Spatiotemporal characterization of PM2.5, O3, and trace gases associated with east Asian continental outflows via drone sounding.

East Asian continental outflows with PM2.5, O3, and other species may determine the baseline conditions and affect the air quality in downwind areas via long-range transport (LRT). To gain insight into the impact and spatiotemporal characteristics of airborne pollutants in East Asian continental outflows, a versatile multicopter drone sounding platform was used to simultaneously observe PM2.5, O3, CO2, and meteorological variables (temperature, specific humidity, pressure, and wind vector) above the northern tip of Taiwan, Cape Fuiguei, which often encounters continental outflows during winter monsoon periods. By coordinating hourly high-spatial-resolution profiles provided by drone soundings, WRF/CMAQ model air quality predictions, HYSPLIT-simulated backward trajectories, and MERRA-2 reanalysis data, we analyzed two prominent phenomena of airborne pollutants in continental outflows to better understand their physical/chemical characteristics. First, we found that pollutants were well mixed within a sounding height of 500 m when continental outflows passed through and completely enveloped Cape Fuiguei. Eddies induced by significant fluctuations in wind speeds coupled with minimal temperature inversion and LRT facilitated vertical mixing, possibly resulting in high homogeneity of pollutants within the outflow layer. Second, the drone soundings indicated exceptionally high O3 concentrations (70-100 ppbv) but relatively low concentrations of PM2.5 (10-20 µg/m3), CO2 (420-425 ppmv), and VOCs in some air masses. The low levels of PM2.5, CO2, and VOCs ruled out photochemistry as the cause of the formation of high-level O3. Further coordination of spatiotemporal data with air mass trajectories and O3 cross sections provided by MERRA-2 suggested that the high O3 concentrations could be attributed to stratospheric intrusion and advection via continental outflows. High-level O3 concentrations persisted in the lower troposphere, even reaching the surface, suggesting that stratospheric intrusion O3 may be involved in the rising trend in O3 concentrations in parts of East Asia in recent years in addition to surface photochemical factors.

Successful treatment of severe primary mitral regurgitation due to rheumatic aetiology using a novel-designed transcatheter edge-to-edge repair system.

The transcatheter edge-to-edge mitral valve repair (TEER) has been recommended as a reliable treatment option for selected patients with severe degenerative and functional mitral regurgitation (MR). Although MR patients with rheumatic etiology were excluded from two significant trials (EVEREST II and COAPT) that established a role for the TEER in degenerative and functional MR. However, it has been reported that the TEER procedure could be safely and effectively performed in carefully selected rheumatic MR patients. Therefore, we share a case report of successfully treating severe rheumatic MR using a novel-designed TEER system (JensClipTM).

Multicolor-Assay-on-a-Chip Processed by Robotic Operation (MACpro) with Improved Diagnostic Accuracy for Field-Deployable Detection.

The ability to deploy decentralized laboratories with autonomous and reliable disease diagnosis holds the potential to deliver accessible healthcare services for public safety. While microfluidic technologies provide precise manipulation of small fluid volumes with improved assay performance, their limited automation and versatility confine them to laboratories. Herein, we report the utility of multicolor assay-on-a-chip processed by robotic operation (MACpro), to address this unmet need. The MACpro platform comprises a robot-microfluidic interface and an eye-in-hand module that provides flexible yet stable actions to execute tasks in a programmable manner, such as the precise manipulation of the microfluidic chip along with different paths. Notably, MACpro shows improved detection performance by integrating the microbead-based antibody immobilization with enhanced target recognition and multicolor sensing via Cu2+-catalyzed plasmonic etching of gold nanorods for rapid and sensitive analyte quantification. Using interferon-gamma as an example, we demonstrate that MACpro completes a sample-to-answer immunoassay within 30 min and achieves a 10-fold broader dynamic range and a 10-fold lower detection limit compared to standard enzyme-linked immunosorbent assays (0.66 vs 5.2 pg/mL). MACpro extends the applications beyond traditional laboratories and presents an automated solution to expand diagnostic capacity in diverse settings.

[Corrigendum] MicroRNA­222­3p promotes tumor cell migration and invasion and inhibits apoptosis, and is correlated with an unfavorable prognosis of patients with renal cell carcinoma.

