Background:Magnesium homeostasis in chronic kidney disease (CKD) is complex, and serum magnesium concentrations reflect only approximately 1% of total body magnesium. Both magnesium deficiency (hypomagnesemia) and excess (hypermagnesemia) have been linked to adverse cardiovascular outcomes, a concern that is particularly relevant in patients with CKD. Magnesium oxide (MgO) is frequently prescribed for dyspepsia and constipation in clinical practice; however, its clinical impact in CKD patients remains uncertain and warrants further investigation. Materials and methods:We included non-dialysis CKD patients identified from the Taipei Medical University Clinical Research Database (TMUCRD) between 1998 and 2021. Adherence to MgO was assessed using the medication possession ratio (MPR). The primary outcomes were acute kidney injury (AKI), acute kidney disease (AKD), hospitalization for AKI, end-stage renal disease (ESRD) requiring dialysis, congestive heart failure with pulmonary edema, cardiac arrhythmia, and acute myocardial infarction. Baseline comorbidities assessed prior to the index date included hypertension, diabetes, hyperlipidemia, ischemic heart disease (IHD), ischemic stroke, congestive heart failure, atrial fibrillation (AF), peripheral arterial disease (PAD), chronic obstructive pulmonary disease (COPD), chronic liver disease (CLD), and dementia. These variables, along with relevant medications, were included as covariates in multivariable models to adjust for potential confounders. Results:Before matching, 6,105 MgO users and 10,143 non-users were identified; approximately 73% of MgO users had MPR <40%. In the ACE inhibitor (ACEI)/angiotensin receptor blocker (ARB) and pre-end-stage renal disease (pre-ESRD) program cohort, 207 MgO users and 1,401 non-users were included; after matching, 151 MgO users and 302 non-users remained. Dementia was more prevalent among MgO users, whereas diabetes was more common in non-users. MgO use was associated with higher risks of AKI, AKD, ESRD requiring dialysis, cardiac arrhythmia, and myocardial infarction in both unmatched and matched cohorts. In matched CKD patients, adjusted hazard ratios (aHRs) were 37.0 for AKI, 6.26 for AKD, 3.13 for ESRD, 2.06 for cardiac arrhythmia, and 1.86 for acute myocardial infarction. In the matched ACEI/ARB and pre-ESRD cohort, MgO users also demonstrated higher risks of AKI (aHR = 16.1) and AKD (aHR = 2.79). Cumulative incidence analyses consistently showed worse outcomes among MgO users. Among MgO users, advancing CKD stage was associated with progressively higher risks of adverse outcomes, particularly in stages 4-5. Both unmatched and matched analyses demonstrated a dose-response pattern, with the highest hazards observed for dialysis progression and cardiac arrhythmia. Conclusion:In this large cohort study, MgO use in CKD patients was associated with increased risks of AKI, AKD, ESRD, arrhythmia, and myocardial infarction. The magnitude of risk appeared greater in advanced CKD stages. These findings highlight the importance of careful risk-benefit assessment and close clinical monitoring when prescribing MgO in this high-risk population.

Chronic kidney disease–mineral and bone disorder (CKD-MBD) confers a substantial fracture burden that is only partly addressed by therapies targeting phosphate, parathyroid hormone, and vitamin D. Redox dysregulation may represent a complementary mechanism: reactive oxygen species (ROS) are required for receptor activator of nuclear factor-κB ligand (RANKL)-dependent osteoclastogenesis, whereas excessive ROS impair Wnt/β-catenin signalling in osteoblast precursors and promote osteocyte dysfunction. Uremic toxins, inflammation, and dialysis further increase oxidative stress. Molecular hydrogen (H2) is a highly diffusible redox modulator that has been proposed to limit damaging radical-chain reactions while preserving physiological oxidant signalling. In non-uremic skeletal models, H2 consistently suppresses osteoclast differentiation and bone loss, but evidence for osteoblast rescue is heterogeneous. In CKD and dialysis, H2-based interventions have shown signals of reduced oxidative stress and symptomatic benefit; however, human evidence is predominantly observational, and no study identified in this review assessed a bone-specific endpoint. We therefore integrate uremic bone redox biology with H2 pharmacology and propose a turnover-state-dependent model in which H2 may restrain excessive resorption in high-turnover disease, while its net effect in low-turnover adynamic bone remains uncertain because potential osteoblast rescue competes with anti-osteoclastic activity established only in non-uremic models. H2 should therefore be considered an experimental, mechanistically differentiated strategy requiring direct evaluation in uremic models and turnover-stratified clinical trials with parallel skeletal and vascular safety endpoints.

