The Innovation

Given Chinas recent introduction of the "three-child policy" in response to population ageing1, safeguarding perinatal health has become an urgent priority2. Previous epidemiological research seldom explored the risk factors of maternal cardiovascular and haematological diseases, or its impact on adverse pregnancy outcomes (APO). To fill the literature gap, here we conducted systematic epidemiological analyses on 121,090 pregnant women and their neonates from the ZEBRA Chinese prospective maternity cohort. We find that incremental exposure in PM2.5, O3, and green space modify the risks of APO, including congenital heart disease, by 11.2%, 7.8%, and -5.5%, respectively. Maternal cardiovascular and haematological complications during pregnancy significantly aggravate the risk of APO by 66.2%, and also modify the environment-APO risk associations by amplifying the hazards of air pollution and weakening the protective effect of greenness accessibility. Our research findings support the Sustainable Development Goals (e.g. SDG3)3,4 by providing first-hand epidemiological evidence and clinical guidance for protecting maternal and neonatal health.

High-level exposure to indoor air pollutants (IAPs), including volatile organic compounds (VOCs), has substantially contributed to the burden of disease in China over the past two decades. However, the source contributions to the indoor VOC-related health burden remain unknown. This study utilized a novel approach based on positive matrix factorization (PMF) of indoor multipollutant data to estimate the source-specific residential VOC concentrations and associated burden of disease. Indoor concentrations of 39 VOCs were collected repeatedly in different seasons from 2016 to 2017 in 249 residences across nine cities in China. In 2017, the disability-adjusted life years (DALYs) attributable to residential VOC exposure across nine provinces in China reached 134.2 (95% UI: 65.7 - 225.0) per 100,000, resulting in financial costs of 28.1 (13.8 - 47.1) billion CNY. Contributions to indoor VOC concentrations from six indoor sources and three outdoor sources were derived by PMF. The top three sources, i.e., wood building materials and furniture, outdoor vehicle exhaust, and cooking and indoor combustion, accounted for 42.7%, 25.9%, and 11.0% of the VOC-attributable DALYs, which suggests prioritizing controlling these sources in China. This approach can be extended to other IAPs and provide fundamental data for future cost-benefit analysis of source control interventions. TOC Art O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=108 SRC="FIGDIR/small/25333590v1_ufig1.gif" ALT="Figure 1"> View larger version (54K): org.highwire.dtl.DTLVardef@1a69a9corg.highwire.dtl.DTLVardef@f07ec4org.highwire.dtl.DTLVardef@1129103org.highwire.dtl.DTLVardef@1ee68d1_HPS_FORMAT_FIGEXP M_FIG C_FIG SynopsisThis novel method leverages multi-seasonal and multi-room residential VOC measurements to identify emission sources, quantify source-specific exposure concentrations, and estimate source-specific health burden, thus prioritizing the sources needing control.

BackgroundEnvironmental factors, including air pollution, can strongly impact on spatio-temporal patterns of infectious diseases outbreak. In this study, we aimed to investigate the association and correlation between ambient air pollutants and case fatality rate (CFR) of the novel coronavirus disease (COVID-19) in China. MethodsPublicly accessible data on COVID-19 average CFR were utilized in the data analysis. The ambient daily air pollutants including fine particulate matter (PM2.5), inhalable particles (PM10) and nitrogen dioxide (NO2) during the period from December 25, 2019 to March 5, 2020 were obtained from National Air Quality Real-time Publishing System of China. Ecological analysis was performed to explore the association and correlation between the cumulative average exposure of ambient air pollutants at different lag days (14 and 28 days) and average CFR in China outside Hubei and cities in Hubei province via model fitting. ResultsThe average case fatality rate was highest in Wuhan city (4.53%) and the cumulative average exposure of ambient PM2.5, PM10 and NO2 at lag 28 days was 55.8{+/-}12.1g/m3, 66.8{+/-}9.2g/m3, 20.7{+/-}4.4g/m3, respectively in Hubei province during the study period. Ecological analysis showed that ambient PM2.5, PM10 and NO2 exposure at both lag 14 and 28 days was positively correlated with average CFR in China outside Hubei (province-level). For city-level analysis in Hubei, significant associations were only found between cumulative ambient NO2 exposure and average CFR(r=0.693 for Lag0-14, r=0.697 for Lag0-28, respectively) during the same period. ConclusionOur findings suggested ambient PM2.5, PM10 and NO2 exposure, especially at 28 lag days, positively associated with the case fatality rate of COVID-19 in China. These results could help provide guidance for identifying potential exposure window and preventing and controlling the epidemic.

