A Retrospective Comparison of Diagnostic Tools of Bullous Pemphigoid for Elderly Patients.

BACKGROUND: Older patients who are predisposed to bullous pemphigoid (BP) may exhibit reluctance to undergo skin biopsy due to potential complications. OBJECTIVES: This study aimed to conduct a comparative evaluation among histology, direct immunofluorescence (DIF), and indirect immunofluorescence (IIF) to determine the optimal diagnostic tool in elderly patients. METHODS: A retrospective study was conducted on 841 patients suspected of having BP. All cases were initially classified as BP and non-BP in accordance with the diagnostic criteria. Student's t-test and chi-squared test examined differences between the 2 groups. We evaluated the sensitivity, specificity, positive predictive value, negative predictive value, positive likelihood ratio, and negative likelihood ratio detected by the 3 tools. We stratified the analysis by age to compare the performance of the diagnostic tools and examined the risk factors associated with BP using logistic regression. RESULTS: Overall, histology exhibited the highest sensitivity (89.4%), while DIF demonstrated the highest specificity (67.1%). In the elderly, the IIF test exhibited the highest specificity (57.5%), the highest positive likelihood ratio (2.047), and the lowest negative likelihood ratio (0.226). Among patients taking Dipeptidyl Peptidase-4 (DPP-4) inhibitors, IIF demonstrated the highest positive likelihood ratio (3.194) and the second-lowest negative likelihood ratio (0.235). CONCLUSIONS: In cases that elderly patients suspected of having BP are reluctant to undergo skin biopsy, IIF demonstrates the optimal diagnostic method due to its highest positive likelihood ratio, the lowest negative likelihood ratio among the 3 diagnostic measures. Moreover, IIF is found to be a more effective tool for detecting BP in patients using DPP-4 inhibitors.

BMI is Associated with Post-Acute Elevations in Biomarkers of Platelet Activation and Inflammation in Unvaccinated Adults Diagnosed with COVID-19 in the Previous 8 Weeks.

Obesity has arisen as a prominent risk factor for COVID-19 severity and Long COVID (LC), potentially owed in part to the obesity-induced proinflammatory state. OBJECTIVES: This study aimed to examine relationships among circulating inflammatory biomarkers and BMI in nonhospitalized adults recently diagnosed with COVID-19. METHODS: This analysis included participants who completed a randomized placebo-controlled trial conducted October 2020-March 2021. Participants (19-53y) were unvaccinated and enrolled following COVID-19 diagnosis as allowed by CDC return-to-work guidance. Anthropometrics and biomarkers were assessed at study baseline and week-four. We examined the associations between BMI and inflammatory biomarkers via multiple regression models. RESULTS: At study baseline, (i.e., the point of enrollment following COVID-19 diagnosis) across all participants (N=60) a higher BMI was associated with elevations in several inflammatory biomarkers including IL-6 (ß=7.63, 95%CI= 3.54, 11.89, p = 0.0004), ferritin (ß= 6.31, 95%CI= 1.97,10.83, p=0.0047), hsCRP (ß= 13.1, 95% CI=8.03, 18.42, p=<0.0001), TNF-α (ß= 3.23, 95%CI=0.91, 5.60, p=0.0069), IL-12p40 (ß= 3.69, 95%CI =0.93,6.52, p=0.0094), IL-13 (ß= 5.11, 95%CI=1.00, 9.40, p=0.0154) and IL-1Ra (ß= 7.57, 95%CI= 3.61, 11.70, p=0.0003). In control group participants (n=30) after four weeks, a higher BMI was associated with elevations in IL-4 (ß=17.8, 95%CI=0.84,37.6, p=0.0397) and sP-selectin (ß=1.16, 95%CI=0.22, 2.11, p=0.0182), controlling for baseline and covariates. CONCLUSIONS: Here, BMI was positively associated with circulating biomarkers of platelet activation and inflammation in adults recently diagnosed with COVID-19 after four weeks. The shift in post-acute COVID-19 inflammatory biomarkers associated with an increasing BMI noted here share similarities to biomarkers of LC reported in the literature.

A pre-trained language model for emergency department intervention prediction using routine physiological data and clinical narratives.

