls. Transfection of down regulated miRNA mimics into CRC cell lines resulted in a significant reduction of the number and viability of cells as well as down regulating the expression of TLRs 1, 7 and 8 with ultimate reduction of downstream effector IL6 protein, suggesting that these miRNAs are negative regulators of carcinogenesis. CONCLUSION: MicroRNAs could act as antagonistic ligands of TLRs limiting the inflammatory tumor microenvironment.

Eosinophilic cystitis mimics bladder tumor: A rare case report.

Eosinophilic cystitis is a rare inflammatory condition of the bladder characterized by eosinophils infiltrating the bladder wall. It affects people of all ages and with no gender difference. Eosinophilic cystitis can mimic bladder tumors and other chronic cystitis, which makes it a challenging condition to diagnose. In this article, we will discuss the causes, symptoms, diagnosis, and treatment of eosinophilic cystitis, as well as how it might be mistaken for bladder tumors.

The association between ferritin levels and all-cause mortality in stroke patients.

Purpose: The purpose of study was to describe the association between ferritin and all-cause mortality of cases with stroke. Methods: Clinical data derived from Multiparameter Intelligent Monitoring in Intensive Care were analyzed. The primary endpoint was 30-day mortality. The potential prognostic roles of Ferritin L were analyzed by Cox proportional hazard models. The independent prognostic roles of Ferritin L in the cases were analyzed by smooth curve fitting. Results: Concerning 30-day mortality, the HR (95% CI) for a high Ferritin (≥373) was 1.925 (1.298, 2.854; p = 0.00113), compared to a low ferritin (< 373). After adjusting for multiple confounders, the HR (95% CI) for a high Ferritin (≥373) was 1.782 (1.126, 2.820; p = 0.01367), compared to a low Ferritin (< 373). A non-linear association between Ferritin and 30-day mortality was found. Using recursive algorithm and two-piecewise linear regression model, inflection point (IP) was calculated, which was 2,204. On the left side of the IP, there was a positive relationship between Ferritin and 30-day mortality, and the effect size, 95% CI and p value were 1.0006 (1.0004, 1.0009) p < 0.0001, respectively. On the right of the IP, the effect size, 95% CI and p value were 1.0000 (1.0000, 1.0000) and 0.3107, respectively. Conclusion: Ferritin was associated with increased risk of stroke; it is important to further examine the association if the increased uric acid would increase the outcome of stroke in a longitudinal study. The non-linear relationship between Ferritin and all-cause mortality of stroke was observed. Ferritin was a risk factor for the outcome of stroke when ferritin was <2204.

Association between lactate/albumin ratio and prognosis in critically ill patients with acute kidney injury undergoing continuous renal replacement therapy.

BACKGROUND: The primary objective was to examine the association between the lactate/albumin ratio (LAR) and the prognosis of patients with acute kidney injury (AKI) undergoing continuous renal replacement therapy (CRRT). METHODS: Utilizing the Medical Information Mart for Intensive Care IV (MIMIC-IV, v2.0) database, we categorized 703 adult AKI patients undergoing CRRT into survival and non-survival groups based on 28-day mortality. Patients were further grouped by LAR tertiles: low (< 0.692), moderate (0.692-1.641), and high (> 1.641). Restricted cubic splines (RCS), Least Absolute Shrinkage and Selection Operator (LASSO) regression, inverse probability treatment weighting (IPTW), and Kaplan-Meier curves were employed. RESULTS: In our study, the patients had a mortality rate of 50.07% within 28 days and 62.87% within 360 days. RCS analysis revealed a non-linear correlation between LAR and the risk of mortality at both 28 and 360 days. Cox regression analysis, which was adjusted for nine variables identified by LASSO, confirmed that a high LAR (>1.641) served as an independent predictor of mortality at these specific time points (p < 0.05) in AKI patients who were receiving CRRT. These findings remained consistent even after IPTW adjustment, thereby ensuring a reliable and robust outcome. Kaplan-Meier survival curves exhibited a gradual decline in cumulative survival rates at both 28 and 360 days as the LAR values increased (log-rank test, χ2 = 48.630, p < 0.001; χ2 = 33.530, p < 0.001). CONCLUSION: A high LAR (>1.641) was found to be an autonomous predictor of mortality at both 28 and 360 days in critically ill patients with AKI undergoing CRRT.

Development and validation of a diagnostic prediction model for severe periventricular-intraventricular hemorrhage in newborns: insights from a retrospective analysis utilizing the MIMIC-III database

Abstract Objective: Periventricular-intraventricular hemorrhage is the most common type of intracranial bleeding in newborns, especially in the first 3 days after birth. Severe periventricular-intraventricular hemorrhage is considered a progression from mild periventricular-intraventricular hemorrhage and is often closely associated with severe neurological sequelae. However, no specific indicators are available to predict the progression from mild to severe periventricular-intraventricular in early admission. This study aims to establish an early diagnostic prediction model for severe PIVH. Method: This study was a retrospective cohort study with data collected from the MIMIC-III (v1.4) database. Laboratory and clinical data collected within the first 24 h of NICU admission have been used as variables for both univariate and multivariate logistic regression analyses to construct a nomogram-based early prediction model for severe periventricular-intraventricular hemorrhage and subsequently validated. Results: A predictive model was established and represented by a nomogram, it comprised three variables: output, lowest platelet count and use of vasoactive drugs within 24 h of NICU admission. The model's predictive performance showed by the calculated area under the curve was 0.792, indicating good discriminatory power. The calibration plot demonstrated good calibration between observed and predicted outcomes, and the Hosmer-Lemeshow test showed high consistency (p = 0.990). Internal validation showed the calculated area under a curve of 0.788. Conclusions: This severe PIVH predictive model, established by three easily obtainable indicators within the NICU, demonstrated good predictive ability. It offered a more user-friendly and convenient option for neonatologists.