Doxorubicin (DOX) is a highly effective anthracycline widely used in cancer therapy but limited by its dose-dependent cardiotoxicity, which may result in arrhythmia, dilated cardiomyopathy, and heart failure. Conventional surveillance tools, including echocardiography and serum biomarkers, often identify injury only after substantial cardiac dysfunction has occurred. This underscores the need for early markers with mechanistic relevance. In this study, we developed an integrated zebrafish platform combining pathophysiological evaluation, electrocardiography (ECG), and transcriptomic profiling to establish a novel approach for early detection of DOX-induced cardiotoxicity (DIC). Consistent with human and mammalian models, DOX administration in adult zebrafish resulted in ventricular enlargement, myocardial fiber disarray, and elevated troponin I levels. ECG recordings revealed dose-dependent conduction disturbances, notably progressive PR interval and QRS prolongation, with P wave widening at higher doses. These findings identify the PR interval as a sensitive, early index of conduction impairment in the zebrafish DIC model, consistent with clinical reports linking PR prolongation to adverse outcomes. RNA sequencing further identified transcriptional pathways associated with conduction delay, with dysregulation of sodium channels (scn5lab, scn1lab), gap junction proteins (cx43, cx40.8), and transcriptional regulators (nkx2.5, tbx family). Notably, scn1lab expression declined progressively, cx43 and nkx2.5 were upregulated, showing temporal changes that co-occurred with the observed ECG and structural phenotypes. Together, these results support adult zebrafish as a scalable platform for cardiotoxicity screening and highlight PR interval prolongation as an early electrophysiological marker of DOX-associated conduction disturbance. The transcriptomic signatures are presented as correlative, hypothesis-generating candidates relevant to cardiac conduction and remodeling.

Differentiating life-threatening arrhythmias, such as ventricular tachycardia and supraventricular tachycardia, from non-threatening ones is crucial for clinical applications. This study aimed to develop a deep learning model to classify five key Electrocardiogram (ECG) patterns: normal sinus rhythm, sinus tachycardia, sinus bradycardia, supraventricular tachycardia, and ventricular tachycardia. We collected 1500 single-lead 10 s ECG signals from public datasets, including PhysioNet/Computing in Cardiology (CiC) Challenge 2020 and the Malignant Ventricular Ectopy Database, for training and 2297 ECGs for testing. Each 10 s signal was decomposed into 1 s sliding windows with a 5-point stride, which served as the input for the proposed deep learning architecture utilizing temporal attention and Time2Vec embedding. The model performance achieved an overall accuracy of 95.2%. For the five classes-supraventricular tachycardia, sinus tachycardia, normal sinus rhythm, ventricular tachycardia, and sinus bradycardia-the model achieved sensitivities of 90.3%, 92.9%, 97.4%, 100.0%, and 99.0% and accuracies of 96.3%, 95.8%, 98.9%, 99.9%, and 99.5%, respectively. Specificities for all rhythm categories exceeded 97.4%. This simple and effective single-lead model can significantly support the growing trend of home healthcare and professional clinical decision-making.

Background: Uric acid (UA) is linked to gout, renal dysfunction, and cardiovascular disease. Prior studies often assume linear relationships, potentially oversimplifying physiological complexity. Methods: We analyzed data from 5200 healthy Taiwanese men. Demographic, biochemical, lifestyle, and inflammatory variables were assessed using Pearson correlation, multiple linear regression (MLR), and multivariate adaptive regression splines (MARS), an interpretable machine learning method for detecting nonlinear, threshold-based effects. Results: Pearson correlation showed broad linear associations, whereas MARS identified fewer but more physiologically meaningful predictors. Waist-to-hip ratio (WHR) had a strong threshold effect, influencing UA only below 0.969. Creatinine showed a nonlinear impact, becoming substantial above 0.97 mg/dL, suggesting a renal threshold within the “normal” range. Calcium and high-sensitivity C-reactive protein (hs-CRP) each displayed inflection points (9.5 mg/dL and 3.38 mg/L, respectively), indicating range-specific effects. Notably, betel nut exposure, nonsignificant in linear models, emerged in MARS as a predictor with a complex, non-binary association with UA metabolism. Predictive performance was comparable (RMSE: 1.6694 for MARS vs. 1.6666 for MLR), but MARS offered superior interpretability by highlighting localized nonlinear effects. Conclusions: MARS modeling revealed critical nonlinear, threshold-dependent associations between UA and WHR, creatinine, calcium, hs-CRP, and betel nut exposure, which were not captured by conventional methods. These findings underscore the value of interpretable machine learning in metabolic research and suggest precise thresholds for clinical risk stratification.