Integrating non-invasive brain imaging techniques, particularly computed tomography (CT) and magnetic resonance imaging (MRI), coupled with the advancement of artificial intelligence, is forging a key pathway for brain disease diagnosis, playing a vital role in safeguarding human health1-4. A robust artificial intelligence copilot is essential for clinical emergencies, functioning as the central processing unit for brain medical imaging systems, aiming to revolutionize the imaging process, expedite the diagnosis of diseases, and support treatment5-7. In this study, we developed an advanced multi-modal brain medical imaging foundational model named Brainfound, utilizing AI-generated content and image-text alignment technology, pre-trained on over 3 million brain CT images and over 7 million brain MRI images with their paired reports. As a clinical brain medical imaging multi-modal model, Brainfound achieved state of the art on seven downstream tasks, including brain disease diagnosis, brain lesion segmentation, MRI image enhancement, MRI cross-modality translation, automatic report generation, zero-shot brain disease classification, and free human-AI conversation. After thorough human-machine validation, Brainfound surpassed the current leading model by 51.75% in automatic report generation for brain imaging. In multiple-choice questions related to brain imaging, the accuracy of Brainfound outstripped GPT-4V by 47.68%, comparable to experienced doctors. We anticipate Brainfound, a clinical model with flexible visual and text input-output capabilities, will provide substantial support in brain medical imaging, clinical education, and human-in-the-loop medical diagnosis.

The outbreak of the novel coronavirus disease, COVID-19, originating from Wuhan, China in early December, has infected more than 70,000 people in China and other countries and has caused more than 2,000 deaths. As the disease continues to spread, the biomedical society urgently began identifying effective approaches to prevent further outbreaks. Through rigorous epidemiological analysis, we characterized the fast transmission of COVID-19 with a basic reproductive number 5.6 and proved a sole zoonotic source to originate in Wuhan. No changes in transmission have been noted across generations. By evaluating different control strategies through predictive modeling and Monte carlo simulations, a comprehensive quarantine in hospitals and quarantine stations has been found to be the most effective approach. Government action to immediately enforce this quarantine is highly recommended.

Post-acute sequelae of SARS-CoV-2 infection (PASC) affects a wide range of organ systems among a large proportion of patients with SARS-CoV-2 infection. Although studies have identified a broad set of patient-level risk factors for PASC, little is known about the contextual and spatial risk factors for PASC. Using electronic health data of patients with COVID-19 from two large clinical research networks in New York City and Florida, we identified contextual and spatial risk factors from nearly 200 environmental characteristics for 23 PASC symptoms and conditions of eight organ systems. We conducted a two-phase environment-wide association study. In Phase 1, we ran a mixed effects logistic regression with 5-digit ZIP Code tabulation area (ZCTA5) random intercepts for each PASC outcome and each contextual and spatial factor, adjusting for a comprehensive set of patient-level confounders. In Phase 2, we ran a mixed effects logistic regression for each PASC outcome including all significant (false positive discovery adjusted p-value < 0.05) contextual and spatial characteristics identified from Phase I and adjusting for confounders. We identified air toxicants (e.g., methyl methacrylate), criteria air pollutants (e.g., sulfur dioxide), particulate matter (PM2.5) compositions (e.g., ammonium), neighborhood deprivation, and built environment (e.g., food access) that were associated with increased risk of PASC conditions related to nervous, respiratory, blood, circulatory, endocrine, and other organ systems. Specific contextual and spatial risk factors for each PASC condition and symptom were different across New York City area and Florida. Future research is warranted to extend the analyses to other regions and examine more granular contextual and spatial characteristics to inform public health efforts to help patients recover from SARS-CoV-2 infection.