Following the publication of the above article, an interested reader drew to the attention of the Editorial Office that, in Fig. 3A on p. 530, two pairs of data panels were overlapping, such that certain of the panels appeared to have been derived from the same original sources where the results from differently performed experiments were intended to have been portrayed. The authors have examined their original data, and realize that errors associated with data handling/labelling during the preparation of the representative images in Fig. 3A had occurred. The revised version of Fig. 3, showing the correct data for the 'NC/ACHN/Invasion and Migration' data panels, the 'Inhibitor NC/786­O' panel and the 'Inhibitor NC/ACHN/Invasion' panel, is shown on the next page. The authors can confirm that the errors associated with this figure did not have any significant impact on either the results or the conclusions reported in this study, and all the authors agree with the publication of this Corrigendum. The authors are grateful to the Editor of International Journal of Molecular Medicine for giving them the opportunity to publish this Corrigendum; furthermore, they apologize to the readership of the Journal for any inconvenience caused. [International Journal of Molecular Medicine 43: 525­534, 2019; DOI: 10.3892/ijmm.2018.3931].

[Retracted] Oncogenic miR-23a-5p is associated with cellular function in RCC.

Following the publication of this article, a concerned reader drew to the Editor's attention that, for the invasion and migration assay data shown in Fig. 4 on p. 2314, three pairs of data panels were overlapping, such that data which were intended to show the results from differently performed experiments were obtained from a smaller number of original sources. Moreover, after having conducted an internal investigation, the Editorial Office also observed that some of the flow cytometric data shown in Fig. 6 were duplicated in Fig. 7. Considering the number of overlapping data panels that have been identified in this published paper, the Editor of Molecular Medicine Reports has concluded that the article should be retracted from the publication on account of a lack of confidence in the integrity of the data. Upon contacting the authors about this matter, they accepted the decision to retract this paper. The Editor apologizes to the readership for any inconvenience caused, and thanks the interested reader for drawing this matter to our attention. [Molecular Medicine Reports 16: 2309-2317, 2017; DOI: 10.3892/mmr.2017.6829].

[Corrigendum] Identification of lncRNA EGOT as a tumor suppressor in renal cell carcinoma.

Subsequently to the publication of the above paper, an interested reader drew to the authors' attention that, concerrning the Transwell cell migration and invasion assay data shown in Fig. 6A and B for the 786­O cell line on p. 7206, the pcDNA3.1­EGOT 'Migration' and 'Invasion' (a­1 and b­1) data panels appeared to contain overlapping sections of data, such that they were potentially derived from the same original source, where these panels were intended to show the results from differently performed experiments. The authors have re­examined their original data, and realize that the 'Invasion' (b­1) panel in Fig. 6B was inadvertently chosen incorrectly. The revised version of Fig. 6, now featuring the correct data for the 'Invasion' experiment (B1 in the replacement figure) in Fig. 6B, is shown on the next page. Note that this error did not adversely affect either the results or the overall conclusions reported in this study. All the authors agree with the publication of this corrigendum, and are grateful to the Editor of Molecular Medicine Reports for allowing them the opportunity to publish this. They also wish to apologize to the readership of the Journal for any inconvenience caused.[Molecular Medicine Reports 16: 7072­7079, 2017; DOI: 10.3892/mmr.2017.7470].

Multi-contrast learning-guided lightweight few-shot learning scheme for predicting breast cancer molecular subtypes.

Invasive gene expression profiling studies have exposed prognostically significant breast cancer subtypes: normal-like, luminal, HER-2 enriched, and basal-like, which is defined in large part by human epidermal growth factor receptor 2 (HER-2), progesterone receptor (PR), and estrogen receptor (ER). However, while dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) has been generally employed in the screening and therapy of breast cancer, there is a challenging problem to noninvasively predict breast cancer molecular subtypes, which have extremely low-data regimes. In this paper, a novel few-shot learning scheme, which combines lightweight contrastive convolutional neural network (LC-CNN) and multi-contrast learning strategy (MCLS), is worthwhile to be developed for predicting molecular subtype of breast cancer in DCE-MRI. Moreover, MCLS is designed to construct One-vs-Rest and One-vs-One classification tasks, which addresses inter-class similarity among normal-like, luminal, HER-2 enriched, and basal-like. Extensive experiments demonstrate the superiority of our proposed scheme over state-of-the-art methods. Furthermore, our scheme is able to achieve competitive results on few samples due to joint LC-CNN and MCLS for excavating contrastive correlations of a pair of DCE-MRI.

Fine-Grained Forecasting of COVID-19 Trends at the County Level in the United States.