IntroductionHerpes zoster (HZ) has been reported as a potential post-viral complication in individuals recovering from COVID-19, possibly due to virus-induced immune dysregulation. We aimed to investigate whether post-COVID HZ is associated with an elevated risk of hematologic or infectious complications.MethodsWe conducted a retrospective cohort study using the TriNetX global research network, which aggregates de-identified electronic health records from more than 140 healthcare institutions. Adults diagnosed with COVID-19 between January 2020 and January 2022 were stratified by the presence or absence of HZ within one year of infection and matched 1:1 by age, sex, and comorbidities. Outcomes including leukopenia, urinary tract infection, multiple myeloma, and acute leukemia were evaluated over a three-year follow-up using time-to-event and multivariable Cox regression analyses.ResultsIndividuals with post-COVID HZ had significantly higher risks of developing hematologic and infectious complications. Subgroup analyses identified older age, impaired kidney function, elevated inflammatory markers, and metabolic abnormalities as factors associated with greater risk.DiscussionThese findings suggest that HZ following COVID-19 may serve as a clinical indicator of immune vulnerability and heightened susceptibility to hematologic and infectious disorders. Long-term monitoring may be warranted in high-risk populations.

Anemia is a global health issue, especially in resource-limited areas, where traditional hemoglobin (Hb) testing is invasive and costly. This study aimed to develop an electrocardiogram–hemoglobin (ECG-Hb) deep learning model (DLM) for detecting anemia and assess its impact on all-cause mortality and new-onset heart failure. This retrospective study analyzed ECGs and corresponding Hb levels from two hospitals. The DLM was trained on 388,166 ECGs from 187,202 patients and tested on 24,279 and 29,247 patients in internal and external sets, respectively. Anemia was defined as moderate (Hb ≤ 10 g/dL) or severe (Hb ≤ 8 g/dL). Diagnostic performance was evaluated using receiver operating characteristic (ROC) curve analysis, and an 8-year follow-up assessed mortality and heart failure risk with Cox regression. The areas under the ROC curves (AUCs) for detecting moderate-to-severe anemia were 0.8545 (internal) and 0.8243 (external), with sensitivities of 65.9

Background: Early detection of dysglycemia in young adults is important but underexplored. This study aimed to (1) predict long-term changes in fasting plasma glucose (δ-FPG) and (2) classify future prediabetes using complementary machine learning (ML) approaches. Methods: We analyzed 6247 Taiwanese men aged 18–35 years (mean follow-up 5.9 years). For δ-FPG (continuous outcome), random forest, stochastic gradient boosting (SGB), eXtreme gradient boosting (XGBoost), and elastic net were compared with multiple linear regression using Symmetric mean absolute percentage error (SMAPE), Root mean squared error (RMSE), Relative absolute error(RAE), and Root relative squared error (RRSE) Sensitivity analyses excluded baseline FPG (FPGbase). Shapley additive explanations(SHAP) values provided interpretability, and stability was assessed across 10 repeated train–test cycles with confidence intervals. For prediabetes (binary outcome), an XGBoost classifier was trained on top predictors, with class imbalance corrected by SMOTE-Tomek. Calibration and decision-curve analysis (DCA) were also performed. Results: ML models consistently outperformed regression on all error metrics. FPGbase was the dominant predictor in full models (100% importance). Without FPGbase, key predictors included body fat, white blood cell count, age, thyroid-stimulating hormone, triglycerides, and low-density lipoprotein cholesterol. The prediabetes classifier achieved accuracy 0.788, precision 0.791, sensitivity 0.995, ROC-AUC 0.667, and PR-AUC 0.873. At a high-sensitivity threshold (0.2892), sensitivity reached 99.53% (specificity 47.46%); at a balanced threshold (0.5683), sensitivity was 88.69% and specificity was 90.61%. Calibration was acceptable (Brier 0.1754), and DCA indicated clinical utility. Conclusions: FPGbase is the strongest predictor of glycemic change, but adiposity, inflammation, thyroid status, and lipids remain informative. A dual interpretable ML framework offers clinically actionable tools for screening and risk stratification in young men.