The novel coronavirus (2019-nCoV) appeared in Wuhan in late 2019 have infected 34,598 people, and killed 723 among them until 8th February 2020. The new virus has spread to at least 316 cities (until 1st February 2020) in China. We used the traffic flow data from Baidu Map, and number of air passengers who left Wuhan from 1st January to 26th January, to quantify the potential infectious people. We developed multiple linear models with local population and air passengers as predicted variables to explain the variance of confirmed cases in every city across China. We found the contribution of air passengers from Wuhan was decreasing gradually, but the effect of local population was increasing, indicating the trend of local transmission. However, the increase of local transmission is slow during the early stage of novel coronavirus, due to the super strict control measures carried out by government agents and communities.

BackgroundHand, foot, and mouth disease (HFMD) represent a significant public health concern in the Asia-Pacific region, imposing a substantial burden that warrants urgent attention. However, the associations between individual-level exposure to ambient meteorological factors and the HFMD risk remain poorly understood. MethodsUsing a time-stratified case-crossover design, we examine the individual-level association between six meteorological factors (temperature, humidity, wind speed, radiation, surface pressure, and precipitation) and HFMD risk. Conditional logistic regression is employed to investigate the relationship between short-term exposure to meteorological factors and HFMD risk, considering lagged effects and adjusting for public holidays and time-varying grid-level HFMD susceptibility. Exposure-response curves are developed using natural cubic splines to model the non-linear associations, and then extreme meteorological exposure under different thresholds is considered. ResultsA total of 1,247,970 eligible cases are identified in the study. Each 1-unit increase in exposure to temperature, humidity, wind speed, and precipitation over a 10-day moving average (lag010) is associated with an elevated risk of HFMD, with odds ratios (ORs) of 1.0124 (95% CI: 1.0013, 1.0134), 1.0063 (95% CI: 1.0059, 1.0068), 1.0069 (95% CI: 1.0022, 1.0115), and 1.0081 (95% CI: 1.0069, 1.0092), respectively. In contrast, radiation and surface pressure exhibited a negative association with HFMD, with ORs of 0.9860 (95% CI: 0.9848, 0.9871) and 0.8780 (95% CI: 0.8660, 0.8900) at lag010. As the exposure thresholds for temperature, humidity, wind speed, and precipitation increase, the negative association between the excess magnitude and HFMD risk is strengthened, whereas the associations for radiation and surface pressure are reversed. ConclusionOur findings indicate that short-term exposure to most meteorological factors, except radiation and surface pressure, is associated with an elevated risk of HFMD, providing valuable insights for developing targeted preventive strategies and public health policies.

The co-existence of coronavirus disease 2019 (COVID-19) and seasonal influenza epidemics has become a potential threat to human health, particularly in China in the oncoming season. However, with the relaxation of nonpharmaceutical interventions (NPIs) during the COVID-19 pandemic, the rebound extent of the influenza activities is still poorly understood. In this study, we constructed a susceptible-vaccinated-infectious-recovered-susceptible (SVIRS) model to simulate influenza transmission and calibrated it using influenza surveillance data from 2018 to 2022. We projected the influenza transmission over the next 3 years using the SVIRS model. We observed that, in epidemiological year 2021-2022, the reproduction numbers of influenza in southern and northern China were reduced by 64.0% and 34.5%, respectively, compared with those before the pandemic. The percentage of people susceptible to influenza virus increased by 138.6% and 57.3% in southern and northern China by October 1, 2022, respectively. After relaxing NPIs, the potential accumulation of susceptibility to influenza infection may lead to a large-scale influenza outbreak in the year 2022-2023, the scale of which may be affected by the intensity of the NPIs. And later relaxation of NPIs in the year 2023 would not lead to much larger rebound of influenza activities in the year 2023-2024. To control the influenza epidemic to the pre-pandemic level after relaxing NPIs, the influenza vaccination rates in southern and northern China should increase to 53.8% and 33.8%, respectively. Vaccination for influenza should be advocated to reduce the potential reemergence of the influenza epidemic in the next few years.