Tailoring the peroxidase-like properties of Mo atom nanoclusters/N-MXene nanozymes for sensitive colorimetric detection of glutathione.

Although nanozyme engineering has made tremendous progress, there is a huge gap between them and natural enzymes due to the enormous challenge of precisely adjusting the geometric and electronic structure of active sites. Considering that intentionally adjusting the metal-carrier interactions may bring the promising catalytic activity, in this work, a novel Mo atom nanocluster is successfully synthesized using nitrogen-doped Mxene (MoACs/N-MXene) nanozymes as carriers. The constructed MoACs/N-MXene displays excellent peroxidase-like catalytic activity and kinetics, outweighing its N-MXene and Mo nanoparticles (NPs)-MXene references and natural horse radish peroxidase. This work not only reports a successful example of MoACs/N-MXene nanozyme as a guide for achieving peroxidase-mimic performance of nanozymes for colorimetric glutathione sensing at 0.29 µM, but also expands the application prospects of two-dimensional MXene nanosheets by reasonably introducing metal atomic clusters and nonmetal atom doping and exploring related nanozyme properties.

A systematic evaluation of the performance of GPT-4 and PaLM2 to diagnose comorbidities in MIMIC-IV patients.

Background: Given the strikingly high diagnostic error rate in hospitals, and the recent development of Large Language Models (LLMs), we set out to measure the diagnostic sensitivity of two popular LLMs: GPT-4 and PaLM2. Small-scale studies to evaluate the diagnostic ability of LLMs have shown promising results, with GPT-4 demonstrating high accuracy in diagnosing test cases. However, larger evaluations on real electronic patient data are needed to provide more reliable estimates. Methods: To fill this gap in the literature, we used a deidentified Electronic Health Record (EHR) data set of about 300,000 patients admitted to the Beth Israel Deaconess Medical Center in Boston. This data set contained blood, imaging, microbiology and vital sign information as well as the patients' medical diagnostic codes. Based on the available EHR data, doctors curated a set of diagnoses for each patient, which we will refer to as ground truth diagnoses. We then designed carefully-written prompts to get patient diagnostic predictions from the LLMs and compared this to the ground truth diagnoses in a random sample of 1000 patients. Results: Based on the proportion of correctly predicted ground truth diagnoses, we estimated the diagnostic hit rate of GPT-4 to be 93.9%. PaLM2 achieved 84.7% on the same data set. On these 1000 randomly selected EHRs, GPT-4 correctly identified 1116 unique diagnoses. Conclusion: The results suggest that artificial intelligence (AI) has the potential when working alongside clinicians to reduce cognitive errors which lead to hundreds of thousands of misdiagnoses every year. However, human oversight of AI remains essential: LLMs cannot replace clinicians, especially when it comes to human understanding and empathy. Furthermore, a significant number of challenges in incorporating AI into health care exist, including ethical, liability and regulatory barriers.

Severe B12 Deficiency Causing a Maturation Defect Mimicking Myelodysplastic Syndrome With Excess Blasts.

Vitamin B12 deficiency is a common condition that is often asymptomatic, though in severe cases may cause megaloblastic anemia and even neurologic symptoms. Occasionally, the clinical presentation can include pancytopenia and thus mimic a more concerning myelodysplastic syndrome (MDS) until corrected by B12 supplementation. In this unusual case, we present a patient with B12 deficiency who presents with severe macrocytic anemia, neutropenia, lymphocytosis, and a bone marrow morphology consistent with MDS.

Prognostic significance of the aspartate aminotransferase to lymphocyte ratio index in patients with acute myocardial infarction.

BACKGROUND: This study aimed to investigate the clinical value and prognostic significance of the alanine aspartate aminotransferase-to-lymphocyte ratio index (ALRI) in patients diagnosed with acute myocardial infarction (AMI). METHODS: Clinical indices of patients with AMI were collected from the Medical Information Mark for Intensive Care (MIMIC) III database and Wuhan Sixth Hospital. Cox regression analysis was used to explore whether ALRI was a risk factor for a worse prognosis in patients with AMI, and a nomogram including ALRI was created to estimate its predictive performance for 28-day mortality. RESULTS: Based on clinical data from the MIMIC-III database, we found that a high ALRI was closely associated with a variety of clinical parameters. It was an important risk factor for 28-day survival in patients with AMI (HR = 5.816). ALRI had a high predictive power for worse 28-day survival in patients with AMI (area under the curve [AUC] = 0.754). Additionally, we used clinical data from the Wuhan Sixth Hospital to verify the predictive power of ALRI in patients with AMI, and a high level of ALRI remained an independent risk factor for worse survival in patients with AMI (HR = 4.969). The AMI nomogram, including ALRI, displayed a good predictive performance for 28-day mortality in both the MIMIC-III (AUC = 0.826) and Wuhan Sixth Hospital cohorts (AUC = 0.795). CONCLUSION: The ALRI is closely related to the survival outcomes of patients with newly diagnosed AMI, indicating that it could serve as a novel biomarker for risk stratification such patients.