INTRODUCTION: The urgency and complexity of emergency room (ER) settings require precise and swift decision-making processes for patient care. Ensuring the timely execution of critical examinations and interventions is vital for reducing diagnostic errors, but the literature highlights a need for innovative approaches to optimize diagnostic accuracy and patient outcomes. In response, our study endeavors to create predictive models for timely examinations and interventions by leveraging the patient's symptoms and vital signs recorded during triage, and in so doing, augment traditional diagnostic methodologies. METHODS: Focusing on four key areas-medication dispensing, vital interventions, laboratory testing, and emergency radiology exams, the study employed Natural Language Processing (NLP) and seven advanced machine learning techniques. The research was centered around the innovative use of BioClinicalBERT, a state-of-the-art NLP framework. RESULTS: BioClinicalBERT emerged as the superior model, outperforming others in predictive accuracy. The integration of physiological data with patient narrative symptoms demonstrated greater effectiveness compared to models based solely on textual data. The robustness of our approach was confirmed by an Area Under the Receiver Operating Characteristic curve (AUROC) score of 0.9. CONCLUSION: The findings of our study underscore the feasibility of establishing a decision support system for emergency patients, targeting timely interventions and examinations based on a nuanced analysis of symptoms. By using an advanced natural language processing technique, our approach shows promise for enhancing diagnostic accuracy. However, the current model is not yet fully mature for direct implementation into daily clinical practice. Recognizing the imperative nature of precision in the ER environment, future research endeavors must focus on refining and expanding predictive models to include detailed timely examinations and interventions. Although the progress achieved in this study represents an encouraging step towards a more innovative and technology-driven paradigm in emergency care, full clinical integration warrants further exploration and validation.

Machine Learning Model for Anesthetic Risk Stratification for Gynecologic and Obstetric Patients: A Novel Approach for Early Detection.

BACKGROUND: Preoperative evaluation is important, our study explored the application of machine learning methods for anesthetic risk classification and for the evaluation of the contributions of various factors. To minimize the effects of confounding variables during model training, we used a homogenous group with similar physiological states and ages undergoing similar pelvic organ-related procedures not involving malignancies. OBJECTIVE: Data on women of reproductive age (age = 20-50 years) who underwent gestational or gynecological surgery between January 1, 2017, and December 31, 2021, were obtained from the National Taiwan University Hospital Integrated Medical Database. METHODS: We first performed an exploratory analysis and selected key features. We then performed data preprocessing to acquire relevant features related to preoperative examination. To further enhance predictive performance, we employed the log likelihood ratio algorithm to generate comorbidity patterns. Lastly, we input the processed features into the light gradient boosting machine (LightGBM) model for training and subsequent prediction. RESULTS: A total of 10,892 patients were included. Within this data set, 9893 patients were classified as having low anesthetic risk (American Society of Anesthesiologists physical status score 1-2), and 999 patients were classified as having high anesthetic risk (American Society of Anesthesiologists physical status score > 2). The area under the receiver operating characteristic curve of the LightGBM model was 90.25. CONCLUSIONS: By combining comorbidity information and clinical laboratory data, our methodology based on the LightGBM model provides more accurate predictions for anesthetic risk classification. CLINICALTRIAL: This study was registered with the Research Ethics Committee of the National Taiwan University Hospital with trial number 202204010RINB.

Discovery of a mu-opioid receptor modulator that in combination with morphinan antagonists induces analgesia.

Morphinan antagonists, which block opioid effects at mu-opioid receptors, have been studied for their analgesic potential. Previous studies have suggested that these antagonists elicit analgesia with fewer adverse effects in the presence of the mutant mu-opioid receptor (MOR; S196A). However, introducing a mutant receptor for medical applications represents significant challenges. We hypothesize that binding a chemical compound to the MOR may elicit a comparable effect to the S196A mutation. Through high-throughput screening and structure-activity relationship studies, we identified a modulator, 4-(2-(4-fluorophenyl)-4-oxothiazolidin-3-yl)-3-methylbenzoic acid (BPRMU191), which confers agonistic properties to small-molecule morphinan antagonists, which induce G protein-dependent MOR activation. Co-application of BPRMU191 and morphinan antagonists resulted in MOR-dependent analgesia with diminished side effects, including gastrointestinal dysfunction, antinociceptive tolerance, and physical and psychological dependence. Combining BPRMU191 and morphinan antagonists could serve as a potential therapeutic strategy for severe pain with reduced adverse effects and provide an avenue for studying G protein-coupled receptor modulation.

Marine sponge-derived alkaloid inhibits the PI3K/AKT/mTOR signaling pathway against diffuse large B-cell lymphoma.