In Alzheimers disease (AD), neuronal death is one of the key pathology. However, the initiation of neuronal death in AD is far from clear, and new targets are urgently needed to develop effective therapeutic methods. This study analyzed sequencing data from single-cell RNAseq and spatialomics, and revealed the impact of global single-cell mapping and cell spatial distribution relationships in early stage of AD. We found that microglia subpopulation Mic_PTPRG can anchor neurons based on ligand-receptor interaction pairs and cause ectopic expression of PTPRG in neurons during AD progression. PTPRG in neurons can bind and upregulate VIRMA expression, which in turn increases the level of m6A methylation, enhances PRKN transcript degradation and represses translation. Repressed PRKN expression blocks the clearance of damaged mitochondria in neurons, which in turn reprograms neuronal energy and nutrient metabolic pathways and leads to neuronal death during AD progression. This study elucidates novel mechanisms, by which the PTPRG-dependent microglia-synaptic modification may play a role in AD, providing a new scientific basis for potential therapeutic targets for AD.

Autism spectrum disorder (ASD) is a highly heritable neurodevelopmental disorder characterized by deficits in social interactions and repetitive behaviors. Although hundreds of ASD risk genes, implicated in synaptic formation and transcriptional regulation, have been identified through human genetic studies, the East Asian ASD cohorts is still under-represented in the genome-wide genetic studies. Here we performed whole-exome sequencing on 369 ASD trios including probands and unaffected parents of Chinese origin. Using a joint-calling analytical pipeline based on GATK toolkits, we identified numerous de novo mutations including 55 high-impact variants and 165 moderate-impact variants, as well as de novo copy number variations containing known ASD-related genes. Importantly, combining with single-cell sequencing data from the developing human brain, we found that expression of genes with de novo mutations were specifically enriched in pre-, post-central gyrus (PRC, PC) and banks of superior temporal (BST) regions in the human brain. By further analyzing the brain imaging data with ASD and health controls, we found that the gray volume of the right BST in ASD patients significantly decreased comparing to health controls, suggesting the potential structural deficits associated with ASD. Finally, we found that there was decrease in the seed-based functional connectivity (FC) between BST/PC/PRC and sensory areas, insula, as well as frontal lobes in ASD patients. This work indicated that the combinatorial analysis with genome-wide screening, single-cell sequencing and brain imaging data would reveal brain regions contributing to etiology of ASD.

BackgroundThe associations of PM2.5 mass and various adverse health outcomes have been widely investigated. However, fewer studies focused on the potential health impacts of PM2.5 components, especially for dementia and Alzheimers diseases (AD). MethodsWe constructed a nationwide population-based open cohort study among Medicare beneficiaries aged 65 or older during 2000-2018. This dataset was linked with the predicted levels of 15 PM2.5 components, including 5 major mass contributors (EC, OC, NH4+, NO3-, SO42-) and 10 trace elements (Br, Ca, Cu, Fe, K, Ni, Pb, Si, V, Zn) across contiguous US territory. Data were aggregated by ZIP code, calendar year and individual level demographics. Two mixture analysis methods, weighted quantile sum regression (WQS) and quantile g-computation (qgcomp), were used with quasi-Poisson models to analyze the health effects of the total mixture of PM2.5 components on dementia and AD, as well as the relative contribution of individual components. ResultsExposure to PM2.5 components over the previous 5 years was significantly associated with increased risks of both dementia and AD, with stronger associations observed for AD. SO42-, OC, Cu were identified with large contributions to the combined positive association of the mixture from both WQS and qgcomp models. ConclusionWe found positive associations between the 15 PM2.5 components and the incidence of dementia and AD. Our findings suggest that reducing PM2.5 emissions from traffic and fossil fuel combustion could help mitigate the growing burden of dementia and Alzheimers disease.

