AIM:To investigate the impact of Kir2.1 channels on abnormal spontaneous pacemaker activities induced by hypokalemia and to elucidate the underlying mechanisms.METHODS:Human induced pluripotent stem cell-derived cardiomyocytes(hiPSC-CMs)were transfected with lentiviral particles containing sequences for human Kir2.1,the Kir2.1-E224G mutant,or Kir4.1.Patch clamp techniques were employed to examine the effects of low extracellular potassium concentration([K+]e)of 1 mmol/L on the resting membrane potentials and whole-cell currents of the cells in each group,assessed via both current and voltage clamp modes.RESULTS:Under conditions of 1 mmol/L[K+]e,cur-rent clamp data revealed that hiPSC-CMs overexpressing Kir2.1 channels exhibited both hyperpolarized and depolarized resting membrane potentials,with the depolarized state triggering abnormal pacemaker activities.In contrast,cells overex-pressing the Kir2.1-E224G mutant or Kir4.1 channels displayed only hyperpolarized resting membrane potentials.Voltage clamp analysis indicated that hiPSC-CMs overexpressing Kir2.1 channels produced"N"-shaped whole-cell currents,whereas cells expressing the Kir2.1-E224G mutant or Kir4.1 exhibited typical K+currents.CONCLUSION:Kir2.1 channels play a crucial role in mediating hypokalemia-induced abnormal spontaneous pacemaker activities in human car-diomyocytes through their inward rectification properties.

Objective:To explore the effect of psoas muscle index (PMI) and construct a machine learning model to validate the 180-day prognosis in patients with decompensated liver cirrhosis.Methods:Retrospective data were collected from patients with decompensated liver cirrhosis at Henan Provincial People's Hospital from January 2022 to November 2022. The area of the psoas muscle index (PMI) at the level of the third lumbar vertebra was measured and calculated based on the abdominal X-ray computed tomography images stored in the Eastern China Hospital Information System (HIS). Patients were divided into low PMI and normal PMI groups according to the receiver operating characteristic curve. Patients clinical data and complication status were collected.The general conditions of both groups were compared using a t-test, chi-square test, and Mann-Whitney U test. The Kaplan-Meier method was applied for survival analysis. The outcome variable was 180-day mortality, and variables were selected using Cox and LASSO regression. The dataset was divided into training and testing sets in a 7∶3 ratio. Machine learning algorithms were used to build models in the training set, and model performance was validated by the test set. The model for MELD-Na score was compared with the model for End-Stage Liver Disease score. Results:A total of 298 patients with decompensated liver cirrhosis were included.The MELD scores, Child-Pugh classification, and NRS2002 scores, along with the incidence rate of complications such as ascites, hepatic encephalopathy, infections, and gastrointestinal bleeding, were significantly higher in the low PMI than the normal PMI group, with statistically significant differences ( P<0.05). The area under a receiver operating characteristic curve for the extreme gradient boosting model was higher than traditional clinical scores (MELD score 0.658, MELD_Na score 0.719) in the machine learning model. Furthermore, the application of SHAP results model indicated that PMI, hemoglobin, NRS2002 score, direct bilirubin, and blood ammonia were important factors in predicting the prognosis of patients with decompensated liver cirrhosis. Conclusion:A low PMI is closely related to poorer survival rates and the development of complication rates in patients with decompensated liver cirrhosis. The machine learning prediction model based on this construction, especially extreme gradient boosting, has favorable predictive performance, which is superior to the traditional clinical scoring system and can provide patients with the most accurate risk assessment and individualized treatment plan.

Depression is a severe mental illness. Although existing antidepressant medications have shown efficacy in many patients, a significant proportion show poor responses to current treatments, necessitating the need for novel therapeutic approaches. Photobiomodulation (PBM), an emerging physical therapy, has recently shown promising efficacy in the treatment of depression, as indicated by two systematic reviews, and is characterized by high safety and good tolerability. However, challenges remain in areas such as light delivery techniques and optimization of treatment parameters. This review summarizes the antidepressant mechanisms of PBM, various methods of light transmission, and recent research progress on its application in the treatment of depression. It also discusses issues related to safety, tolerability, and impact of specific parameters, aiming to provide insights and recommendations for future research on PBM as a therapy for depression.