Diffuse large B-cell lymphoma (DLBCL) is a genetically heterogeneous non-Hodgkin lymphoma that is extremely aggressive and has an intermediate to high malignancy. Some patients still experience treatment failure, relapse, or resistance to rituximab, cyclophosphamide, adriamycin, vincristine, and prednisone (R-CHOP) therapy. Therefore, there is an urgent need for further research on new agents for the treatment of DLBCL. AP-48 is an aaptamine alkaloid analog with potent anti-tumor effects that originates from marine natural products. In this study, we found that AP-48 exhibits dose-dependent cytotoxicity in DLBCL cell lines. Flow cytometry showed that AP-48 induced cell cycle arrest in the G0/G1 phase in SU-DHL-4 and Farage cells and in the S phase in WSU-DLCL-2 cells. AP-48 also accelerated apoptosis via the caspase-3-mediated intrinsic apoptotic pathway. Further experiments demonstrated that AP-48 exerted its anti-DLBCL effects through the PI3K/AKT/mTOR pathway, and that the PI3K agonist YS49 partially alleviated the inhibition of cell proliferation and apoptosis induced by AP-48. Finally, in a tumor xenograft model, AP-48 inhibited tumor growth and promoted apoptosis in tumor tissues, indicating its therapeutic potential in DLBCL.

Genomic characterization of Escherichia coli harbor a polyketide synthase ( pks ) island associated with colorectal cancer (CRC) development.

The E. coli strain harboring the polyketide synthase ( Pks) island encodes the genotoxin colibactin, a secondary metabolite reported to have severe implications for human health and for the progression of colorectal cancer. The present study involved whole-genome-wide comparison and phylogenetic analysis of pks harboring E. coli isolates to gain insight into the distribution and evolution of these organism. Fifteen E. coli strains isolated from patients with ulcerative colitis were sequenced, 13 of which harbored pks islands. In addition, 2,654 genomes from the public database were also screened for pks harboring E. coli genomes, 158 of which were pks -positive isolates. Whole-genome-wide comparison and phylogenetic analysis revealed that 171 (158+13) pks -positive isolates belonged to phylogroup B2, and most of the isolates associated to sequence types ST73 and ST95. One isolate from an ulcerative colitis (UC) patient was of the sequence type ST8303. The maximum likelihood tree based on the core genome of pks -positive isolates revealed horizontal gene transfer across sequence types and serotypes. Virulome and resistome analyses revealed the preponderance of virulence genes and a reduced number of antimicrobial genes in Pks -positive isolates. This study strongly contributes to understanding the evolution of pks islands in E. coli .

Marine derived macrolide bryostatin 4 inhibits the TGF-ß signaling pathway against acute erythroleukemia.

PURPOSE: Acute erythroleukemia (AEL) is a rare and highly aggressive subtype of acute myeloid leukemia (AML) with an extremely poor prognosis when treated with available drugs. Therefore, new investigational agents capable of inducing remission are urgently required. METHODS: Bioinformatics analysis, western blot and qRT-PCR were used to reveal the potential biological mechanism of bryostatin 4 (B4), an antineoplastic macrolide derived from the marine bryozoan Bugula neritina. Then, in vivo experiments were conducted to evaluate the role of transforming growth factor (TGF)-ß signaling in the progression of AEL. RESULTS: Our results revealed that the proliferation of K562 cells and TF-1 cells was significantly inhibited by B4 at IC50 values of 37 nM and 52 nM, respectively. B4 inhibited TGF-ß signaling and its downstream pathway targets, particularly the phosphorylation of Smad2, Smad3, Ras, C-RAF, ERK1/2, and MEK. B4 also played an important role in cell invasion and migration in K562 cells and TF-1 cells by reducing the protein levels of the mesenchymal cell marker vimentin. Moreover, Flow cytometry and western blot analyses demonstrated that B4 induced apoptosis and initiated G0/G1 phase arrest by modulating mitochondrial dysfunction and cyclin-dependent kinase (CDK) expression. CONCLUSION: These findings indicated that B4 could inhibit the proliferation, migration, invasion, and TGF-ß signaling pathways of AEL cells, thus suggesting that B4 possesses therapeutic potential as a treatment for AEL.

What is the best predictor of mortality in patients with type 2 diabetes and chronic kidney disease: mean, variability of HbA1c or HbA1c-Hemoglobin ratio?