In late December 2019, Chinese authorities reported a cluster of pneumonia cases of unknown aetiology in Wuhan, China1. A novel strain of coronavirus named Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) was isolated and identified on 2 January 2020 2. Human-to-human transmission have been confirmed by a study of a family cluster and have occurred in health-care workers 3,4. Until 10 February 2020, 42638 cases of 2019 novel coronavirus disease (COVID-19) have been confirmed in China, of which 31728 came from Hubei Province (Figure). Wenzhou, as a southeast coastal city with the most cases outside Hubei Province, its policy control and epidemic projections have certain references for national and worldwide epidemic prevention and control. We described the epidemiologic characteristics of COVID-19 in Wenzhou and made a transmission model to predict the expected number of cases in the coming days.

Since COVID-19 emerged in early December, 2019 in Wuhan and swept across China Mainland, a series of large-scale public health interventions, especially Wuhan lock-down combined with nationwide traffic restrictions and Stay At Home Movement, have been taken by the government to control the epidemic. Based on Baidu Migration data and the confirmed cases data, we identified two key factors affecting the later (e.g February 27, 2020) cumulative confirmed cases in non-Wuhan region (y). One is the sum travelers from Wuhan during January 20 to January 26 (x1), which had higher infected probability but lower transmission ability because the human-to-human transmission risk of COVID-19 was confirmed and announced on January 20. The other is the "seed cases" from Wuhan before January 19, which had higher transmission ability and could be represented with the confirmed cases before January 29 (x2) due to a mean 10-day delay between infection and detection. A simple yet effective regression model then was established as follow: y= 70.0916+0.0054xx1+2.3455xx2 (n = 44, R2 = 0.9330, P<10-7). Even the lock-down date only delay or in advance 3 days, the estimated confirmed cases by February 27 in non-Wuhan region will increase 35.21% or reduce 30.74% - 48.59%. Although the above interventions greatly reduced the human mobility, Wuhan lock-down combined with nationwide traffic restrictions and Stay At Home Movement do have a determining effect on the ongoing spread of COVID-19 across China Mainland. The strategy adopted by China has changed the fast-rising curve of newly diagnosed cases, the international community should learn from lessons of Wuhan and experience from China. Efforts of 29 Provinces and 44 prefecture-level cities against COVID-19 were also assessed preliminarily according to the interpretive model. Big data has played and will continue playing an important role in public health.

ObjectiveTo evaluate long-term antemortem exposure to four pollutants in relation to Alzheimers disease (AD), cerebrovascular, and other dementia-related neuropathologies, measured at autopsy. DesignRetrospective cohort study. SettingIndividual participant data from four Rush Alzheimers Disease Center (RADC) longitudinal cohort studies: Memory and Aging Project, Minority Aging Research Study, Rush Clinical Core, and Latino Core. The cohorts enrolled participants residing in Chicago, Illinois (USA), including its metropolitan area and suburbs, and in outlying areas of Illinois. Participants909 decedents from the four RADC cohorts who underwent brain autopsy. SettingRADC cohorts are drawn from northeastern Illinois (IL) and Chicago metropolitan, suburban, and outlying areas of IL. ParticipantsAll participants were aged >60 years at enrollment. Analyses included 909 decedents with air pollution exposure measures who underwent autopsies prior to 2020 (of 3,579 who enrolled by the end of 2019); all autopsies were from community-based cohorts. ExposuresExposure to fine particulate matter (PM2.5; particles <2.5 m in aerodynamic diameter), nitrogen dioxide (NO2), oxides of nitrogen (NOx; nitrous oxide and NO2 combined), and ground-level ozone (O3) during the five years preceding death. Exposures were estimated with validated models developed for both the conterminous USA and the Chicago metropolitan area. Main Outcomes and MeasuresTwelve dementia-related neuropathologies measured by a neuropathologist at autopsy: Alzheimers disease neuropathology (ADNC), {beta}-amyloid density, tau tangle density, cerebral arteriolosclerosis, cerebral atherosclerosis, cerebral amyloid angiopathy, chronic cerebral infarctions (microscopic and gross), hippocampal sclerosis, Lewy bodies and limbic predominant age-related TDP-43 encephalopathy (LATE-NC). ResultsExposure to PM2.5 and NO2, as measured using Chicago-specific models, were both associated with higher tau tangle density [mean difference per 2.5 {micro}g/m3 PM2.5 = 0.25 tangles/mm2, (95% confidence interval [CI], -0.05 to 0.56); mean difference per 5 ppb NO2 = 0.10 tangles/mm2, (95% CI -0.07 to 0.28)]. PM2.5 exposure was associated with higher prevalence of arteriolosclerosis [prevalence ratio (PR) per 2.5 {micro}g/m3 = 1.51 (95% CI, 1.02 to 2.24)]. Both PM2.5 and NOx exposure were associated with higher prevalence of cerebral atherosclerosis [PR per 2.5 {micro}g/m3 PM2.5 = 1.41 (95% CI, 0.93 to 2.13); PR per 5 ppb NOx = 1.10 (95% CI, 0.98 to 1.23)]. None of the exposures was clearly adversely associated with the other neuropathologic outcomes, including {beta}-amyloid density and ADNC. Conclusion and RelevanceHigher exposure to PM2.5 was associated with cerebral arteriolosclerosis and atherosclerosis at death, consistent with the known vascular toxicity of this pollutant.

