To the Editor—Severe fever with thrombocytopenia syndrome (SFTS), an emerging vector-borne disease, is caused by a novel bunyavirus belonging to the genus Phlebovirus [1, 2]. SFTS infections can be life-threatening and are characterized by sudden onset of fever, thrombocytopenia, gastrointestinal symptoms, and leukocytopenia. The tick Haemaphysalis longicornis is generally considered to be the vector of SFTS, which is widely distributed in China [2]. Person-to-person transmission through direct contact with contaminated blood has also been reported as a possible means of SFTS transmission [3–5]. Currently, there is no specific treatment other than supportive care [6].

Evidence on the burden of chronic obstructive pulmonary disease (COPD) morbidity attributable to the interaction between ambient air pollution and temperature has been limited. This study aimed to examine the modification effect of temperature on the association of ambient air pollutants (including particulate matter (PM) with aerodynamic diameter <10 μm (PM₁₀) and <2.5 μm (PM_2.5), nitrogen dioxide (NO₂), sulfur dioxide (SO₂), carbon monoxide (CO) and ozone (O₃)) with risk of hospital admissions (HAs) for COPD, as well as the associated morbidity burden in urban areas of Chengdu, China, from 2015 to 2016. Based on the generalized additive model (GAM) with quasi-Poisson link, bivariate response surface model and stratification parametric model were developed to investigate the potential interactions between ambient air pollution and temperature on COPD HAs. We found consistent interactions between ambient air pollutants (PM_2.5, PM₁₀ and SO₂) and low temperature on COPD HAs, demonstrated by the stronger associations between ambient air pollutants and COPD HAs at low temperatures than at moderate temperatures. Subgroup analyses showed that the elderly (≥80 years) and males were more vulnerable to this interaction. The joint effect of PM and low temperature had the greatest impact on COPD morbidity burden. Using WHO air quality guidelines as reference concentration, about 17.30% (95% CI: 12.39%, 22.19%) and 14.72% (95% CI: 10.38%, 19.06%) of COPD HAs were attributable to PM_2.5 and PM₁₀ exposures on low temperature days, respectively. Our findings suggested that low temperature significantly enhanced the effects of PM and SO₂ on COPD HAs in urban Chengdu, resulting in increased morbidity burden. This evidence has important implications for developing interventions to reduce the risk effect of COPD morbidity.

Hemorrhagic fever with renal syndrome (HFRS) is a rodent-borne disease caused by many serotypes of hantaviruses. In China, HFRS has been recognized as a severe public health problem with 90% of the total reported cases in the world. This study describes the spatiotemporal dynamics of HFRS cases in China and identifies the regions, time, and populations at highest risk, which could help the planning and implementation of key preventative measures. Data on all reported HFRS cases at the county level from January 2005 to December 2012 were collected from Chinese Center for Disease Control and Prevention. Geographic Information System-based spatiotemporal analyses including Local Indicators of Spatial Association and Kulldorff's space-time scan statistic were performed to detect local high-risk space-time clusters of HFRS in China. In addition, cases from high-risk and low-risk counties were compared to identify significant demographic differences. A total of 100,868 cases were reported during 2005–2012 in mainland China. There were significant variations in the spatiotemporal dynamics of HFRS. HFRS cases occurred most frequently in June, November, and December. There was a significant positive spatial autocorrelation of HFRS incidence during the study periods, with Moran's I values ranging from 0.46 to 0.56 (P<0.05). Several distinct HFRS cluster areas were identified, mainly concentrated in northeastern, central, and eastern of China. Compared with cases from low-risk areas, a higher proportion of cases were younger, non-farmer, and floating residents in high-risk counties. This study identified significant space-time clusters of HFRS in China during 2005–2012 indicating that preventative strategies for HFRS should be particularly focused on the northeastern, central, and eastern of China to achieve the most cost-effective outcomes. Hemorrhagic fever with renal syndrome (HFRS) is a rodent-borne viral disease caused by many serotypes of hantaviruses. In China, HFRS has been recognized as a severe public health problem and accounts for 90% of the reported cases in the world. We examined the spatiotemporal dynamics of HFRS cases in China during 2005–2012 and compared characteristics between cases from high-risk and low-risk counties. Several distinct HFRS cluster areas were identified, concentrated in northeastern, central, and eastern of China. Compared with cases from low-risk areas, a higher proportion of cases were younger, non-farmer, and floating residents in high-risk counties. These findings suggest preventative strategies for HFRS should be focused on the identified clusters in order to achieve the most cost-effective outcomes.

Scrub typhus is a vector-borne disease carried by the chigger mite. The aetiological agent is the rickettsia Orientia tsutsugamushi, which is endemic to several countries in the Asia-Pacific region, including China [1]. It is also a travel-associated disease [2] and of great importance among military personnel [3], [4]. During the Second World War, scrub typhus was associated with a higher case fatality ratio than any other infectious disease in the China-Burma-India theatre of operations 1,3. Clinical presentation in patients varies from asymptomatic to life-threatening disease [5], including acute hearing loss and multiple organ failure [6], [7]. To date, there is still no effective and reliable human vaccine against scrub typhus and no point-of-care diagnostics available [1]

Colorectal cancer (CRC) is one of the most common cancers worldwide. Accurate early detection and diagnosis, comprehensive assessment of treatment response, and precise prediction of prognosis are essential to improve the patients’ survival rate. In recent years, due to the explosion of clinical and omics data, and groundbreaking research in machine learning, artificial intelligence (AI) has shown a great application potential in clinical field of CRC, providing new auxiliary approaches for clinicians to identify high-risk patients, select precise and personalized treatment plans, as well as to predict prognoses. This review comprehensively analyzes and summarizes the research progress and clinical application value of AI technologies in CRC screening, diagnosis, treatment, and prognosis, demonstrating the current status of the AI in the main clinical stages. The limitations, challenges, and future perspectives in the clinical implementation of AI are also discussed.