AIM: Limitations in the measurement of glycated hemoglobin (HbA1c) in patients with type 2 diabetes (T2D) and chronic kidney disease (CKD) result in uncertainty about the best predictor of mortality among these patients. Our study aimed to determine the association between the mean and average real variability (ARV) of HbA1c, as well as HbA1c-hemoglobin (HH) ratio with mortality among patients with T2D and CKD. MATERIALS AND METHODS: We identified 16,868 T2D patients with stage 3 or above CKD from outpatient visits during 2003-2018. We ascertained all-cause and cardiovascular mortality through linkage to Taiwan's National Death Registry. Mortality rates were estimated using the Poisson distribution, and we conducted Cox proportional hazards regressions to assess relative risks of mortality corresponding to the mean HbA1c, ARV of HbA1c and HH ratio. RESULTS: Compared to patients with a mean HbA1c of 7.0-7.9%, a mean HbA1c < 7.0% was persistently associated with highest risk of all-cause but not cardiovascular mortality after adjusting for confounders. On the contrary, patients with HbA1c-ARV in the second to fourth quartiles and HH ratios in the higher quartiles showed increased risk of all-cause and cardiovascular mortality compared to those in the first quartiles. CONCLUSIONS: HbA1c-ARV was more effective than mean HbA1c or HH ratio in predicting mortality in T2D patients with CKD. Apart from optimal glucose control, multidisciplinary care focusing on glycemic variability is essential for reducing mortality in these patients.

Use of Sodium-Glucose Co-Transporter 2 Inhibitors in Patients With Type 2 Diabetes and the Incidence of Retinal Vein Occlusion in Taiwan.

Purpose: The purpose of this study was to evaluate the risk of newly diagnosed retinal vein occlusion (RVO) in patients with type 2 diabetes (T2D) using sodium-glucose cotransporter-2 inhibitors (SGLT-2i) compared to dipeptidyl peptidase-4 inhibitors (DPP-4i). Methods: Claims data from the National Health Insurance Research Database of Taiwan were used in this nationwide retrospective cohort study. A target trial emulation framework was applied. Patients with T2D with no prior diagnosis of RVO who had newly commenced treatment with SGLT-2i or DPP-4i between May 1, 2016, and December 31, 2020, were included. Potential systematic differences in baseline characteristics between the paired groups were controlled using stabilized inverse probability of treatment weighting. The outcome of interest was incident RVO. The hazard ratio (HR) for SGLT-2i compared with that of DPP-4i was estimated using a Cox regression model. Results: Data from 123,567 and 578,665 patients receiving SGLT-2i and DPP-4i, respectively, were analyzed. The incidence of RVO was lower in patients newly receiving SGLT-2i (0.59 events per 1000 person-years) compared to those receiving DPP-4i (0.77 events per 1000 person-years) over a mean follow-up of 1.61 years. SGLT-2i users had a significantly lower risk of developing RVO compared with DPP-4i users (HR = 0.76, 95% confidence interval [CI] = 0.59-0.98). In the individual outcome analysis, SGLT-2i use was significantly associated with a lower risk of branch RVO (HR = 0.71, 95% CI = 0.52-0.96), but not central RVO (HR = 0.84, 95% CI = 0.57-1.24). Conclusions: The risk of developing RVO was lower in patients with T2D receiving SGLT-2i compared with that in those receiving DPP-4i.

Calycosin Enhances Heat Shock Related-Proteins in H9c2 Cells to Modulate Survival and Apoptosis against Heat Shock.

Heat shock proteins (HSPs), which function as chaperones, are activated in response to various environmental stressors. In addition to their role in diverse aspects of protein production, HSPs protect against harmful protein-related stressors. Calycosin exhibits numerous beneficial properties. This study aims to explore the protective effects of calycosin in the heart under heat shock and determine its underlying mechanism. H9c2 cells, western blot, TUNEL staining, flow cytometry, and immunofluorescence staining were used. The time-dependent effects of heat shock analyzed using western blot revealed increased HSP expression for up to 2[Formula: see text]h, followed by protein degradation after 4[Formula: see text]h. Hence, a heat shock damage duration of 4[Formula: see text]h was chosen for subsequent investigations. Calycosin administered post-heat shock demonstrated dose-dependent recovery of cell viability. Under heat shock conditions, calycosin prevented the apoptosis of H9c2 cells by upregulating HSPs, suppressing p-JNK, enhancing Bcl-2 activation, and inhibiting cleaved caspase 3. Calycosin also inhibited Fas/FasL expression and activated cell survival markers (p-PI3K, p-ERK, p-Akt), indicating their cytoprotective properties through PI3K/Akt activation and JNK inhibition. TUNEL staining and flow cytometry confirmed that calycosin reduced apoptosis. Moreover, calycosin reversed the inhibitory effects of quercetin on HSF1 and Hsp70 expression, illustrating its role in enhancing Hsp70 expression through HSF1 activation during heat shock. Immunofluorescence staining demonstrated HSF1 translocation to the nucleus following calycosin treatment, emphasizing its cytoprotective effects. In conclusion, calycosin exhibits pronounced protective effects against heat shock-induced damages by modulating HSP expression and regulating key signaling pathways to promote cell survival in H9c2 cells.