Glioblastoma (GBM) is a common malignancy featured by an extremely strong proliferation with enriched genetic information. Due to the rapid proliferation and the unstable genome-induced evolutionary potential, unique proteins may be expressed in GBM cells under the certain influence of the microenvironment. We therefore speculated that fluorescent proteins exist in GBM cells. During the immunofluorescence staining assay, we accidently discovered autofluorescence in primary GBM cells without fluorescent labeling, which were further validated as 2 newly discovered fluorescent proteins excited by 467 nm and 378 nm wavelength, respectively, namely human fluorescent protein and (HFP1, HFP2). Fluorescence colocalization and fluorescence resonance energy transfer (FRET) results showed the tight interaction of HFP1 and HFP2, and their synergistic effect. Our results for the first time identified 2 newly discovered fluorescent proteins in GBM cells, and clarified their chemical properties.

BackgroundVascular cognitive impairment (VCI) is one of the most common diseases among the elderly. However, few effective drugs have been approved for VCI. Traditional Chinese medicine (TCM) has been used in dementia for thousands of years. Currently, there is limited high-quality evidence for the efficacy of TCM, and the specific characteristics of its effects and the appropriate patient populations for TCM therapies remain unclear. Herein, we aim to explore the effectiveness and safety of TCM by conducting a longitudinal, patient-centered study. MethodsREgistry for Vascular cognitive Impairment trEatment With Traditional Chinese Medicine (REVIEW-TCM) is a prospective, observational disease registry study. 1000 VCI patients at the Hunan Hospital of Integrated Traditional Chinese and Western Medicine will be recruited based on the following criteria: aged 18 years or older, Montreal Cognitive Assessment (MoCA) score < 26, and Hachinski Ischemic Score (HIS)[&ge;]7. There is no strict limit on the intervention, and different TCM formulas will be focused. Cognition, activity of daily living, quality of life, mental, psychology, ZHENG of TCM, and burden of caregiver will be evaluated at admission, and 6, 12, 18, and 24 months. Meanwhile, biological tests and neuroimaging examination will be applied to further explore the mechanism of TCM. Especially, a mixed-methods embedded design will be applied by adopting quantitative and qualitative studies to explore patients-reported outcomes of TCM. Finally, propensity score matching will be adopted to analyze the effectiveness of TCM. DiscussionTo the best of our knowledge, the REVIEW-TCM study is the first comprehensive, prospective, mixed-methods, registry-based study to evaluate TCM treatment in VCI, which will analyze the effectiveness and safety of TCM in the real world and explore population characteristics and subtypes of VCI suitable for TCM. Study registrationThis study was registered on www.chictr.org.cn (ChiCTR2200064756).