Background The healing process after a myocardial infarction (MI) in humans involves complex events that replace damaged tissue with a fibrotic scar. The affected cardiac tissue may lose its function permanently. In contrast, zebrafish display a remarkable capacity for scar-free heart regeneration. Previous studies have revealed that syndecan-4 (SDC4) regulates inflammatory response and fibroblast activity following cardiac injury in higher vertebrates. However, whether and how Sdc4 regulates heart regeneration in highly regenerative zebrafish remains unknown.Methods and Results This study showed that sdc4 expression was differentially regulated during zebrafish heart regeneration by transcriptional analysis. Specifically, sdc4 expression increased rapidly and transiently in the early regeneration phase upon ventricular cryoinjury. Moreover, the knockdown of sdc4 led to a significant reduction in extracellular matrix protein deposition, immune cell accumulation, and cell proliferation at the lesion site. The expression of tgfb1a and col1a1a, as well as the protein expression of Fibronectin, were all down-regulated under sdc4 knockdown. In addition, we verified that sdc4 expression was required for cardiac repair in zebrafish via in vivo electrocardiogram analysis. Loss of sdc4 expression caused an apparent pathological Q wave and ST elevation, which are signs of human MI patients.Conclusions Our findings support that Sdc4 is required to mediate pleiotropic repair responses in the early stage of zebrafish heart regeneration.

BACKGROUNDBecause clinically used 12-lead electrocardiography (ECG) devices have high falsepositive errors in automatic interpretations of atrial fibrillation (AF), they require substantial improvements before use.OBJECTIVEA clinical 12-lead ECG pre-processing method with a parallel convolutional neural network (CNN) model for 12-lead ECG automatic AF recognition is introduced.METHODSRaw AF diagnosis data from a 12-lead ECG device were collected and analyzed by two cardiologists to differentiate between true- and false-positives. Using a stationary wavelet transform (SWT) and independent component analysis (ICA) noise reduction was conducted and baseline wandering was corrected for the raw signals. AF patterns were learned and predicted using a parallel CNN deep learning (DL) model. (1) The proposed method alleviates the decreased ECG QRS amplitude enhances the signal-to-noise ratio and clearly shows atrial and ventricular activities. (2) After training, the CNNbased AF detector significantly reduced false-positive errors. The precision of AF diagnosis increased from 77.3% to 94.0 ± 1.5% as compared to ECG device interpretation. For AF screening, the model showed an average sensitivity of 96.8 ± 2.2%, specificity of 79.0 ± 5.8%, precision of 94.0 ± 1.5%, F1-measure of 95.2 ± 1.0%, and overall accuracy of 92.7 ± 1.5%.CONCLUSIONSThe method can bridge the gap between the research and clinical practice The ECG signal pre-processing and DL-based AF interpretation can be rapidly implemented clinically.

BACKGROUND/AIM:Triple-negative breast cancer (TNBC) is an aggressive type of breast cancer that still requires improvement in treatment. Magnolol extract, derived from the bark of Magnolia officinalis, has traditionally been used in Asia to treat sleeping disorders and anxiety, and as an anti-inflammatory agent. Several reports have indicated that magnolol may have the potential to inhibit the progression of hepatocellular carcinoma and glioblastoma. However, the anti-tumor effect of magnolol on TNBC remains unknown.MATERIALS AND METHODS:In this study, we used two TNBC cell lines, MDA-MB-231 and 4T1, to examine the cytotoxicity, apoptosis, and metastasis effects of magnolol. These were evaluated using MTT assay, flow cytometry, western blotting, and invasion/migration transwell assay, respectively.RESULTS:Magnolol significantly induced cytotoxicity and extrinsic/intrinsic apoptosis in both TNBC cell lines. It also decreased metastasis and associated protein expression in a dose-dependent manner. Furthermore, the anti-tumor effect was associated with the inactivation of the epidermal growth factor receptor (EGFR)/Janus kinase (JAK)/signal transducer and activator of transcription (STAT3) signaling pathway.CONCLUSION:Magnolol may not only induce cell death in TNBC through apoptosis signaling activation but also by down-regulating EGFR/JAK/STAT3 signaling, which mediates TNBC progression.

