A combination of a TLR7/8 agonist and an epigenetic inhibitor suppresses triple-negative breast cancer through triggering anti-tumor immune.

BACKGROUND: Combination therapy involving immune checkpoint blockade (ICB) and other drugs is a potential strategy for converting immune-cold tumors into immune-hot tumors to benefit from immunotherapy. To achieve drug synergy, we developed a homologous cancer cell membrane vesicle (CM)-coated metal-organic framework (MOF) nanodelivery platform for the codelivery of a TLR7/8 agonist with an epigenetic inhibitor. METHODS: A novel biomimetic codelivery system (MCM@UN) was constructed by MOF nanoparticles UiO-66 loading with a bromodomain-containing protein 4 (BRD4) inhibitor and then coated with the membrane vesicles of homologous cancer cells that embedding the 18 C lipid tail of 3M-052 (M). The antitumor immune ability and tumor suppressive effect of MCM@UN were evaluated in a mouse model of triple-negative breast cancer (TNBC) and in vitro. The tumor immune microenvironment was analyzed by multicolor immunofluorescence staining. RESULTS: In vitro and in vivo data showed that MCM@UN specifically targeted to TNBC cells and was superior to the free drug in terms of tumor growth inhibition and antitumor immune activity. In terms of mechanism, MCM@UN blocked BRD4 and PD-L1 to prompt dying tumor cells to disintegrate and expose tumor antigens. The disintegrated tumor cells released damage-associated molecular patterns (DAMPs), recruited dendritic cells (DCs) to efficiently activate CD8+ T cells to mediate effective and long-lasting antitumor immunity. In addition, TLR7/8 agonist on MCM@UN enhanced lymphocytes infiltration and immunogenic cell death and decreased regulatory T-cells (Tregs). On clinical specimens, we found that mature DCs infiltrating tumor tissues of TNBC patients were negatively correlated with the expression of BRD4, which was consistent with the result in animal model. CONCLUSION: MCM@UN specifically targeted to TNBC cells and remodeled tumor immune microenvironment to inhibit malignant behaviors of TNBC.

Autocrine S100B in astrocytes promotes VEGF-dependent inflammation and oxidative stress and causes impaired neuroprotection.

Minimal hepatic encephalopathy (MHE) is strongly associated with neuroinflammation. Nevertheless, the underlying mechanism of the induction of inflammatory response in MHE astrocytes remains not fully understood. In the present study, we investigated the effect and mechanism of S100B, a predominant isoform expressed and released from mature astrocytes, on MHE-like neuropathology in the MHE rat model. We discovered that S100B expressions and autocrine were significantly increased in MHE rat brains and MHE rat brain-derived astrocytes. Furthermore, S100B stimulates VEGF expression via the interaction between TLR2 and RAGE in an autocrine manner. S100B-facilitated VEGF autocrine expression further led to a VEGFR2 and COX-2 interaction, which in turn induced the activation of NFƙB, eventually resulting in inflammation and oxidative stress in MHE astrocytes. MHE astrocytes supported impairment of neuronal survival and growth in a co-culture system. To sum up, a comprehensive understanding of the role of S100B-overexpressed MHE astrocyte in MHE pathogenesis may provide insights into the etiology of MHE.

Bioengineered stem cell membrane functionalized nanoparticles combine anti-inflammatory and antimicrobial properties for sepsis treatment.

BACKGROUND: Sepsis is a syndrome of physiological, pathological and biochemical abnormalities caused by infection. Although the mortality rate is lower than before, many survivors have persistent infection, which means sepsis calls for new treatment. After infection, inflammatory mediators were largely released into the blood, leading to multiple organ dysfunction. Therefore, anti-infection and anti-inflammation are critical issues in sepsis management. RESULTS: Here, we successfully constructed a novel nanometer drug loading system for sepsis management, FZ/MER-AgMOF@Bm. The nanoparticles were modified with LPS-treated bone marrow mesenchymal stem cell (BMSC) membrane, and silver metal organic framework (AgMOF) was used as the nanocore for loading FPS-ZM1 and meropenem which was delivery to the infectious microenvironments (IMEs) to exert dual anti-inflammatory and antibacterial effects. FZ/MER-AgMOF@Bm effectively alleviated excessive inflammatory response and eliminated bacteria. FZ/MER-AgMOF@Bm also played an anti-inflammatory role by promoting the polarization of macrophages to M2. When sepsis induced by cecal ligation and puncture (CLP) challenged mice was treated, FZ/MER-AgMOF@Bm could not only reduce the levels of pro-inflammatory factors and lung injury, but also help to improve hypothermia caused by septic shock and prolong survival time. CONCLUSIONS: Together, the nanoparticles played a role in combined anti-inflammatory and antimicrobial properties, alleviating cytokine storm and protecting vital organ functions, could be a potential new strategy for sepsis management.