BackgroundIn recent years, the impact of air pollution on the emergency departments of medical institutions has become increasingly evident. Emergency Ambulance Calls (EACs), compared to traditional health indicators such as mortality and hospitalization rates, provide a more direct reflection of the short-term effects of air pollution on public health. This study aims to explore the short-term association between the daily average concentrations of six major pollutants (PM2.5, PM10, SO2, NO2, CO, O3) and EACs related to respiratory diseases in the central urban areas of Shanghai. Methods: The Generalized Additive Model (GAM) was used to estimate the excess relative risk (ERR) of each pollutant on EACs at different lag times (0-7 days). Stratified analyses were also conducted based on age, time of day, and season. Results: 122,037 respiratory diseases related EACs were recorded during the study period. In different lag-day models, each interquartile range increase in pollutant concentration was associated with the highest single-day lag excess risk of EACs on the 6th day for all six pollutants, except for O3, which peaked on the 3rd day. The study found that individuals aged 65 and above are a vulnerable population to exposure. Specifically, in spring, PM2.5 on the 6th day of single-day lag was associated with a 3.19% (95% CI, 1.48-4.93%) increase in all-day EACs risk; PM10 on the 7th day of cumulative lag was associated with a 4.98% (95% CI, 1.35-8.74%) increase in daytime EACs risk; and O3 on the 3rd day of single-day lag was associated with a 3.60% increase in daytime EACs risk among the elderly (95% CI, 1.19-6.06%). Conclusion: This study indicates that even under the national ambient air pollutant concentration limits, air pollution could still serve as significant triggers for acute respiratory disease exacerbations. It is recommended that stricter air pollution control and early warning policies be implemented to reduce the occurrence of respiratory disease-related emergencies.

We tracked the effective reproduction number Rt of SARS-CoV-2 Omicron BF.7 in Beijing in November - December 2022 by fitting a transmission dynamic model parameterized with real-time mobility data to (i) the daily number of new symptomatic cases on November 1-11 (when the zero-covid interventions were still strictly enforced) and (ii) the proportion of individuals who participated in online polls on December 10-22 and self-reported to have been previously test-positive since November 1. After the announcement of "20 measures", we estimated that Rt increased to 3.44 (95% CrI: 2.82 - 4.14) on November 18 and the infection incidence peaked on December 11. The cumulative infection attack rate (i.e. the proportion of population who have been infected since November 1) was 43.1% (95% CrI: 25.6 - 60.9) on December 14 and 75.7% (95% CrI: 60.7 - 84.4) on December 22. Surveillance programmes should be rapidly set up to monitor the evolving epidemiology and evolution of SARS-CoV-2 across China.

Circulating tumor DNA (ctDNA) panels hold high promise of accurately predicting the therapeutic response of tumors while being minimally invasive and cost-efficient. However, their use has been limited to a small number of tumor types and patients. Here, we developed individualized ctDNA fingerprints suitable for most patients with multiple cancer types. The panels were designed based on individual whole-exome sequencing data in 521 Chinese patients and targeting high clonal population clusters of somatic mutations. Together, these patients represent 12 types of cancers and seven different treatments. The customized ctDNA panels have a median somatic mutation number of 19, most of which are patient-specific rather than cancer hotspot mutations; 66.8% of the patients were ctDNA-positive. We further evaluated the ctDNA content fraction (CCF) of the mutations, and analyzed the association between the change of ctDNA concentration and therapeutic response. We followed up 106 patients for clinical evaluation, demonstrating a significant correlation of changes in ctDNA with clinical outcomes, with a consistency rate of 93.4%. In particular, the median CCF increased by 204.6% in patients with progressive disease, decreased by 82.5% in patients with remission, and was relatively stable in patients with stable disease. Overall, 85% of the patients with a ctDNA-positive status experienced metastasis or relapse long before imaging detection, except for two patients who developed recurrence and metastasis almost simultaneously. The average lead time between the first ctDNA-positive finding and radiological diagnosis was 76 days in three patients that changed from a ctDNA-negative to -positive status. Our individualized ctDNA analysis can effectively monitor the treatment response, metastasis, and recurrence in multiple cancer types in patients with multiple treatment options, therefore offering great clinical applicability for improving personalized treatment in cancer.\n\nOne Sentence SummaryctDNA fingerprint panels were customized to predict the treatment response for multiple cancer types from individual whole-exome sequencing data.