BACKGROUND:Postoperative adjuvant chemotherapy is a common method for the treatment of gastric cancer,but the curative effect of chemotherapy in different patients varies considerably.A new pre-clinical treatment model is needed to guide personalized drug therapy for patients with gastric cancer.OBJECTIVE:To construct organoid model based on gastric cancer tissue and investigate its application in personalized drug screening.METHODS:The tissue samples of 20 patients with gastric cancer were collected,digested and decomposed,mixed with matrix glue,and cultured with organoid medium containing epidermal growth factor and fibroblast growth factor 10.Hematoxylin-eosin staining and immunohistochemical method were used to verify the homogeneity of pathological morphology and immune molecular markers of gastric cancer organoids and original tumor tissues.The feasibility of the established gastric cancer organoid model for drug screening was evaluated through drug sensitivity screening of six drugs including carboplatin,irinotecan,fluorouracil,oxaliplatin,paclitaxel,and epirubicin.RESULTS AND CONCLUSION:Fourteen organoids of gastric cancer cases were successfully cultured.There were individual differences in morphology and growth characteristics of organoids.All organoids could be stably passed through,froze and resuscitated.Gastric cancer organoids retained the same morphological features and immunomolecular expression as primary tumor tissues.Six organoids showed different drug sensitivities to six chemotherapy drugs,which initially confirmed the feasibility of gastric cancer organoids as a drug screening model in vitro.

BACKGROUND:Postoperative adjuvant chemotherapy is a common method for the treatment of gastric cancer,but the curative effect of chemotherapy in different patients varies considerably.A new pre-clinical treatment model is needed to guide personalized drug therapy for patients with gastric cancer.OBJECTIVE:To construct organoid model based on gastric cancer tissue and investigate its application in personalized drug screening.METHODS:The tissue samples of 20 patients with gastric cancer were collected,digested and decomposed,mixed with matrix glue,and cultured with organoid medium containing epidermal growth factor and fibroblast growth factor 10.Hematoxylin-eosin staining and immunohistochemical method were used to verify the homogeneity of pathological morphology and immune molecular markers of gastric cancer organoids and original tumor tissues.The feasibility of the established gastric cancer organoid model for drug screening was evaluated through drug sensitivity screening of six drugs including carboplatin,irinotecan,fluorouracil,oxaliplatin,paclitaxel,and epirubicin.RESULTS AND CONCLUSION:Fourteen organoids of gastric cancer cases were successfully cultured.There were individual differences in morphology and growth characteristics of organoids.All organoids could be stably passed through,froze and resuscitated.Gastric cancer organoids retained the same morphological features and immunomolecular expression as primary tumor tissues.Six organoids showed different drug sensitivities to six chemotherapy drugs,which initially confirmed the feasibility of gastric cancer organoids as a drug screening model in vitro.

AIM:To investigate the impact of Kir2.1 channels on abnormal spontaneous pacemaker activities induced by hypokalemia and to elucidate the underlying mechanisms.METHODS:Human induced pluripotent stem cell-derived cardiomyocytes(hiPSC-CMs)were transfected with lentiviral particles containing sequences for human Kir2.1,the Kir2.1-E224G mutant,or Kir4.1.Patch clamp techniques were employed to examine the effects of low extracellular potassium concentration([K+]e)of 1 mmol/L on the resting membrane potentials and whole-cell currents of the cells in each group,assessed via both current and voltage clamp modes.RESULTS:Under conditions of 1 mmol/L[K+]e,cur-rent clamp data revealed that hiPSC-CMs overexpressing Kir2.1 channels exhibited both hyperpolarized and depolarized resting membrane potentials,with the depolarized state triggering abnormal pacemaker activities.In contrast,cells overex-pressing the Kir2.1-E224G mutant or Kir4.1 channels displayed only hyperpolarized resting membrane potentials.Voltage clamp analysis indicated that hiPSC-CMs overexpressing Kir2.1 channels produced"N"-shaped whole-cell currents,whereas cells expressing the Kir2.1-E224G mutant or Kir4.1 exhibited typical K+currents.CONCLUSION:Kir2.1 channels play a crucial role in mediating hypokalemia-induced abnormal spontaneous pacemaker activities in human car-diomyocytes through their inward rectification properties.