Comprehensive Analysis of Tripterine Anti-Ovarian Cancer Effects Using Weighted Gene Co-Expression Network Analysis and Molecular Docking.

BACKGROUND Ovarian cancer has the highest mortality of gynecological cancers worldwide. The aim of this study was to identify the role of tripterine against ovarian cancer. MATERIAL AND METHODS GSE18520 and GSE12470 data sets were downloaded from the GEO database. WGCNA was used to analyze gene modules and hub genes related to ovarian cancer. These hub genes were intersected with tripterine targets, and GO and KEGG enrichment analyses were performed. HPA and GEPIA determined the expression of tripterine anti-ovarian hub genes in tumor tissues. Kaplan-Meier plotter was used to explore the role of hub genes in ovarian cancer prognosis. AutoDock was used to conduct molecular docking of tripterine and hub genes to observe whether the combination was stable. RESULTS By differential analysis of gene expression and the construction of WGCNA co-expression network, 5 hub genes, ARHGAP11A, MUC1, HBB, RUNX1T1, and FUT8, were screened by module gene screening. Seven biological processes and 20 KEGG-related pathways were obtained by gene enrichment. The expression of tripterine anti-ovarian hub genes ARHGAP11A, MUC1, and FUT8 were obtained by HPA and GEPIA. Using Kaplan-Meier plotter, the survival of ovarian cancer was negatively correlated with ARHGAP11A, MUC1, and FUT8. Molecular docking showed the combination of tripterine and FUT8 was most stable, having the greatest potential role. CONCLUSIONS Tripterine may be involved in megakaryocyte development and platelet production through potential genes ARHGAP11A, MUC1, HBB, RUNX1T1, and FUT8 and may have an anti-ovarian cancer effect in immune factors signaling, transporting and exchanging oxygen pathways, and autophagy pathways, through these 5 key genes.

Exploring structural relations among computer self-efficacy, perceived immersion, and intention to use virtual reality training systems.

The use of virtual reality (VR) training systems for education has grown in popularity in recent years. Scholars have reported that self-efficacy and interactivity are important predictors of learning outcomes in virtual learning environments, but little empirical research has been conducted to explain how computer self-efficacy (as a subcategory of self-efficacy) and perceived immersion (as a correlate of interactivity) are connected to the intention to use VR training systems. The present study aims to determine which factors significantly influence behavioral intention when students are exposed to VR training systems via an updated technology acceptance frame by incorporating the constructs of computer self-efficacy and perceived immersion simultaneously. We developed a VR training system regarding circuit connection and a reliable and validated instrument including 9 subscales. The sample data were collected from 124 junior middle school students and 210 senior high school students in two schools located in western China. The samples were further processed into a structural equation model with path analysis and cohort analysis. The results showed that the intention to use VR training systems was indirectly influenced by computer self-efficacy but directly influenced by perceived immersion (ß = 0.451). However, perceived immersion seemed to be influenced mostly by learner interaction (ß = 0.332). Among external variables, learner interaction (ß = 0.149) had the largest total effect on use intention, followed by facilitating conditions (ß = 0.138), computer self-efficacy (ß = 0.104), experimental fidelity (ß = 0.083), and subjective norms (ß = 0.077). The moderating roles of gender differences, grade level, and previous experience in structural relations were also identified. The findings of the present study highlight the ways in which factors and associations are considered in the practical development of VR training systems.