CXCR7 activation evokes the anti-PD-L1 antibody against glioblastoma by remodeling CXCL12-mediated immunity.

The interaction between glioblastoma cells and glioblastoma-associated macrophages (GAMs) influences the immunosuppressive tumor microenvironment, leading to ineffective immunotherapies. We hypothesized that disrupting the communication between tumors and macrophages would enhance the efficacy of immunotherapies. Transcriptomic analysis of recurrent glioblastoma specimens indicated an enhanced neuroinflammatory pathway, with CXCL12 emerging as the top-ranked gene in secretory molecules. Single-cell transcriptome profiling of naïve glioblastoma specimens revealed CXCL12 expression in tumor and myeloid clusters. An analysis of public glioblastoma datasets has confirmed the association of CXCL12 with disease and PD-L1 expression. In vitro studies have demonstrated that exogenous CXCL12 induces pro-tumorigenic characteristics in macrophage-like cells and upregulated PD-L1 expression through NF-κB signaling. We identified CXCR7, an atypical receptor for CXCL12 predominantly present in tumor cells, as a negative regulator of CXCL12 expression by interfering with extracellular signal-regulated kinase activation. CXCR7 knockdown in a glioblastoma mouse model resulted in worse survival outcomes, increased PD-L1 expression in GAMs, and reduced CD8+ T-cell infiltration compared with the control group. Ex vivo T-cell experiments demonstrated enhanced cytotoxicity against tumor cells with a selective CXCR7 agonist, VUF11207, reversing GAM-induced immunosuppression in a glioblastoma cell-macrophage-T-cell co-culture system. Notably, VUF11207 prolonged survival and potentiated the anti-tumor effect of the anti-PD-L1 antibody in glioblastoma-bearing mice. This effect was mitigated by an anti-CD8ß antibody, indicating the synergistic effect of VUF11207. In conclusion, CXCL12 conferred immunosuppression mediated by pro-tumorigenic and PD-L1-expressing GAMs in glioblastoma. Targeted activation of glioblastoma-derived CXCR7 inhibits CXCL12, thereby eliciting anti-tumor immunity and enhancing the efficacy of anti-PD-L1 antibodies.

Protein Binder Toolbox for Studies of Solute Carrier Transporters.

Transporters of the solute carrier superfamily (SLCs) are responsible for the transmembrane traffic of the majority of chemical substances in cells and tissues and are therefore of fundamental biological importance. As is often the case with membrane proteins that can be heavily glycosylated, a lack of reliable high-affinity binders hinders their functional analysis. Purifying and reconstituting transmembrane proteins in their lipidic environments remains challenging and standard approaches to generate binders for multi-transmembrane proteins, such as SLCs, channels or G protein-coupled receptors (GPCRs) are lacking. While generating protein binders to 27 SLCs, we produced full length protein or cell lines as input material for binder generation by selected binder generation platforms. As a result, we obtained 525 binders for 22 SLCs. We validated the binders with a cell-based validation workflow using immunofluorescent and immunoprecipitation methods to process all obtained binders. Finally, we demonstrated the potential applications of the binders that passed our validation pipeline in structural, biochemical, and biological applications using the exemplary protein SLC12A6, an ion transporter relevant in human disease. With this work, we were able to generate easily renewable and highly specific binders against SLCs, which will greatly facilitate the study of this neglected protein family. We hope that the process will serve as blueprint for the generation of binders against the entire superfamily of SLC transporters.

The Interrelationship of Benefit Finding, Demoralization, and Stigma among Patients with Parkinson's Disease and Their Caregivers.