The novel coronavirus (COVID-19) pandemic, first identified in Wuhan China in December 2019, has profoundly impacted various aspects of daily life, society, healthcare systems, and global health policies. There have been more than half a billion human infections and more than 6 million deaths globally attributable to COVID-19. Although treatments and vaccines to protect against COVID-19 are now available, people continue being hospitalized and dying due to COVID-19 infections. Real-time surveillance of population-level infections, hospitalizations, and deaths has helped public health officials better allocate healthcare resources and deploy mitigation strategies. However, producing reliable, real-time, short-term disease activity forecasts (one or two weeks into the future) remains a practical challenge. The recent emergence of robust time-series forecasting methodologies based on deep learning approaches has led to clear improvements in multiple research fields. We propose a recurrent neural network model named Fine-Grained Infection Forecast Network (FIGI-Net), which utilizes a stacked bidirectional LSTM structure designed to leverage fine-grained county-level data, to produce daily forecasts of COVID-19 infection trends up to two weeks in advance. We show that FIGI-Net improves existing COVID-19 forecasting approaches and delivers accurate county-level COVID-19 disease estimates. Specifically, FIGI-Net is capable of anticipating upcoming sudden changes in disease trends such as the onset of a new outbreak or the peak of an ongoing outbreak, a skill that multiple existing state-of-the-art models fail to achieve. This improved performance is observed across locations and periods. Our enhanced forecasting methodologies may help protect human populations against future disease outbreaks.

Application of dose-gradient function in reducing radiation induced lung injury in breast cancer radiotherapy.

OBJECTIVE: Try to create a dose gradient function (DGF) and test its effectiveness in reducing radiation induced lung injury in breast cancer radiotherapy. MATERIALS AND METHODS: Radiotherapy plans of 30 patients after breast-conserving surgery were included in the study. The dose gradient function was defined as DGH=VDVp3, then the area under the DGF curve of each plan was calculated in rectangular coordinate system, and the minimum area was used as the trigger factor, and other plans were triggered to optimize for area reduction. The dosimetric parameters of target area and organs at risk in 30 cases before and after re-optimization were compared. RESULTS: On the premise of ensuring that the target dose met the clinical requirements, the trigger factor obtained based on DGF could further reduce the V5, V10, V20, V30 and mean lung dose (MLD) of the ipsilateral lung in breast cancer radiotherapy, P < 0.01. And the D2cc and mean heart dose (MHD) of the heart were also reduced, P < 0.01. Besides, the NTCPs of the ipsilateral lung and the heart were also reduced, P < 0.01. CONCLUSION: The trigger factor obtained based on DGF is efficient in reducing radiation induced lung injury in breast cancer radiotherapy.

CGMA-Net: Cross-Level Guidance and Multi-Scale Aggregation Network for Polyp Segmentation.

Colonoscopy is considered the best prevention and control method for colorectal cancer, which suffers extremely high rates of mortality and morbidity. Automated polyp segmentation of colonoscopy images is of great importance since manual polyp segmentation requires a considerable time of experienced specialists. However, due to the high similarity between polyps and mucosa, accompanied by the complex morphological features of colonic polyps, the performance of automatic polyp segmentation is still unsatisfactory. Accordingly, we propose a network, namely Cross-level Guidance and Multi-scale Aggregation (CGMA-Net), to earn a performance promotion. Specifically, three modules, including Cross-level Feature Guidance (CFG), Multi-scale Aggregation Decoder (MAD), and Details Refinement (DR), are individually proposed and synergistically assembled. With CFG, we generate spatial attention maps from the higher-level features and then multiply them with the lower-level features, highlighting the region of interest and suppressing the background information. In MAD, we parallelly use multiple dilated convolutions of different sizes to capture long-range dependencies between features. For DR, an asynchronous convolution is used along with the attention mechanism to enhance both the local details and the global information. The proposed CGMA-Net is evaluated on two benchmark datasets, i.e., CVC-ClinicDB and Kvasir-SEG, whose results demonstrate that our method not only presents state-of-the-art performance but also holds relatively fewer parameters. Concretely, we achieve the Dice Similarity Coefficient (DSC) of 91.85% and 95.73% on Kvasir-SEG and CVC-ClinicDB, respectively. The assessment of model generalization is also conducted, resulting in DSC scores of 86.25% and 86.97% on the two datasets respectively.

Pulmonary embolism after diagnostic curettage in patient with adenomyosis and hysteromyoma: A case report and brief review of literature.