A survey of laboratory biosafety and protective measures in blood transfusion departments during the COVID-19 pandemic.

BACKGROUND AND OBJECTIVES: Thousands of healthcare workers (HCWs) have been infected with 2019 novel coronavirus pneumonia (COVID-19) during the COVID-19 pandemic. Laboratory personnel in blood transfusion departments may be infected with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) if laboratory biosafety protection is insufficient. Therefore, we investigated the current situation of laboratory biosafety protection in blood transfusion departments to determine how to improve the safety of laboratory processes. MATERIALS AND METHODS: An online survey was conducted in blood transfusion departments from 1st to 6th May 2020 in China. A total of 653 individuals completed the questionnaire. The questionnaire was designed with reference to COVID-19 laboratory biosafety summarized in Annex II. All responses were summarized using only descriptive statistics and expressed as frequencies and ratios [n (%)]. RESULTS: Most participants were concerned about COVID-19. Some participants had inadequate knowledge of COVID-19. Two participants stated that there were laboratory personnel infected with SARS-CoV-2 in their departments. A total of 31 (4.7%) participants did not receive any safety and security training. In terms of laboratory biosafety protection practices, the major challenges were suboptimal laboratory safety practices and insufficient laboratory conditions. CONCLUSION: The major deficiencies were insufficient security and safety training, and a lack of personal protective equipment, automatic cap removal centrifuges and biosafety cabinets. Consequently, we should enhance the security and safety training of laboratory personnel to improve their laboratory biosafety protection practices and ensure that laboratory conditions are sufficient to improve the safety of laboratory processes.

Oridonin prevents insulin resistance-mediated cognitive disorder through PTEN/Akt pathway and autophagy in minimal hepatic encephalopathy.

Minimal hepatic encephalopathy (MHE) was characterized for cognitive dysfunction. Insulin resistance (IR) has been identified to be correlated with the pathogenesis of MHE. Oridonin (Ori) is an active terpenoid, which has been reported to rescue synaptic loss and restore insulin sensitivity. In this study, we found that intraperitoneal injection of Ori rescued IR, reduced the autophagosome formation and synaptic loss and improved cognitive dysfunction in MHE rats. Moreover, in insulin-resistant PC12 cells and N2a cells, we found that Ori blocked IR-induced synaptic deficits via the down-regulation of PTEN, the phosphorylation of Akt and the inhibition of autophagy. Taken together, these results suggested that Ori displays therapeutic efficacy towards memory deficits via improvement of IR in MHE and represents a novel bioactive therapeutic agent for treating MHE.

Hydrogen sulphide ameliorates dopamine-induced astrocytic inflammation and neurodegeneration in minimal hepatic encephalopathy.

It has been demonstrated that the action of dopamine (DA) could enhance the production of tumour necrosis factor-α (TNF-α) by astrocytes and potentiate neuronal apoptosis in minimal hepatic encephalopathy (MHE). Recently, sodium hydrosulfide (NaHS) has been found to have neuroprotective properties. Our study addressed whether NaHS could rescue DA-challenged inflammation and apoptosis in neurons to ameliorate memory impairment in MHE rats and in the neuron and astrocyte coculture system. We found that NaHS suppressed DA-induced p65 acetylation, resulting in reduced TNF-α production in astrocytes both in vitro and in vivo. Furthermore, decreased apoptosis was observed in neurons exposed to conditioned medium from DA + NaHS-challenged astrocytes, which was similar to the results obtained in the neurons exposed to TNF-α + NaHS, suggesting a therapeutic effect of NaHS on the suppression of neuronal apoptosis via the reduction of TNF-α level. DA triggered the inactivation of p70 S6 ribosomal kinase (S6K1) and dephosphorylation of Bad, resulting in the disaggregation of Bclxl and Bak and the release of cytochrome c (Cyt. c), and this process could be reversed by NaHS administration. Our work demonstrated that NaHS attenuated DA-induced astrocytic TNF-α release and ameliorated inflammation-induced neuronal apoptosis in MHE. Further research into this approach may uncover future potential therapeutic strategies for MHE.

The inactivation of JAK2/STAT3 signaling and desensitization of M1 mAChR in minimal hepatic encephalopathy (MHE) and the protection of naringin against MHE.

