We conducted a systematic literature review to obtain risk population-based fungal disease incidence or prevalence data from China. Data were categorized by risk factors and extrapolated by using ...most recent demographic figures. A total of 71,316,101 cases (5.0% of the population) were attributed to 12 risk factors and 17 fungal diseases. Excluding recurrent Candida vaginitis (4,057/100,000 women) and onychomycosis (2,600/100,000 persons), aspergillosis (317/100,000 persons) was the most common problem; prevalence exceeded that in most other countries. Cryptococcal meningitis, an opportunistic infection, occurs in immunocompetent persons almost twice as often as AIDS. The pattern of fungal infections also varies geographically; Talaromyces marneffei is distributed mainly in the Pearl River Basin, and the Yangtze River bears the greatest histoplasmosis burden. New host populations, new endemic patterns, and high fungal burdens in China, which caused a huge impact on public health, underscore the urgent need for building diagnostic and therapeutic capacity.
Metabolic reprogramming is a hallmark of cancer. However, systematic characterizations of metabolites in triple-negative breast cancer (TNBC) are still lacking. Our study profiled the polar ...metabolome and lipidome in 330 TNBC samples and 149 paired normal breast tissues to construct a large metabolomic atlas of TNBC. Combining with previously established transcriptomic and genomic data of the same cohort, we conducted a comprehensive analysis linking TNBC metabolome to genomics. Our study classified TNBCs into three distinct metabolomic subgroups: C1, characterized by the enrichment of ceramides and fatty acids; C2, featured with the upregulation of metabolites related to oxidation reaction and glycosyl transfer; and C3, having the lowest level of metabolic dysregulation. Based on this newly developed metabolomic dataset, we refined previous TNBC transcriptomic subtypes and identified some crucial subtype-specific metabolites as potential therapeutic targets. The transcriptomic luminal androgen receptor (LAR) subtype overlapped with metabolomic C1 subtype. Experiments on patient-derived organoid and xenograft models indicate that targeting sphingosine-1-phosphate (S1P), an intermediate of the ceramide pathway, is a promising therapy for LAR tumors. Moreover, the transcriptomic basal-like immune-suppressed (BLIS) subtype contained two prognostic metabolomic subgroups (C2 and C3), which could be distinguished through machine-learning methods. We show that N-acetyl-aspartyl-glutamate is a crucial tumor-promoting metabolite and potential therapeutic target for high-risk BLIS tumors. Together, our study reveals the clinical significance of TNBC metabolomics, which can not only optimize the transcriptomic subtyping system, but also suggest novel therapeutic targets. This metabolomic dataset can serve as a useful public resource to promote precision treatment of TNBC.
The interactions between non-coding RNAs (ncRNAs) and proteins play an important role in many biological processes, and their biological functions are primarily achieved by binding with a variety of ...proteins. High-throughput biological techniques are used to identify protein molecules bound with specific ncRNA, but they are usually expensive and time consuming. Deep learning provides a powerful solution to computationally predict RNA-protein interactions. In this work, we propose the RPI-SAN model by using the deep-learning stacked auto-encoder network to mine the hidden high-level features from RNA and protein sequences and feed them into a random forest (RF) model to predict ncRNA binding proteins. Stacked assembling is further used to improve the accuracy of the proposed method. Four benchmark datasets, including RPI2241, RPI488, RPI1807, and NPInter v2.0, were employed for the unbiased evaluation of five established prediction tools: RPI-Pred, IPMiner, RPISeq-RF, lncPro, and RPI-SAN. The experimental results show that our RPI-SAN model achieves much better performance than other methods, with accuracies of 90.77%, 89.7%, 96.1%, and 99.33%, respectively. It is anticipated that RPI-SAN can be used as an effective computational tool for future biomedical researches and can accurately predict the potential ncRNA-protein interacted pairs, which provides reliable guidance for biological research.
With the assistance of microwave irradiation, greenish‐yellow luminescent graphene quantum dots (gGQDs) with a quantum yield (QY) up to 11.7% are successfully prepared via cleaving graphene oxide ...(GO) under acid conditions. The cleaving and reduction processes are accomplished simultaneously using microwave treatment without additional reducing agent. When the gGQDs are further reduced with NaBH4, bright blue luminescent graphene quantum dots (bGQDs) are obtained with a QY as high as 22.9%. Both GQDs show well‐known excitation‐dependent PL behavior, which could be ascribed to the transition from the lowest unoccupied molecular orbital (LUMO) to the highest occupied molecular orbital (HOMO) with a carbene‐like triplet ground state. Electrochemiluminescence (ECL) is observed from the graphene quantum dots for the first time, suggesting promising applications in ECL biosensing and imaging. The ECL mechanism is investigated in detail. Furthermore, a novel sensor for Cd2+ is proposed based on Cd2+ induced ECL quenching with cysteine (Cys) as the masking agent.
Two‐color graphene quantum dots are prepared using a facile microwave‐assisted approach to have fluorescent quantum yields as high as 22.9%. The graphene quantum dots are demonstrated to be electrochemiluminescent. A novel electrochemiluminescence sensor for Cd2+ is proposed based on the competitive coordination between cysteine and graphene quantum dots for metal ions.