Parkinson's disease (PD) is a debilitating neurodegenerative disease with a relentlessly progressive course of illness. This study aimed to assess the dyadic dynamics of benefit finding (BF), demoralization, and stigma on the depression severity of PD patients and their caregivers. This study used a cross-sectional design with purposive sampling. In total, 120 PD patients and 120 caregivers were recruited from the neurological ward or neurological outpatient clinic of a medical center in Taiwan from October 2021 to September 2022. PD patients and their caregivers were enrolled and assessed using the Mini International Neuropsychiatric Interview, the Benefit Finding scale, Demoralization Scale, Stigma Subscale of the Explanatory Model Interview Catalogue, and Taiwanese Depression Questionnaire. Among the 120 patients and 120 caregivers that successfully completed the study, 41.7% (N = 50) and 60% (N = 72) were female, respectively. The most common psychiatric diagnoses of both the PD patients (17.5%) and their caregivers (13.3%) were depressive disorders. Using structural equation modeling, we found that the stigma, BF, and demoralization of PD patients might contribute to their depression severity. Demoralization and stigma of PD patients' caregivers might also contribute to the depression severity of PD patients. Caregivers' BF and demoralization were significantly linked with their depression severity. PD patients' BF degree and their caregivers' BF degree had significant interactive effects. Both patients' and their caregivers' stigma levels had significant interactive effects. Clinicians should be aware of and manage these contributing factors between PD patients and their caregivers in order to prevent them from exacerbating each other's depression.

The Impact of Thallium Exposure in Public Health and Molecular Toxicology: A Comprehensive Review.

This review offers a synthesis of the current understanding of the impact of low-dose thallium (Tl) on public health, specifically emphasizing its diverse effects on various populations and organs. The article integrates insights into the cytotoxic effects, genotoxic potential, and molecular mechanisms of thallium in mammalian cells. Thallium, a non-essential heavy metal present in up to 89 different minerals, has garnered attention due to its adverse effects on human health. As technology and metallurgical industries advance, various forms of thallium, including dust, vapor, and wastewater, can contaminate the environment, extending to the surrounding air, water sources, and soil. Moreover, the metal has been identified in beverages, tobacco, and vegetables, highlighting its pervasive presence in a wide array of food sources. Epidemiological findings underscore associations between thallium exposure and critical health aspects such as kidney function, pregnancy outcomes, smoking-related implications, and potential links to autism spectrum disorder. Thallium primarily exerts cellular toxicity on various tissues through mitochondria-mediated oxidative stress and endoplasmic reticulum stress. This synthesis aims to shed light on the intricate web of thallium exposure and its potential implications for public health, emphasizing the need for vigilant consideration of its risks.

A nursing note-aware deep neural network for predicting mortality risk after hospital discharge.

BACKGROUND: ICU readmissions and post-discharge mortality pose significant challenges. Previous studies used EHRs and machine learning models, but mostly focused on structured data. Nursing records contain crucial unstructured information, but their utilization is challenging. Natural language processing (NLP) can extract structured features from clinical text. This study proposes the Crucial Nursing Description Extractor (CNDE) to predict post-ICU discharge mortality rates and identify high-risk patients for unplanned readmission by analyzing electronic nursing records. OBJECTIVE: Developed a deep neural network (NurnaNet) with the ability to perceive nursing records, combined with a bio-clinical medicine pre-trained language model (BioClinicalBERT) to analyze the electronic health records (EHRs) in the MIMIC III dataset to predict the death of patients within six month and two year risk. DESIGN: A cohort and system development design was used. SETTING(S): Based on data extracted from MIMIC-III, a database of critically ill in the US between 2001 and 2012, the results were analyzed. PARTICIPANTS: We calculated patients' age using admission time and date of birth information from the MIMIC dataset. Patients under 18 or over 89 years old, or who died in the hospital, were excluded. We analyzed 16,973 nursing records from patients' ICU stays. METHODS: We have developed a technology called the Crucial Nursing Description Extractor (CNDE), which extracts key content from text. We use the logarithmic likelihood ratio to extract keywords and combine BioClinicalBERT. We predict the survival of discharged patients after six months and two years and evaluate the performance of the model using precision, recall, the F1-score, the receiver operating characteristic curve (ROC curve), the area under the curve (AUC), and the precision-recall curve (PR curve). RESULTS: The research findings indicate that NurnaNet achieved good F1-scores (0.67030, 0.70874) within six months and two years. Compared to using BioClinicalBERT alone, there was an improvement in performance of 2.05 % and 1.08 % for predictions within six months and two years, respectively. CONCLUSIONS: CNDE can effectively reduce long-form records and extract key content. NurnaNet has a good F1-score in analyzing the data of nursing records, which helps to identify the risk of death of patients after leaving the hospital and adjust the regular follow-up and treatment plan of relevant medical care as soon as possible.