BACKGROUND: We previously reported that elevation of intracranial dopamine (DA) levels from cirrhotic livers is implicated in the pathogenesis of minimal hepatic encephalopathy (MHE). Intracellular events in neurons, which lead to memory loss in MHE by elevated DA, however, remain elusive. METHODS: In our present study, an MHE rat model, a DA - intracerebroventricularly (i.c.v.) injected rat model and DA-treated primary cortical neurons (PCNs) were used to study this issue using behavioral tests, double-labeled fluorescent staining, immunoblotting, and semi-quantitative RT-PCR. RESULTS: Cognitive impairment was observed in MHE rats and DA (10 µg, i.c.v.)-treated rats. The levels of DA in the cerebral cortex of both MHE and DA (10 µg)-treated rats were increased. DA conversely modulated the p-JAK2/p-STAT3 levels in PCNs. In accordance, DA downregulated an anacetylcholine-producing enzyme, choline acetyltransferase (ChAT), and desensitized the M1-type muscarinic acetylcholine receptor (M1 mAChR). Furthermore, naringin completely restored cognitive function in MHE/DA (10 µg)-treated models by activating the JAK2/STAT3 axis, paralleling the upregulation of ChAT and sensitization of M1 mAChR. CONCLUSIONS: We propose a hypothesis accounting for memory impairment related to MHE: DA-dependent inactivation of the JAK2/STAT3 axis causes memory loss through cholinergic dysfunction. Our findings provide not only a novel pathological hallmark in MHE but also a novel target in MHE therapy.

Machine learning for the early prediction of acute respiratory distress syndrome (ARDS) in patients with sepsis in the ICU based on clinical data.

Background: Acute respiratory distress syndrome (ARDS) is a fatal outcome of severe sepsis. Machine learning models are helpful for accurately predicting ARDS in patients with sepsis at an early stage. Objective: We aim to develop a machine-learning model for predicting ARDS in patients with sepsis in the intensive care unit (ICU). Methods: The initial clinical data of patients with sepsis admitted to the hospital (including population characteristics, clinical diagnosis, complications, and laboratory tests) were used to predict ARDS, and screen out the crucial variables. After comparing eight different algorithms, namely, XG boost, logistic regression, light GBM, random forest, GaussianNB, complement NB, support vector machine (SVM), and K nearest neighbors (KNN), rebuilding a prediction model with the best one. When remodeling with the best algorithm, 10% was randomly selected to test, and the remaining was trained for cross-validation. Using the area under the curve (AUC), sensitivity, accuracy, specificity, positive and negative predictive value, F1 score, kappa value, and clinical decision curve to evaluate the model's performance. Eventually, the application in the model illustrated by the SHAP package. Results: Ten critical features were screened utilizing the lasso method, namely, PaO2/PAO2, A-aDO2, PO2(T), CRP, gender, PO2, RDW, MCH, SG, and chlorine. The prior ranking of variables demonstrated that PaO2/PAO2 was the most significant variable. Among the eight algorithms, the performance of the Gaussian NB algorithm was significantly better than that of the others. After remodeling with the best algorithm, the AUC in the training and validation sets were 0.777 and 0.770, respectively, and the algorithm performed well in the test set (AUC = 0.781, accuracy = 78.6%, sensitivity = 82.4%, F1 score = 0.824). A comparison of the overlap factors with those of previous models revealed that the model we developed performs better. Conclusion: Sepsis-associated ARDS can be accurately predicted early via a machine learning model based on existing clinical data. These findings are helpful for accurate identification and improvement of the prognosis in patients with sepsis-associated ARDS.

Targeting BMAL1 reverses drug resistance of acute myeloid leukemia cells and promotes ferroptosis through HMGB1-GPX4 signaling pathway.