The development of versatile nanotheranostic platforms that integrate both diagnostic and therapeutic functions have always been an intractable challenge in precise cancer treatment. Herein, an ...aptamer‐tethered deoxyribonucleic acids‐gold particle (Apt‐DNA‐Au) nanomachine has been developed for in situ imaging and targeted multimodal synergistic therapy of mammary carcinoma. Upon specifically internalized into MCF‐7 cells, the tumor‐related TK1 mRNA activates the Apt‐DNA‐Au nanomachine by DNA strand displacement cascades, resulting in the release of the fluorophore and antisense DNA as well as the aggregation of AuNPs for in situ imaging, suppression of survivin expression and photothermal therapy, respectively. Meanwhile, the controlled released drugs are used for chemotherapy, while under the laser irradiation the loaded photosensitizer produces reactive oxygen species (ROS) for photodynamic therapy. The results confirm that the proposed Apt‐DNA‐Au nanomachine provides a powerful nanotheranostic platform for in situ imaging‐guided combinatorial anticancer therapy.
A multifunctional DNA‐Au nanomachine which can be triggered by endogenous tumor growth‐related TK1 mRNA has been devised as the combinatorial theranostic agent for fluorescence imaging‐guided chemo, genic, photodynamic, and photothermal synergistic targeted therapy of breast cancer. This theranostic nanoplatform achieves the significant inhibition of tumor growth and improvement of therapeutic efficacy through in situ imaging.
Nationwide nonpharmaceutical interventions (NPIs) have been effective at mitigating the spread of the novel coronavirus disease (COVID-19), but their broad impact on other diseases remains ...under-investigated. Here we report an ecological analysis comparing the incidence of 31 major notifiable infectious diseases in China in 2020 to the average level during 2014-2019, controlling for temporal phases defined by NPI intensity levels. Respiratory diseases and gastrointestinal or enteroviral diseases declined more than sexually transmitted or bloodborne diseases and vector-borne or zoonotic diseases. Early pandemic phases with more stringent NPIs were associated with greater reductions in disease incidence. Non-respiratory diseases, such as hand, foot and mouth disease, rebounded substantially towards the end of the year 2020 as the NPIs were relaxed. Statistical modeling analyses confirm that strong NPIs were associated with a broad mitigation effect on communicable diseases, but resurgence of non-respiratory diseases should be expected when the NPIs, especially restrictions of human movement and gathering, become less stringent.
Abstract
Background
The high cost and insufficient supply of human papillomavirus (HPV) vaccines have slowed the pace of controlling cervical cancer. A phase III clinical trial was conducted to ...evaluate the efficacy, safety, and immunogenicity of a novel Escherichia coli-produced bivalent HPV-16/18 vaccine.
Methods
A multicenter, randomized, double-blind trial started on November 22, 2012 in China. In total, 7372 eligible women aged 18–45 years were age-stratified and randomly assigned to receive three doses of the test or control (hepatitis E) vaccine at months 0, 1, and 6. Co-primary endpoints included high-grade genital lesions and persistent infection (over 6 months) associated with HPV-16/18. The primary analysis was performed on a per-protocol susceptible population of individuals who were negative for relevant HPV type-specific neutralizing antibodies (at day 0) and DNA (at day 0 through month 7) and who received three doses of the vaccine. This report presents data from a prespecified interim analysis used for regulatory submission.
Results
In the per-protocol cohort, the efficacies against high-grade genital lesions and persistent infection were 100.0% (95% confidence interval = 55.6% to 100.0%, 0 of 3306 in the vaccine group vs 10 of 3296 in the control group) and 97.8% (95% confidence interval = 87.1% to 99.9%, 1 of 3240 vs 45 of 3246), respectively. The side effects were mild. No vaccine-related serious adverse events were noted. Robust antibody responses for both types were induced and persisted for at least 42 months.
Conclusions
The E coli-produced HPV-16/18 vaccine is well tolerated and highly efficacious against HPV-16/18–associated high-grade genital lesions and persistent infection in women.
Retinal fundus diseases can lead to irreversible visual impairment without timely diagnoses and appropriate treatments. Single disease-based deep learning algorithms had been developed for the ...detection of diabetic retinopathy, age-related macular degeneration, and glaucoma. Here, we developed a deep learning platform (DLP) capable of detecting multiple common referable fundus diseases and conditions (39 classes) by using 249,620 fundus images marked with 275,543 labels from heterogenous sources. Our DLP achieved a frequency-weighted average F1 score of 0.923, sensitivity of 0.978, specificity of 0.996 and area under the receiver operating characteristic curve (AUC) of 0.9984 for multi-label classification in the primary test dataset and reached the average level of retina specialists. External multihospital test, public data test and tele-reading application also showed high efficiency for multiple retinal diseases and conditions detection. These results indicate that our DLP can be applied for retinal fundus disease triage, especially in remote areas around the world.
Objectives
Early recognition of coronavirus disease 2019 (COVID-19) severity can guide patient management. However, it is challenging to predict when COVID-19 patients will progress to critical ...illness. This study aimed to develop an artificial intelligence system to predict future deterioration to critical illness in COVID-19 patients.
Methods
An artificial intelligence (AI) system in a time-to-event analysis framework was developed to integrate chest CT and clinical data for risk prediction of future deterioration to critical illness in patients with COVID-19.
Results
A multi-institutional international cohort of 1,051 patients with RT-PCR confirmed COVID-19 and chest CT was included in this study. Of them, 282 patients developed critical illness, which was defined as requiring ICU admission and/or mechanical ventilation and/or reaching death during their hospital stay. The AI system achieved a C-index of 0.80 for predicting individual COVID-19 patients’ to critical illness. The AI system successfully stratified the patients into high-risk and low-risk groups with distinct progression risks (
p
< 0.0001).
Conclusions
Using CT imaging and clinical data, the AI system successfully predicted time to critical illness for individual patients and identified patients with high risk. AI has the potential to accurately triage patients and facilitate personalized treatment.
Key Point
• AI system can predict time to critical illness for patients with COVID-19 by using CT imaging and clinical data.
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