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.

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.

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.

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.

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.

The national pediatric cancer surveillance data known as the pediatric cancer case report card(report card), had the characteristics of wide sources, diverse collection methods and a large amount of information. Based on the characteristics of the surveillance data, the National Center for Pediatric Cancer Surveillance (surveillance center) established quality control program for surveillance data according to the relevant norms and standards from China and other countries. The program defined the variables, requirements and rules for the quality control of surveillance data. The surveillance center designed different quality control processes according to the way of data reporting including manual filling/file import and port docking, and formulated a series of supporting measures to achieve the completeness, accuracy and standardization of surveillance data. By analyzing the report cards of patients discharged from hospital from 2021 to 2023, the surveillance center found that the number of problem report cards decreased from 40.6% (202 185 cards / 497 538 cards) before feedback to 31.1% (157 725 cards / 506 817 cards) after feedback. The data quality control program not only improved the quality of surveillance data, but also provided references for the establishment of the data quality control program of other registration systems of medical field.

Objective:To comprehensively understand the disease burden of liver cirrhosis and other chronic liver diseases caused by alcohol use in China from 1990 to 2019, as well as to predict the trends in disease burden from 2020 to 2030.Methods:The analysis utilized data from the Global Burden of Disease study in 2019 (GBD2019). Key indicators such as incidence rate, mortality rate, disability-adjusted life years (DALY), years of life lost due to premature mortality, and years lived with disability were selected to describe the disease burden of alcohol-related liver cirrhosis and other chronic liver diseases in China from 1990 to 2019. The estimated annual percentage change (EAPC) was used to depict the temporal trends in disease burden. Furthermore, a Bayesian age-period-cohort (BAPC) model was constructed using R software to predict the age-standardized incidence rate (ASIR) and age-standardized mortality rate (ASMR) of alcohol-related liver cirrhosis and other chronic liver diseases in China from 2020 to 2030.Results:From 1990 to 2019, the incidence of alcohol-related liver cirrhosis and other chronic liver diseases in China showed an upward trend, with an EAPC of 0.31% (95% CI: 0.10%-0.52%). However, the DALY declined, with an EAPC of -2.81% (95% CI: -2.92% - -2.70%). The ASMR showed a downward trend, with an EAPC of -2.55% (95% CI: -2.66% - -2.45%). The highest incidence of cirrhosis of liver caused by alcohol and other chronic liver diseases was reported in the age group of 35-49 years, while the ASMR increased gradually with age, with a significant rise after the age of 30. The age-standardized DALY rate peaked between the ages of 55 and 64. The disease burden indicators for males were consistently higher than those for females during the same period. According to the predictions of the BAPC model, from 2020 to 2030, the ASIR for cirrhosis of liver caused by alcohol and other chronic liver diseases in the entire population of China was projected to increase from 3.45/100 000 in 2020 to 3.78/100 000 in 2030, a growth of 9.57%. Conversely, the ASMR was expected to decrease from 1.45/100 000 in 2020 to 1.24/100 000 in 2030, a reduction of 14.48%. Conclusions:The disease burden of cirrhosis of liver caused by alcohol and other chronic liver diseases remained serious in China, especially in men and the middle-aged to elderly population. There is a pressing need to prioritize attention and resources towards these groups. Despite the projected decrease in ASMR, the ASIR continued to rise and is expected to persist in its upward trend until 2030.