PURPOSE: Acute myeloid leukemia (AML) is a refractory hematologic malignancy that poses a serious threat to human health. Exploring alternative therapeutic strategies capable of inducing alternative modes of cell death, such as ferroptosis, holds great promise as a viable and effective intervention. METHODS: We analyzed online database data and collected clinical samples to verify the expression and function of BMAL1 in AML. We conducted experiments on AML cell proliferation, cell cycle, ferroptosis, and chemotherapy resistance by overexpressing/knocking down BMAL1 and using assays such as MDA detection and BODIPY 581/591 C11 staining. We validated the transcriptional regulation of HMGB1 by BMAL1 through ChIP assay, luciferase assay, RNA level detection, and western blotting. Finally, we confirmed the results of our cell experiments at the animal level. RESULTS: BMAL1 up-regulation is an observed phenomenon in AML patients. Furthermore, there existed a strong correlation between elevated levels of BMAL1 expression and inferior prognosis in individuals with AML. We found that knocking down BMAL1 inhibited AML cell growth by blocking the cell cycle. Conversely, overexpressing BMAL1 promoted AML cell proliferation. Moreover, our research results revealed that BMAL1 inhibited ferroptosis in AML cells through BMAL1-HMGB1-GPX4 pathway. Finally, knocking down BMAL1 can enhance the efficacy of certain first-line cancer therapeutic drugs, including venetoclax, dasatinib, and sorafenib. CONCLUSION: Our research results suggest that BMAL1 plays a crucial regulatory role in AML cell proliferation, drug resistance, and ferroptosis. BMAL1 could be a potential important therapeutic target for AML.

Gold-catalyzed annulations of 2-alkynyl benzaldehydes with vinyl ethers: synthesis of dihydronaphthalene, isochromene, and bicyclo[2.2.2]octane derivatives.

With the suitable selection of a gold catalyst as well as the appropriate control of the reaction conditions, various new gold-catalyzed cyclizations of 2-alkynyl benzaldehyde with acyclic or cyclic vinyl ethers have been developed. Acetal-tethered dihydronaphthalene and isochromenes were obtained from the reactions of 2-alkynyl benzaldehydes with acyclic vinyl ethers under mild conditions. And, more interestingly, the gold-catalyzed reactions of 2-alkynyl benzaldehyde with a cyclic vinyl ether afforded the bicyclo[2.2.2]octane derivative involving two molecules of cyclic vinyl ethers. These products contain interesting substructures that have been found in many biologically active molecules and natural products. In addition, a gold-catalyzed homo-dimerization of 2-phenylethynyl benzaldehyde 1a was observed when the reaction was carried out in the absence of vinyl ether, affording a set of separable diastereomeric products. Plausible mechanisms for these transformations are discussed; a gold-containing benzopyrylium was regarded as the crucial intermediate by which a number of these new transformations took place.

Association study of OPRM1 polymorphisms with Schizophrenia in Han Chinese population.

BACKGROUND: The expression of µ-opioid receptor has important role in cognitive dysfunction in Schizophrenia (SZ). The results of studies about the association of polymorphisms of µ-opioid receptor gene (OPRM1) with SZ were inconsistent. METHODS: We conducted a case-control study to investigate the genetic association between OPRM1 polymorphisms and SZ among the Han chinese population. 264 SZ patients and 264 age-matched control subjects were recruited. Four SNPs of OPRM1 were successfully genotyped by using PCR-RFLP. RESULTS: Of four polymorphisms, rs1799971 and rs2075572 were shown to associate with SZ. Compared with the A allele of rs1799971 and C allele of rs2075572, the G allele of rs1799971 and rs2075572 was associated with an almost 0.46-fold risk (OR=0.46, 95% CI: 0.357-0.59, P<0.01) and 0.7-fold risk (OR=0.707, 95% CI: 0.534-0.937, P=0.015) of the occurrence of SZ,. When subjects were divided by gender, rs1799971 remained significant difference only in males (OR=0.309, 95% CI: 0.218-0.439 for G allele, P<0.01), and rs2075572 only in females (OR=0.399, 95% CI: 0.246-0.648 for G allele, P<0.01). In secondary analysis with subsets of patients, the G allele of rs1799971 (compared to the A allele) was associated with a decreased risk of all patients and male patients with apathy symptoms (OR=0.086, 95% CI: 0.048-0.151, P=0.01; OR=0.083, 95% CI: 0.045-0.153, P<0.01), and the G allele of rs2075572 (compared to the C allele) was associated with a decreased risk of all patients and female patients with positive family history (OR=0.468, 95% CI: 0.309-0.71, P<0.01; OR=0.34, 95% CI: 0.195-0.593, P<0.01). In addition, haplotype analysis revealed that two SNP haplotypes (A-C-C-G and G-C-C-A) were associated with decreased risks of SZ (P<0.01). The other two (G-C-C-G and G-G-C-G) with increased risks of SZ (P<0.01). CONCLUSIONS: The present study demonstrated for the first time that the OPRM1 polymorphism may be a risk factor for schizophrenia in the Han Chinese. Further studies are needed to give a global view of this polymorphism in pathogenesis of schizophrenia in a large-scale sample, family-based association design or well-defined subgroups of schizophrenia.

