Chinese Medical Journal最新文献

Background: Data on antiviral treatment for infants with chronic hepatitis B (CHB) are limited. This study is aimed to investigate whether and how antiviral treatment can achieve a functional cure in infants with CHB.

Methods: This real-world study enrolled 21 infants (9 boys and 12 girls) with active hepatitis B e antigen (HBeAg)-positive CHB from the Fifth Medical Center of Chinese PLA General Hospital, who were perinatally infected with hepatitis B virus (HBV) by mother-to-child transmission. The median baseline age was 9 months. Twenty-one cases initially received Lamivudine (LAM) monotherapy, and interferon-α (IFN-α) treatment was added when they were more than 12 months old. The virological responses, functional cure, and treatment safety were analyzed for 36 months (treatment plus follow-up visit duration).

Results: All 21 infants with CHB (baseline median HBV DNA was 7.75 log10 IU/mL) had full viral suppression on antiviral treatment, and the median time taken for undetectable HBV DNA was 6 months (range: 1-19 months). Correspondingly, the median time taken for HBeAg seroconversion was 7 months (range: 3-18 months) for 20 infants, and one case did not obtain HBeAg seroconversion; 19 of the 21 infants achieved a functional cure through a median time of 9 months (range: 4-27 months) since baseline. Two infants did not achieve a functional cure at the 36-month endpoint, but they achieved undetectable HBV DNA, and one of them had HBeAg seroconversion. Flu-like symptoms associated with IFN-α treatment were the common side effects; however, no serious adverse events were observed.

Conclusion: Our findings indicate that under 1-year-old infants with active CHB can achieve a significantly high probability of a functional cure when they receive a definite duration of antiviral treatment using LAM add-on IFN-α therapy.

Background: Family history (FH) of cancer is an established risk factor for early onset of cancer. However, reliable estimates on the difference in onset age between familial and sporadic cancers remain scarce in the Chinese population.

Methods: This multicenter, hospital-based, cross-sectional study included 23 hospitals across 12 provinces in China. Patients diagnosed with cancers of the lung, stomach, esophagus, or colorectum between January 1, 2016 and December 31, 2017 were identified. Detailed information on sociodemographic characteristics, lifestyle factors, stage at diagnosis, and onset age was collected. We analyzed the association between FH and onset age across different cancer types using quantile regressions.

Results: Among 41,072 eligible patients, 3054 (7.44%) reported a first-degree FH of cancer, and they were diagnosed at younger ages than those without FH (median difference: -1.19, 95% confidence interval [CI]: -1.59 to -0.79). Stratified by cancer type, the most pronounced difference was observed in colorectal cancer (median difference: -2.25, 95% CI: -3.31 to -1.19). Failure to account for lead time bias resulted in an overestimation of the FH effect, ranging from 3.4% to 15.4% across cancer types. Quantile regression analysis revealed that the impact of FH on age at diagnosis was more pronounced at the upper tail of the age distribution for all cancers combined and for each cancer type individually.

Conclusions: Our findings suggest that FH of cancer is associated with the early onset of lung, stomach, esophageal, and colorectal cancers in China. Cancer screening at earlier ages is needed for individuals with an FH.

Background: Organoids have attracted enormous interest in disease modeling, drug screening, and precision medicine. However, developing robust patient-derived organoids (PDOs) was time-consuming, costly, and had low success rates for certain cancer types, which limited their clinical utility. This study aimed to develop an interpretable deep learning-based model to predict the cultivation outcome of ovarian cancer organoids in advance.

Methods: Longitudinal microscopy images of 517 ovarian cancer organoid droplets were divided into training ( n = 325), validation ( n = 88), and test ( n = 104) sets. Subsequently, growth prediction models were developed based on four neural network backbones (ResNet18, VGG11, ConvNeXt v2, and Swin Transformer v2), and specific optimization methods were designed for better prediction. Finally, 179 samples from multiple centers were collected for prospective validation, and the gradient-weighted class activation mapping (Grad-CAM) method was used for interpretability analysis of the deep model to reveal the basis of the model's decisions.

Results: The test set showed that the deep learning models could achieve high-performance prediction at the third stage with area under the curve (AUC) values greater than 0.8 for all four models. The homogeneous transfer learning optimization method improved the AUC from 0.833 to 0.884 ( P = 0.0039). In prospective validation, the optimized model achieved an AUC of 0.832, a Brier score of 0.1919 in the calibration curve, and a greater net benefit in the decision curve. Interpretability analysis revealed that the area where organoids are being formed and have already formed is important for prediction.

Conclusions: Our developed models achieved satisfactory results in predicting the growth of ovarian cancer organoids. There is potential for further development of the model toward process automation.

Abstract: Sepsis remains a leading cause morbidity and mortality worldwide; effective targeted therapies remain elusive due to its inherent heterogeneity and dynamic temporal evolution. Existing frameworks often focus on either the diverse manifestations of sepsis or its progression over time, but fail to integrate these critical aspects. In this review, we propose a novel spatial-temporal framework that integrates both the heterogeneity and temporality of sepsis. The framework consists of two key dimensions: The cross-sectional (heterogeneity) dimension, which addresses pathogen variability, host factors, and pathogen-host interactions; The longitudinal (temporality) dimension, which explores the dynamic evolution of sepsis and the need for adaptive, real-time interventions. Given the complexity of multidimensional temporal data, big data techniques have the potential to integrate these data and decompose sepsis into distinct disease subtypes. Stratification facilitates the development of personalized therapeutic approaches tailored to specific subtypes. Moreover, methods, such as reinforcement learning, can track the dynamic transitions between these subtypes, enabling real-time adaptation of treatment strategies.

Abstract: Long interspersed nucleotide element-1 (LINE-1) is the only known reverse transcriptional transposon in the human genome with autonomous transposition capabilities. It can replicate and insert itself into new gene sites through the reverse transcription of transposons. The activation of LINE-1 is closely related to the occurrence and development of aging, cancer, and neurological diseases, and therefore has received widespread attention. However, research on LINE-1 in the nervous system is still in its early stages. Emerging evidence suggests that LINE-1 can undergo reverse transcriptional translocation and be regulated in neurons and neuroglial cells, playing a crucial role in neuronal diversity, neural plasticity, and behavioral phenotypes. In this review, we summarize the multifaceted functions of LINE-1 in neuronal function and evolution, synapse formation, and its implications for various neurological conditions, including neurodevelopmental disorders, neurodegenerative diseases, and emotional disorders. In addition, we also discuss the potential role of LINE-1 as a diagnostic biomarker and a therapeutic target in these neurological disorders. A comprehensive understanding of LINE-1's functions in the nervous system will enhance our insight into the pathogenesis of neurological diseases and may aid in the development of new therapeutic strategies.