BACKGROUND:The prevalence of type 2 diabetes (T2D) has been increasing dramatically in recent decades, and 47.5% of T2D patients will die of cardiovascular disease. Thallium-201 myocardial perfusion scan (MPS) is a precise and non-invasive method to detect coronary artery disease (CAD). Most previous studies used traditional logistic regression (LGR) to evaluate the risks for abnormal CAD. Rapidly developing machine learning (Mach-L) techniques could potentially outperform LGR in capturing non-linear relationships.AIM:To aims were: (1) Compare the accuracy of Mach-L methods and LGR; and (2) Found the most important factors for abnormal TMPS.METHODS:556 T2D were enrolled in the study (287 men and 269 women). Demographic and biochemistry data were used as independent variables and the sum of stressed score derived from MPS scan was the dependent variable. Subjects with a MPS score ≥ 9 were defined as abnormal. In addition to traditional LGR, classification and regression tree (CART), random forest, Naïve Bayes, and eXtreme gradient boosting were also applied. Sensitivity, specificity, accuracy and area under the receiver operation curve were used to evaluate the respective accuracy of LGR and Mach-L methods.RESULTS:Except for CART, the other Mach-L methods outperformed LGR, with gender, body mass index, age, low-density lipoprotein cholesterol, glycated hemoglobin and smoking emerging as the most important factors to predict abnormal MPS.CONCLUSION:Four Mach-L methods are found to outperform LGR in predicting abnormal TMPS in Chinese T2D, with the most important risk factors being gender, body mass index, age, low-density lipoprotein cholesterol, glycated hemoglobin and smoking.

Background: Boron neutron capture therapy (BNCT) is a radiotherapeutic approach that can destroy cancer cells while sparing the surrounding normal cells. Currently, boronophenylalanine (BPA) is the most common boron delivery agent used in BNCT for treating recurrent cancers of the head and neck, gliomas, and melanomas. On the other hand, valproic acid (VPA) is one of the representative class I histone deacetylase inhibitors (HDACi), which is a promising sensitizer for cancer therapies. In this study, we aimed to verify whether VPA could induce an enhanced effect in destroying melanoma cells in concurrence with BNCT and to explore the underlying mechanism of VPA-BNCT action in killing these cells. Materials and Methods: Murine melanoma B16-F10 cells were pre-treated with VPA and irradiated with neutron during BPA-BNCT. We explored the clonogenic assay and the expression of phosphorylated H2AX (gamma H2AX) for cell survival and DNA double-strand breaks (DSBs), respectively. We also examined the expression levels of DNA damage responses-associated proteins and performed a cell cycle analysis. Results: Our data indicated that the combination treatment of VPA and BNCT could significantly inhibit the growth of melanoma cells. Furthermore, VPA-BNCT treatment could exacerbate and perturb DNA DSBs in B16-F10 cells. In addition, pre-treatment of VPA abolished the G(2)/M arrest checkpoint caused by BNCT. Conclusion: Our results demonstrate that VPA has the potential to serve as a radiosensitizer of BPA-mediated BNCT for melanoma. These findings could improve BNCT treatments for melanoma.

Background/Aim: Osteosarcoma is an aggressive primary malignant bone tumor that occurs in childhood. Although the diagnostic and treatment options have been improved, osteosarcoma confers poor prognosis. Magnolol, an active component of Magnoliae officinalis cortex, has been widely applied in herb medicine and has been shown to have multiple pharmacological activities. However, whether magnolol possesses anti-osteosarcoma capacity remains unknown. Materials and Methods: We examined magnolol is cytotoxicity, and whether it regulates apoptosis and oncogene expression using MTT, flow cytometry and Western blotting assays in osteosarcoma cells. Results: Magnolol exerted toxicity towards U-2 OS cells by inducing intrinsic/extrinsic apoptosis pathways. Additionally, treatment of U-2 OS cells with magnolol inhibited MAPK1 mitogen-activated protein kinase 1 (ERK)/Nuclear factor kappa B (NF-B) signaling involved in tumor progression and reduced the expression of anti-apoptotic and metastasis-associated genes. Conclusion: Magnolol may induce apoptosis and inactivate ERK/NF-B signal transduction in osteosarcoma cells.