Recent advances in the isolation and reactivity of organogold complexes.

Homogeneous gold catalysis has been one of the most active research fields in organic chemistry for more than a decade, and it has been also among the most efficient methodologies for forming carbon-carbon or carbon-heteroatom bonds. Recently, a number of organogold intermediates were isolated from stoichiometric reactions, which helps to better understand the mechanisms. Meanwhile, the reactivity of organogold compounds has been attracting the attention of organic chemists in the field. This tutorial review collects the most recent advances in the isolation and reactivity of organogold compounds that may help to open new directions in homogenous gold catalysis.

Enhancing Freeze-Thaw Resistance of Alkali-Activated Slag by Metakaolin.

Incorporating metakaolin (MK) into slag to prepare alkali-activated materials can reduce shrinkage and improve the durability of alkali-activated slag (AAS). But its durability under freeze-thaw conditions is unknown. In this paper, the effects of MK content on the freeze-thaw properties of AAS were investigated from the perspective of gel composition and pore solution. The experimental results showed that the addition of MK generates a gel mixture of C-A-S-H and N-A-S-H with a cross-linked structure and decreases the content of bound water and pore water absorption. With the increase of alkali dosage, water absorption decreased to 0.28% and then increased to 0.97%, and the leaching rate of ions was Ca2+ > Al3+ > Na+ > OH-. When the alkali dosage was 8 wt % and the MK content was 30 wt %, the compressive strength loss rate of AAS was 0.58% and the mass loss rate was 0.25% after 50 freeze-thaw cycles.

Preparation of porous silica materials using a eucalyptus template method and its efficient adsorption of methylene blue.

Methylene blue (MB) is a prevalent pollutant in organic wastewater. For this research, eucalyptus wood was used as a template, into which quartz powder dissolved in NaOH was grown, resulting in a low-cost and efficient porous silica adsorbent material (PSAM). This PSAM successfully replaces expensive materials for MB removal from water. Through the application of Scanning Electron Microscopy (SEM) and Brunauer-Emmett-Teller (BET) analysis, it became evident that PSAM displays a porous slit pore structure characterized by numerous active sites, leading to an impressive maximum specific surface area of 88.05 m²/g. The central objective of this research was to investigate the impact of experimental temperature, initial dye concentration, and pH on the adsorption process. The adsorption kinetics were analyzed using the pseudo-first-order and pseudo-second-order models, as well as the Langmuir model. Remarkably, PSAM exhibited a substantial maximum adsorption capacity of 90.01 mg/g at 293 K, achieving an adsorption rate of over 85% within a mere 10-minute timeframe. The thermodynamic analysis revealed that the adsorption of MB onto PSAM was characterized by spontaneity and accompanied by heat absorption. Fourier Transform Infrared (FTIR) and SEM comparisons of PSAM before and after adsorption indicated that MB adsorption primarily occurred through electrostatic gravitational binding. In comparison to other adsorbents, PSAM exhibited exceptional efficacy in removing MB from water.

Heparin-induced thrombocytopenia associated with low-molecular-weight heparin: clinical feature analysis of cases and pharmacovigilance assessment of the FAERS database.

