Guangxi, a province in southwestern China, has the second highest reported number of HIV/AIDS cases in China. This study aimed to develop an accurate and effective model to describe the tendency of HIV and to predict its incidence in Guangxi. HIV incidence data of Guangxi from 2005 to 2016 were obtained from the database of the Chinese Center for Disease Control and Prevention. Long short-term memory (LSTM) neural network models, autoregressive integrated moving average (ARIMA) models, generalised regression neural network (GRNN) models and exponential smoothing (ES) were used to fit the incidence data. Data from 2015 and 2016 were used to validate the most suitable models. The model performances were evaluated by evaluating metrics, including mean square error (MSE), root mean square error, mean absolute error and mean absolute percentage error. The LSTM model had the lowest MSE when the N value (time step) was 12. The most appropriate ARIMA models for incidence in 2015 and 2016 were ARIMA (1, 1, 2) (0, 1, 2)₁₂and ARIMA (2, 1, 0) (1, 1, 2)₁₂, respectively. The accuracy of GRNN and ES models in forecasting HIV incidence in Guangxi was relatively poor. Four performance metrics of the LSTM model were all lower than the ARIMA, GRNN and ES models. The LSTM model was more effective than other time-series models and is important for the monitoring and control of local HIV epidemics.

As the most abundant messenger RNA (mRNA) modification, N⁶-methyladenosine (m⁶A) plays a crucial role in RNA fate, impacting cellular and physiological processes in various tumor types. However, our understanding of the role of the m⁶A methylome in tumor heterogeneity remains limited. Herein, we collected and analyzed m⁶A methylomes across nine human tissues from 97 m⁶A sequencing (m⁶A-seq) and RNA sequencing (RNA-seq) samples. Our findings demonstrate that m⁶A exhibits different heterogeneity in most tumor tissues compared to normal tissues, which contributes to the diverse clinical outcomes in different cancer types. We also found that the cancer type-specific m⁶A level regulated the expression of different cancer-related genes in distinct cancer types. Utilizing a novel and reliable method called “m⁶A-express”, we predicted m⁶A-regulated genes and revealed that cancer type-specific m⁶A-regulated genes contributed to the prognosis, tumor origin, and infiltration level of immune cells in diverse patient populations. Furthermore, we identified cell-specific m⁶A regulators that regulate cancer-specific m⁶A and constructed a regulatory network. Experimental validation was performed, confirming that the cell-specific m⁶A regulator CAPRIN1 controls the m⁶A level of TP53. Overall, our work reveals the clinical relevance of m⁶A in various tumor tissues and explains how such heterogeneity is established. These results further suggest the potential of m⁶A in cancer precision medicine for patients with different cancer types.

Monkeypox is a critical public health emergency with international implications. Few confirmed monkeypox cases had previously been reported outside endemic countries. However, since May 2022, the number of monkeypox infections has increased exponentially in non-endemic countries, especially in North America and Europe. The objective of this study was to develop optimal models for predicting daily cumulative confirmed monkeypox cases to help improve public health strategies. Autoregressive integrated moving average (ARIMA), exponential smoothing, long short-term memory (LSTM) and GM (1, 1) models were employed to fit the cumulative cases in the world, the USA, Spain, Germany, the UK and France. Performance was evaluated by minimum mean absolute percentage error (MAPE), among other metrics. The ARIMA (2, 2, 1) model performed best on the global monkeypox dataset, with a MAPE value of 0.040, while ARIMA (2, 2, 3) performed the best on the USA and French datasets, with MAPE values of 0.164 and 0.043, respectively. The exponential smoothing model showed superior performance on the Spanish, German and UK datasets, with MAPE values of 0.043, 0.015 and 0.021, respectively. In conclusion, an appropriate model should be selected according to the local epidemic characteristics, which is crucial for monitoring the monkeypox epidemic. Monkeypox epidemics remain severe, especially in North America and Europe, e.g. in the USA and Spain. The development of a comprehensive, evidence-based scientific programme at all levels is critical to controlling the spread of monkeypox infection.