It is still a challenge to develop an electrocardiography (ECG) interpreter based on ECG basic characteristics because of the uncertainty of ECG delineation. Based on the clinical investigation in this study, ECG devices generated interpretations of Atrial Fibrillation (AF), Premature Ventricular Contraction (PVC), and Premature Atrial Contraction (PAC) have high ratios of false-positive errors. An ECG interpretation gap exists between ECG devices and cardiologists. This study aimed to develop an ECG interpreter to improve the performance of AF, PVC, and PAC based on clinical ECGs. This study first adopted a deep learning model to delineate ECG features such as P, QRS, and T waves based on 1160 8–10-s lead I or lead II ECG signals from a clinically-used 12-lead ECG device whose ECG device interpretation is AF as a training dataset. Second, a sliding window with 3-RR intervals in length is applied to the raw ECG to examine the delineated features in the window, and the ECG interpretation is then determined based on the experiences of cardiologists. The results indicate the following: (1) This delineator achieves good performance on P-, QRS-, and T- wave delineation with a sensitivity/specificity of 0.94/0.98, 1.00/0.99, and 0.97/0.98, respectively, in 48 10-s test ECGs mixed with true-positive AF and false-positive AF ECGs. (2) As compared to ECG-device generated interpretations, the precision of the detection of AF, PVC, and PAC in this study was increased from 0.77 to 0.86, 0.76 to 0.84, and 0.82 to 0.87 in 188 10-s test ECGs. Finally, (3) the F1 measure, which is a measure of the accuracy of test data but takes false-positive and false-negative into account, on the detection of AF, PVC, and PAC were 0.92, 0.91, and 0.83, respectively. In conclusion, this study overcomes the difficulties of ECG P-wave discrimination between true-positive AF and false-positive AF which are not documented well in previous research and improves the precision of ECG devices’ interpretation. We believe that this study can facilitate clinical applications of ECG, and bridge the gap between machines’ ECG interpretation and cardiologists.

Histamine is released from mast cells when tissues are inflamed or stimulated by allergens. Activation of histamine receptors and calcium influx via TRPV1 could be related to histamine-induced itch and skin inflammation. Quercetin is known to have anti-inflammatory and anti-itching effects. This study aims to understand whether quercetin can directly affect histamine-induced calcium influx in human keratinocyte. In it, we investigated quercetin, which acts on histamine-induced intracellular free calcium ([Ca2+]i) elevation in human keratinocyte. Changes in [Ca2+]i were measured using spectrofluorometry and confocal Imaging. We detected the expression of IL-8 after treatment of quercetin using qRT-PCR and evaluated its anti-itching effect in BALB/c mice. We also performed a docking study to estimate the binding affinity of quercetin to H4 receptors. We found that quercetin pretreatment decreased histamine-induced [Ca2+]i elevation in a concentration-dependent manner. The inhibitory effect of quercetin on histamine-induced [Ca2+]i elevation was blocked by JNJ7777120, a selective H4 antagonist, as well as by U73122, a PLC inhibitor, and by GF109203X, a PKC inhibitor. We also found that H4 agonist (4-methylhistamine)-induced [Ca2+]i elevation could be inhibited by quercetin. Moreover, the selective TRPV1 blocker capsazepine significantly suppressed the quercetin-mediated inhibition of histamine-induced [Ca2+]i elevation, whereas the TRPV4 blocker GSK2193874 had no effect. Last, quercetin decreased histamine and H4 agonist-induced IL-8 expression in keratinocyte and inhibited the scratching behavior-induced compound 48/80 in BALB/c mice. The molecular docking study also showed that quercetin exhibited high binding affinities with H4 receptors (autodock scores for H4 = -8.7 kcal/mol). These data suggest that quercetin could decrease histamine 4 receptor-induced calcium influx through the TRPV1 channel and could provide a molecular mechanism of quercetin in anti-itching, anti-inflammatory, and unpleasant sensations.