Background: Unfractionated heparin (UFH) and low-molecular-weight heparin (LMWH) are commonly used anticoagulants for the management of arterial and venous thromboses. However, it is crucial to be aware that LMWH can, in rare cases, lead to a dangerous complication known as heparin-induced thrombocytopenia (HIT). The objective of this study was to evaluate the pharmacovigilance and clinical features of HIT associated with LMWH, as well as identify treatment strategies and risk factors to facilitate prompt management. Methods: We extracted adverse event report data from the FDA Adverse Event Reporting System (FAERS) database for pharmacovigilance assessment. Case reports on LMWH-induced thrombocytopenia dated up to 20 March 2023 were collected for retrospective analysis. Results: Significantly elevated reporting rates of HIT were shown in adverse event (AE) data of LMWHs in the FAERS database, while tinzaparin had a higher proportional reporting ratio (PRR) and reporting odds ratio (ROR) than other LMWHs, indicating a greater likelihood of HIT. Case report analysis indicated that a total of 43 patients showed evidence of LMWH-induced thrombocytopenia with a median onset time of 8 days. Almost half of the events were caused by enoxaparin. LMWHs were mainly prescribed for the treatment of embolism and thromboprophylaxis of joint operation. Patients with a history of diabetes or surgery appeared to be more susceptible to HIT. Clinical symptoms were mostly presented as thrombus, skin lesion, and dyspnea. Almost 90% of the patients experienced a platelet reduction of more than 50% and had a Warkentin 4T score of more than 6, indicating a high likelihood of HIT. In all patients, LMWHs that were determined to be the cause were promptly withdrawn. Following the discontinuation of LMWHs, almost all patients were given alternative anticoagulants and eventually achieved recovery. Conclusion: LMWH-induced thrombocytopenia is rare but serious, with increased risk in patients with diabetes or a surgical history. Prompt recognition and management are crucial for the safe use of LMWHs.

Identification of common molecular signatures of SARS-CoV-2 infection and its influence on acute kidney injury and chronic kidney disease.

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is the main cause of COVID-19, causing hundreds of millions of confirmed cases and more than 18.2 million deaths worldwide. Acute kidney injury (AKI) is a common complication of COVID-19 that leads to an increase in mortality, especially in intensive care unit (ICU) settings, and chronic kidney disease (CKD) is a high risk factor for COVID-19 and its related mortality. However, the underlying molecular mechanisms among AKI, CKD, and COVID-19 are unclear. Therefore, transcriptome analysis was performed to examine common pathways and molecular biomarkers for AKI, CKD, and COVID-19 in an attempt to understand the association of SARS-CoV-2 infection with AKI and CKD. Three RNA-seq datasets (GSE147507, GSE1563, and GSE66494) from the GEO database were used to detect differentially expressed genes (DEGs) for COVID-19 with AKI and CKD to search for shared pathways and candidate targets. A total of 17 common DEGs were confirmed, and their biological functions and signaling pathways were characterized by enrichment analysis. MAPK signaling, the structural pathway of interleukin 1 (IL-1), and the Toll-like receptor pathway appear to be involved in the occurrence of these diseases. Hub genes identified from the protein-protein interaction (PPI) network, including DUSP6, BHLHE40, RASGRP1, and TAB2, are potential therapeutic targets in COVID-19 with AKI and CKD. Common genes and pathways may play pathogenic roles in these three diseases mainly through the activation of immune inflammation. Networks of transcription factor (TF)-gene, miRNA-gene, and gene-disease interactions from the datasets were also constructed, and key gene regulators influencing the progression of these three diseases were further identified among the DEGs. Moreover, new drug targets were predicted based on these common DEGs, and molecular docking and molecular dynamics (MD) simulations were performed. Finally, a diagnostic model of COVID-19 was established based on these common DEGs. Taken together, the molecular and signaling pathways identified in this study may be related to the mechanisms by which SARS-CoV-2 infection affects renal function. These findings are significant for the effective treatment of COVID-19 in patients with kidney diseases.

Machine learning for the prediction of all-cause mortality in patients with sepsis-associated acute kidney injury during hospitalization.