Using a clinical narrative-aware pre-trained language model for predicting emergency department patient disposition and unscheduled return visits.

The increasing prevalence of overcrowding in Emergency Departments (EDs) threatens the effective delivery of urgent healthcare. Mitigation strategies include the deployment of monitoring systems capable of tracking and managing patient disposition to facilitate appropriate and timely care, which subsequently reduces patient revisits, optimizes resource allocation, and enhances patient outcomes. This study used âˆ¼ 250,000 emergency department visit records from Taipei Medical University-Shuang Ho Hospital to develop a natural language processing model using BlueBERT, a biomedical domain-specific pre-trained language model, to predict patient disposition status and unplanned readmissions. Data preprocessing and the integration of both structured and unstructured data were central to our approach. Compared to other models, BlueBERT outperformed due to its pre-training on a diverse range of medical literature, enabling it to better comprehend the specialized terminology, relationships, and context present in ED data. We found that translating Chinese-English clinical narratives into English and textualizing numerical data into categorical representations significantly improved the prediction of patient disposition (AUROC = 0.9014) and 72-hour unscheduled return visits (AUROC = 0.6475). The study concludes that the BlueBERT-based model demonstrated superior prediction capabilities, surpassing the performance of prior patient disposition predictive models, thus offering promising applications in the realm of ED clinical practice.

A Preliminary Investigation of the Roles of Endometrial Cells in Endometriosis Development via In Vitro and In Vivo Analyses.

Endometriosis is a complex gynecological disease that affects more than 10% of women in their reproductive years. While surgery can provide temporary relief from women's pain, symptoms often return in as many as 75% of cases within two years. Previous literature has contributed to theories about the development of endometriosis; however, the exact pathogenesis and etiology remain elusive. We conducted a preliminary investigation into the influence of primary endometrial cells (ECs) on the development and progression of endometriosis. In vitro studies, they were involved in inducing Lipopolysaccharide (LPS) in rat-isolated primary endometrial cells, which resulted in increased nuclear factor-kappa B (NF-κB) and vascular endothelial growth factor (VEGF) mRNA gene expression (quantitative polymerase chain reaction analysis, qPCR) and protein expression (western blot analysis). Additionally, in vivo studies utilized autogenic and allogeneic transplantations (rat to rat) to investigate endometriosis-like lesion cyst size, body weight, protein levels (immunohistochemistry), and mRNA gene expression. These studies demonstrated that estrogen upregulates the gene and protein regulation of cytoskeletal (CK)-18, transforming growth factor-ß (TGF-ß), VEGF, and tumor necrosis factor (TNF)-α, particularly in the peritoneum. These findings may influence cell proliferation, angiogenesis, fibrosis, and inflammation markers. Consequently, this could exacerbate the occurrence and progression of endometriosis.

Benefit analysis of the auto-verification system of intelligent inspection for microorganisms.

In recent years, the automatic machine for microbial identification and antibiotic susceptibility tests has been introduced into the microbiology laboratory of our hospital, but there are still many steps that need manual operation. The purpose of this study was to establish an auto-verification system for bacterial naming to improve the turnaround time (TAT) and reduce the burden on clinical laboratory technologists. After the basic interpretation of the gram staining results of microorganisms, the appearance of strain growth, etc., the 9 rules were formulated by the laboratory technologists specialized in microbiology for auto-verification of bacterial naming. The results showed that among 70,044 reports, the average pass rate of auto-verification was 68.2%, and the reason for the failure of auto-verification was further evaluated. It was found that the main causes reason the inconsistency between identification results and strain appearance rationality, the normal flora in the respiratory tract and urine that was identified, the identification limitation of the mass spectrometer, and so on. The average TAT for the preliminary report of bacterial naming was 35.2 h before, which was reduced to 31.9 h after auto-verification. In summary, after auto-verification, the laboratory could replace nearly 2/3 of manual verification and issuance of reports, reducing the daily workload of medical laboratory technologists by about 2 h. Moreover, the TAT on the preliminary identification report was reduced by 3.3 h on average, which could provide treatment evidence for clinicians in advance.