Talaromyces marneffei tends to induce systemic infection in immunocompromised individuals, which is one of the causes of the high mortality. The underlying molecular mechanisms of T.marneffei-induced abnormal liver function are still poorly understood. In this study, we found that T.marneffei-infected patients could develop abnormal liver function, evidenced by reduced albumin and increased levels of aspartate aminotransferase (AST) and AST/alanine aminotransferase (ALT). T. marneffei-infected mice exhibited similar characteristics. In vitro investigations showed that T.marneffei induced the death of AML-12 cells. Furthermore, we determined that T.marneffei infection induced pyroptosis in hepatocytes of C57BL/6J mice and AML-12 cells, demonstrated by the increase of AIM2, caspase-1/-4, Gasdermin D(GSDMD) and pyroptosis-related cytokines in T.marneffei-infected mice/cells. Importantly, cell death was markedly suppressed in the presence of VX765 (an inhibitor of caspase-1/-4). Furthermore, in the presence of VX765, T.marneffei-induced pyroptosis was blocked. Nevertheless, necroptosis and apoptosis were also detected in infected animal model at 14 days post-infection. In conclusion, T.marneffei induces pyroptosis in hepatocytes through activation of the AIM2-caspase-1/-4-GSDMD axis, which may be an important cause of liver damage, and other death pathways including necroptosis and apoptosis may also be involved in the later stage of infection.

As acute infectious pneumonia, the coronavirus disease-2019 (COVID-19) has created unique challenges for each nation and region. Both India and the United States (US) have experienced a second outbreak, resulting in a severe disease burden. The study aimed to develop optimal models to predict the daily new cases, in order to help to develop public health strategies. The autoregressive integrated moving average (ARIMA) models, generalised regression neural network (GRNN) models, ARIMA–GRNN hybrid model and exponential smoothing (ES) model were used to fit the daily new cases. The performances were evaluated by minimum mean absolute per cent error (MAPE). The predictive value with ARIMA (3, 1, 3) (1, 1, 1)₁₄ model was closest to the actual value in India, while the ARIMA–GRNN presented a better performance in the US. According to the models, the number of daily new COVID-19 cases in India continued to decrease after 27 May 2021. In conclusion, the ARIMA model presented to be the best-fit model in forecasting daily COVID-19 new cases in India, and the ARIMA–GRNN hybrid model had the best prediction performance in the US. The appropriate model should be selected for different regions in predicting daily new cases. The results can shed light on understanding the trends of the outbreak and giving ideas of the epidemiological stage of these regions.

It is widely accepted that N⁶-methyladenosine (m⁶A) exhibits significant intercellular specificity, which poses challenges for its detection using existing m⁶A quantitative methods. In this study, we introduced Single-cell m⁶A Analysis (Scm⁶A), a machine learning-based approach for single-cell m⁶A quantification. Scm⁶A leverages input features derived from the expression levels of m⁶A trans regulators and cis sequence features, and offers remarkable prediction efficiency and reliability. To further validate the robustness and precision of Scm⁶A, we applied a winscore-based m⁶A calculation method to conduct N⁶-methyladenosine sequencing (m⁶A-seq) analysis on CD4⁺ and CD8⁺ T-cells isolated through magnetic-activated cell sorting (MACS). Subsequently, we employed Scm⁶A for analysis on the same samples. Notably, the m⁶A levels calculated by Scm⁶A exhibited a significant positive correlation with m⁶A quantified through m⁶A-seq in different cells isolated by MACS, providing compelling evidence for Scm⁶A’s reliability. Additionally, we performed single-cell level m⁶A analysis on lung cancer tissues as well as blood samples from the coronavirus disease 2019 (COVID-19) patients, and demonstrated the landscape and regulatory mechanisms of m⁶A in different T-cell subtypes from these diseases. In summary, our work has yielded a novel, dependable, and accurate method for single-cell m⁶A detection. We are confident that Scm⁶A have broad applications in the realm of m⁶A-related research.