Background: Sepsis-associated acute kidney injury (S-AKI) is considered to be associated with high morbidity and mortality, a commonly accepted model to predict mortality is urged consequently. This study used a machine learning model to identify vital variables associated with mortality in S-AKI patients in the hospital and predict the risk of death in the hospital. We hope that this model can help identify high-risk patients early and reasonably allocate medical resources in the intensive care unit (ICU). Methods: A total of 16,154 S-AKI patients from the Medical Information Mart for Intensive Care IV database were examined as the training set (80%) and the validation set (20%). Variables (129 in total) were collected, including basic patient information, diagnosis, clinical data, and medication records. We developed and validated machine learning models using 11 different algorithms and selected the one that performed the best. Afterward, recursive feature elimination was used to select key variables. Different indicators were used to compare the prediction performance of each model. The SHapley Additive exPlanations package was applied to interpret the best machine learning model in a web tool for clinicians to use. Finally, we collected clinical data of S-AKI patients from two hospitals for external validation. Results: In this study, 15 critical variables were finally selected, namely, urine output, maximum blood urea nitrogen, rate of injection of norepinephrine, maximum anion gap, maximum creatinine, maximum red blood cell volume distribution width, minimum international normalized ratio, maximum heart rate, maximum temperature, maximum respiratory rate, minimum fraction of inspired O2, minimum creatinine, minimum Glasgow Coma Scale, and diagnosis of diabetes and stroke. The categorical boosting algorithm model presented significantly better predictive performance [receiver operating characteristic (ROC): 0.83] than other models [accuracy (ACC): 75%, Youden index: 50%, sensitivity: 75%, specificity: 75%, F1 score: 0.56, positive predictive value (PPV): 44%, and negative predictive value (NPV): 92%]. External validation data from two hospitals in China were also well validated (ROC: 0.75). Conclusions: After selecting 15 crucial variables, a machine learning-based model for predicting the mortality of S-AKI patients was successfully established and the CatBoost model demonstrated best predictive performance.

Identification and Validation of the Diagnostic Characteristic Genes of Ovarian Cancer by Bioinformatics and Machine Learning.

Background: Ovarian cancer (OC) has a high mortality rate and poses a severe threat to women's health. However, abnormal gene expression underlying the tumorigenesis of OC has not been fully understood. This study aims to identify diagnostic characteristic genes involved in OC by bioinformatics and machine learning. Methods: We utilized five datasets retrieved from the Gene Expression Omnibus (GEO) database, The Cancer Genome Atlas (TCGA) database, and the Genotype-Tissue Expression (GTEx) Project database. GSE12470 and GSE18520 were combined as the training set, and GSE27651 was used as the validation set A. Also, we combined the TCGA database and GTEx database as validation set B. First, in the training set, differentially expressed genes (DEGs) between OC and non-ovarian cancer tissues (nOC) were identified. Next, Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), Disease Ontology (DO) enrichment analysis, and Gene Set Enrichment Analysis (GSEA) were performed for functional enrichment analysis of these DEGs. Then, two machine learning algorithms, Least Absolute Shrinkage and Selector Operation (LASSO) and Support Vector Machine-Recursive Feature Elimination (SVM-RFE), were used to get the diagnostic genes. Subsequently, the obtained diagnostic-related DEGs were validated in the validation sets. Then, we used the computational approach (CIBERSORT) to analyze the association between immune cell infiltration and DEGs. Finally, we analyzed the prognostic role of several genes on the KM-plotter website and used the human protein atlas (HPA) online database to analyze the expression of these genes at the protein level. Results: 590 DEGs were identified, including 276 upregulated and 314 downregulated DEGs.The Enrichment analysis results indicated the DEGs were mainly involved in the nuclear division, cell cycle, and IL-17 signaling pathway. Besides, DEGs were also closely related to immune cell infiltration. Finally, we found that BUB1, FOLR1, and PSAT1 have prognostic roles and the protein-level expression of these six genes SFPR1, PSAT1, PDE8B, INAVA and TMEM139 in OC tissue and nOC tissue was consistent with our analysis. Conclusions: We screened nine diagnostic characteristic genes of OC, including SFRP1, PSAT1, BUB1B, FOLR1, ABCB1, PDE8B, INAVA, BUB1, TMEM139. Combining these genes may be useful for OC diagnosis and evaluating immune cell infiltration.