Objective:To explore the effect of psoas muscle index (PMI) and construct a machine learning model to validate the 180-day prognosis in patients with decompensated liver cirrhosis.Methods:Retrospective data were collected from patients with decompensated liver cirrhosis at Henan Provincial People's Hospital from January 2022 to November 2022. The area of the psoas muscle index (PMI) at the level of the third lumbar vertebra was measured and calculated based on the abdominal X-ray computed tomography images stored in the Eastern China Hospital Information System (HIS). Patients were divided into low PMI and normal PMI groups according to the receiver operating characteristic curve. Patients clinical data and complication status were collected.The general conditions of both groups were compared using a t-test, chi-square test, and Mann-Whitney U test. The Kaplan-Meier method was applied for survival analysis. The outcome variable was 180-day mortality, and variables were selected using Cox and LASSO regression. The dataset was divided into training and testing sets in a 7∶3 ratio. Machine learning algorithms were used to build models in the training set, and model performance was validated by the test set. The model for MELD-Na score was compared with the model for End-Stage Liver Disease score. Results:A total of 298 patients with decompensated liver cirrhosis were included.The MELD scores, Child-Pugh classification, and NRS2002 scores, along with the incidence rate of complications such as ascites, hepatic encephalopathy, infections, and gastrointestinal bleeding, were significantly higher in the low PMI than the normal PMI group, with statistically significant differences ( P<0.05). The area under a receiver operating characteristic curve for the extreme gradient boosting model was higher than traditional clinical scores (MELD score 0.658, MELD_Na score 0.719) in the machine learning model. Furthermore, the application of SHAP results model indicated that PMI, hemoglobin, NRS2002 score, direct bilirubin, and blood ammonia were important factors in predicting the prognosis of patients with decompensated liver cirrhosis. Conclusion:A low PMI is closely related to poorer survival rates and the development of complication rates in patients with decompensated liver cirrhosis. The machine learning prediction model based on this construction, especially extreme gradient boosting, has favorable predictive performance, which is superior to the traditional clinical scoring system and can provide patients with the most accurate risk assessment and individualized treatment plan.

Depression is a severe mental illness. Although existing antidepressant medications have shown efficacy in many patients, a significant proportion show poor responses to current treatments, necessitating the need for novel therapeutic approaches. Photobiomodulation (PBM), an emerging physical therapy, has recently shown promising efficacy in the treatment of depression, as indicated by two systematic reviews, and is characterized by high safety and good tolerability. However, challenges remain in areas such as light delivery techniques and optimization of treatment parameters. This review summarizes the antidepressant mechanisms of PBM, various methods of light transmission, and recent research progress on its application in the treatment of depression. It also discusses issues related to safety, tolerability, and impact of specific parameters, aiming to provide insights and recommendations for future research on PBM as a therapy for depression.

Coronary artery calcifications (CAC) is an independent risk factor for cardiovascular disease (CVD). It has been revealed that this condition can be automatically quantified through computerize tomographic (CT) scan contained in radiotherapy plan for patients with breast cancer, with which, physicians can identify the patients with increased risk of CVD after radiotherapy prematurely and take intervention measures in advance. In this article, the current literature and research progress on the correlation between CAC and cardiotoxicity in patients with breast cancer after radiotherapy were reviewed, expecting to provide a strategy to reduce the CVD risk in patients with breast cancer after radiotherapy.

BackgroundPrevious investigations have illuminated the correlation between smoking and C-reactive protein （CRP）， but previous research findings may be influenced by other confounding factors. The causal relationship of CRP in smoking-related pathological process requires further exploration. ObjectiveTo investigate the causal relationship between smoking behavior and CRP by utilizing the cumulative statistical data from existing genome-wide association studies （GWAS）， so as to provide references for formulating relevant public health policies and smoking intervention measures. MethodsThis research utilized the GWAS summary statistics for CRP and four smoking phenotypes： age of initiation of regular smoking， smoking initiation， smoking cessation and cigarettes per day-selecting independent genetic loci correlated with smoking and CRP as instrumental variables. The study employed the inverse variance weighted method （IVW） and the weighted median approach for two-sample Mendelian randomization （MR） analysis to explore the bidirectional causal relationship between smoking and CRP. The Cochran's Q test was applied to assess heterogeneity among single nucleotide polymorphisms （SNPs）. MR pleiotropy residual sum and outlier was used to detect SNP outliers. MR-Egger intercept test examined the horizontal pleiotropy of SNPs. Leave-one-out sensitivity analysis assessed the impact of individual SNP on the Mendelian randomization results. ResultsThe MR analysis revealed a bidirectional causal relationship between CRP and smoking initiation （β=0.170， P=0.01） （with smoking initiationas the exposure）， （β=0.040， P=0.001） （with CRP as the exposure）. ConclusionSmoking may lead to alterations in CRP levels， while changes in CRP levels could also influence individual's propensity to initiate smoking.