Atopic dermatitis (AD) is a chronic and persistent inflammatory skin disease characterized by eczematous lesions and itching, and it has become a serious health problem. However, the common clinical treatments provide limited relief and are accompanied by adverse effects. Therefore, there is a need to develop novel and effective therapies to treat AD. Neferine is a small molecule compound isolated from the green embryo of the mature seeds of lotus (Nelumbo nucifera). It has a bisbenzylisoquinoline alkaloid structure. Relevant studies have shown that neferine has many pharmacological and biological activities, including anti-inflammatory, anti-thrombotic, and anti-diabetic activities. However, there are very few studies on neferine in the skin, especially the related effects on inflammatory skin diseases. In this study, we proved that it has the potential to be used in the treatment of atopic dermatitis. Through in vitro studies, we found that neferine inhibited the expression of cytokines and chemokines in TNF-α/IFN-γ-stimulated human keratinocyte (HaCaT) cells, and it reduced the phosphorylation of MAPK and the NF-κB signaling pathway. Through in vivo experiments, we used 2,4-dinitrochlorobenzene (DNCB) to induce atopic dermatitis-like skin inflammation in a mouse model. Our results show that neferine significantly decreased the skin barrier damage, scratching responses, and epidermal hyperplasia induced by DNCB. It significantly decreased transepidermal water loss (TEWL), erythema, blood flow, and ear thickness and increased surface skin hydration. Moreover, it also inhibited the expression of cytokines and the activation of signaling pathways. These results indicate that neferine has good potential as an alternative medicine for the treatment of atopic dermatitis or other skin-related inflammatory diseases.

Insulin-like growth factor 2 mRNA-binding protein 1 (IGF2BP1) is an RNA-binding protein and serves as a post-transcriptional fine-tuner regulating the expression of mRNA targets. However, the clinicopathological roles of IGF2BP1 in colorectal cancer (CRC) remains limited. Thus, we aimed to elucidate the clinical significance and biomarker potentials of IGF2BP1 in CRC. A total of 266 specimens from two sets of CRC patients were collected. IGF2BP1 expression was studied by immunohistochemical (IHC) staining. The Kaplan-Meier survival plot and a log-rank test were used for survival analysis. The Cox proportional hazards model was applied to determine the survival impact of IGF2BP1. Public datasets sets from The Cancer Genome Atlas (TCGA) and Human Cancer Metastasis Database (HCMDB), receiver operating characteristic (ROC) plotter, and two CRC cell lines, HCT-116 and DLD-1, were used for validating our findings. We showed that IGF2BP1 was overexpressed in tumor specimens compared to 13 paired normal parts by examining the immunoreactivity of IGF2BP1 (p = 0.045). The increased expression of IGF2BP1 in primary tumor parts was observed regardless of metastatic status (p < 0.001) in HCMDB analysis. IGF2BP1 expression was significantly associated with young age (59.6% vs. 46.7%, p-value = 0.043) and advanced stage (61.3% vs. 40.0%, p-value = 0.001). After controlling for confounding factors, IGF2BP1 remained an independent prognostic factor (HR = 1.705, p-value = 0.005). TCGA datasets analysis indicated that high IGF2BP1 expression showed a lower 5-year survival rate (58% vs. 65%) in CRC patients. The increased expression of IGF2BP1 in chemotherapy non-responder rectal cancer patients was observed using a ROC plotter. Overexpression of IGF2BP1 promoted the colony-forming capacity and 5-fluorouracil and etoposide resistance in CRC cells. Here, IGF2BP1 was an independent poor prognostic marker in CRC patients and contributed to aggressive phenotypes in CRC cell lines.

Coronary artery diseases are major problems of the world. Coronary artery disease patients frequently suffer from peptic ulcers when they receive aspirin treatment. For diagnostic and therapeutic purposes, the implementation of panendoscopy (PES) with biopsy is necessary. Some biopsy samples are wasted after the assay is completed. In the present study, we established a protocol for human gastric fibroblast isolation and induced pluripotent stem cell (iPSC) generation from gastric fibroblasts via PES with biopsy. We showed that these iPSCs can be differentiated into functional cardiomyocytes in vitro. To our knowledge, this is the first study to generate iPSCs from gastric fibroblasts in vitro.