RATIONALE: Pulmonary embolism (PE) is a common cause of cardiovascular death whose major acquired risk factors include postoperative states, pregnancy, malignancy, and age. We report a case of PE that occurred after diagnostic curettage for abnormal uterine bleeding, with a medical history of adenomyosis and hysteromyoma. PATIENT CONCERNS AND DIAGNOSES: A 31-year-old Han Chinese female was referred to our hospital with menstrual disorders, increased menstrual flow, and severe anemia. After admission, the patient was treated with a blood transfusion, iron supplementation, and erythropoietin, and diagnostic curettage was performed the following day. On the first postoperative day, the patient developed pulmonary embolism with dyspnea and fever diagnosed by CT pulmonary angiography and significantly elevated D-dimer. INTERVENTIONS AND OUTCOMES: Molecular weight heparin was administered for PE for 2 weeks, dyspnea was relieved significantly after 2 days of treatment and the uterine bleeding did not increase; and gonadotropin-releasing hormone agonists were administered for adenomyosis after 1 week of anticoagulant therapy to reduce bleeding. We followed up for 6 months, and the patient had no recurrence of thrombosis and uterine bleeding had improved. CONCLUSION: We speculate that the occurrence of pulmonary embolism was closely related to adenomyosis, hysteromyoma, and curettage in this patient. Treating the presence of both menstrual bleeding and thromboembolism is challenging, and careful management is necessary to avoid therapeutic contradictions.

Application research on reducing radiation-induced lung injury with a trigger operator based on overlap volume histogram (OVH) in breast cancer postoperative radiotherapy.

This study aims to develop a trigger operator based on the Overlap Volume Histogram (OVH) and examined its effectiveness in enhancing plan quality to minimize radiation-induced lung injury in postoperative radiotherapy for breast cancer. This trigger operator was applied for plan re-optimization to the previous Volumetric Modulated Arc Therapy (VMAT) plans of 16 left breast conserving surgery cases. These cases were categorized into a Contiguous Group (CG) and a Separated Group (SG) based on the relative position between the target and the Left-Lung (L-Lung). We investigated the changes in Vx, mean dose, and Normal Tissue Complication Probability (NTCP) values of organs-at-risk (OARs) before and after using the trigger operator. The Pairwise Sample T test was employed to evaluate the differences in indices between the two groups before and after optimizations. The trigger operator effectively initiated plan re-optimization. The values of V5, V10, V20, V30, and V40 of the L-Lung, as well as the mean dose of the heart, all decreased after re-optimization. The Pairwise Sample T test results showed statistically significant differences in the V20, V30, and V40 of the L-Lung in the CG (P < 0.01), and in the V5, V10, V20, V30, and V40 of the L-Lung in the SG (P < 0.01). Our findings suggest that the proposed trigger operator can improve plan quality, thereby reducing radiation-induced lung injury in postoperative radiotherapy for breast cancer.

Mechanisms for enhanced lignin humification with reduced organic matter loss by goethite in biogas residue composting.

In this study, effects of three iron (oxyhydr)oxides on the biogas residue composting, i.e., composting with goethite (CFe1), hematite (CFe2) or magnetite (CFe3), were investigated. Results showed that composting performance of CFe1 was much better than those of CFe2 and CFe3. Addition of goethite increased temperature of CFe1 and enhanced lignin humification. More than 31.49% of Fe(III) in goethite was reduced to amorphous Fe(II) during the composting, suggesting that goethite worked as electron acceptor for microbial metabolism and heat generation. The functional bacteria Chloroflexi and Actinobacteria, and genes encoding key enzymes (AA1 family), which play essential roles in humification of lignin, were enriched in CFe1. Besides, goethite reduced 10.96% organic matter (OM) loss probably by increasing the molecular size and aggregation of OM for its protection during the composting. This study shows that adding goethite is an efficient strategy to enhancing the humification of lignin-rich biowaste.

The heterogeneity of tumour immune microenvironment revealing the CRABP2/CD69 signature discriminates distinct clinical outcomes in breast cancer.

BACKGROUND: It has been acknowledged that the tumour immune microenvironment (TIME) plays a critical role in determining therapeutic responses and clinical outcomes in breast cancer (BrCa). Thus, the identification of the TIME features is essential for guiding therapy and prognostic assessment for BrCa. METHODS: The heterogeneous cellular composition of the TIME in BrCa by single-cell RNA sequencing (scRNA-seq). Two subtype-special genes upregulated in the tumour-rich subtype and the immune-infiltrating subtype were extracted, respectively. The CRABP2/CD69 signature was established based on CRABP2 and CD69 expression, and its predictive values for the clinical outcome and the neoadjuvant chemotherapy (NAT) responses were validated in multiple cohorts. Moreover, the oncogenic role of CRABP2 was explored in BrCa cells. RESULTS: Based on the heterogeneous cellular composition of the TIME in BrCa, the BrCa samples could be divided into the tumour-rich subtype and the immune-infiltrating subtype, which exhibited distinct prognosis and chemotherapeutic responses. Next, we extracted CRABP2 as the biomarker for the tumour-rich subtype and CD69 as the biomarker for the immune-infiltrating subtype. Based on the CRABP2/CD69 signature, BrCa samples were re-divided into three subtypes, and the CRABP2highCD69low subtype exhibited the worst prognosis and the lowest chemotherapeutic response, while the CRABP2lowCD69high subtype showed the opposite results. Furthermore, CARBP2 functioned as a novel oncogene in BrCa, which promoted tumour cell proliferation, migration, and invasion, and CRABP2 inhibition triggered the activation of cytotoxic T lymphocytes (CTLs). CONCLUSION: The CRABP2/CD69 signature is significantly associated with the TIME features and could effectively predict the clinical outcome. Also, CRABP2 is determined to be a novel oncogene, which could be a therapeutic target in BrCa.

Using drone soundings to study the impacts and compositions of plumes from a gigantic coal-fired power plant.

The immense impacts of coal-fired power plant plumes on the atmospheric environment have caused great concern linked to climate and health issues. However, studies on the field observations of aerial plumes are relatively limited, mainly due to the lack of suitable plume observation tools and techniques. In this study, we use a multicopter unmanned aerial vehicle (UAV) sounding technique to study the influences of the aerial plumes of the world's fourth-largest coal-fired power plant on the atmospheric physical/chemical conditions and air quality. A set of species, including 106 volatile organic compounds (VOCs), CO, CO2, CH4, PM2.5, and O3, and meteorological variables of temperature (T), specific humidity (SH), and wind data, are collected by the UAV sounding technique. The results reveal that the large-scale plumes of the coal-fired power plant cause local temperature inversion and humidity changes, and even affect the dispersion of pollutants below. The chemical compositions of coal-fired power plant plumes are significantly different from those of another ubiquitous vehicular source. High fractions of ethane, ethene, and benzene and low fractions of n-butane and isopentane found in plumes could serve as the key features to help distinguish the influences of coal-fired power plant plumes from other pollution sources in a particular area. By taking the ratios of pollutants (e.g., PM2.5, CO, CH4, and VOCs) to CO2 in plumes and the CO2 emission amounts of the power plant into calculation, we enable the easy quantification of the specific pollutant emissions released from power plant plumes to the atmosphere. In summary, observation by using drone soundings dissecting the aerial plumes provides a new methodology that allows aerial plumes to be readily detected and characterized. Furthermore, the influences of the plumes on the atmospheric physical/chemical conditions and air quality can be assessed rather straightforwardly, which was not easily achievable in the past.

MEAI: an artificial intelligence platform for predicting distant and lymph node metastases directly from primary breast cancer.

PURPOSE: Breast cancer patients typically have decent prognoses, with a 5-year survival rate of more than 90%, but when the disease metastases to lymph node or distant, the prognosis drastically declines. Therefore, it is essential for future treatment and patient survival to quickly and accurately identify tumor metastasis in patients. An artificial intelligence system was developed to recognize lymph node and distant tumor metastases on whole-slide images (WSIs) of primary breast cancer. METHODS: In this study, a total of 832 WSIs from 520 patients without tumor metastases and 312 patients with breast cancer metastases (including lymph node, bone, lung, liver, and other) were gathered. Based on the WSIs were randomly divided into the training and testing cohorts, a brand-new artificial intelligence system called MEAI was built to identify lymph node and distant metastases in primary breast cancer. RESULTS: The final AI system attained an area under the receiver operating characteristic curve of 0.934 in a test set of 187 patients. In addition, the potential for AI system to increase the precision, consistency, and effectiveness of tumor metastasis detection in patients with breast cancer was highlighted by the AI's achievement of an AUROC higher than the average of six board-certified pathologists (AUROC 0.811) in a retrospective pathologist evaluation. CONCLUSION: The proposed MEAI system can provide a non-invasive approach to assess the metastatic probability of patients with primary breast cancer.