DCDiff: Dual-Granularity Cooperative Diffusion Models for Pathology Image Analysis.

Whole Slide Images (WSIs) are paramount in the medical field, with extensive applications in disease diagnosis and treatment. Recently, many deep-learning methods have been used to classify WSIs. However, these methods are inadequate for accurately analyzing WSIs as they treat regions in WSIs as isolated entities and ignore contextual information. To address this challenge, we propose a novel Dual-Granularity Cooperative Diffusion Model (DCDiff) for the precise classification of WSIs. Specifically, we first design a cooperative forward and reverse diffusion strategy, utilizing fine-granularity and coarse-granularity to regulate each diffusion step and gradually improve context awareness. To exchange information between granularities, we propose a coupled U-Net for dual-granularity denoising, which efficiently integrates dual-granularity consistency information using the designed Fine- and Coarse-granularity Cooperative Aware (FCCA) model. Ultimately, the cooperative diffusion features extracted by DCDiff can achieve cross-sample perception from the reconstructed distribution of training samples. Experiments on three public WSI datasets show that the proposed method can achieve superior performance over state-of-the-art methods. The code is available at https://github.com/hemo0826/DCDiff.

Advancing the Boundary of Pre-trained Models for Drug Discovery: Interpretable Fine-Tuning Empowered by Molecular Physicochemical Properties.

In the field of drug discovery, a proliferation of pre-trained models has surfaced, exhibiting exceptional performance across a variety of tasks. However, the extensive size of these models, coupled with the limited interpretative capabilities of current fine-tuning methods, impedes the integration of pre-trained models into the drug discovery process. This paper pushes the boundaries of pre-trained models in drug discovery by designing a novel fine-tuning paradigm known as the Head Feature Parallel Adapter (HFPA), which is highly interpretable, high-performing, and has fewer parameters than other widely used methods. Specifically, this approach enables the model to consider diverse information across representation subspaces concurrently by strategically using Adapters, which can operate directly within the model's feature space. Our tactic freezes the backbone model and forces various small-size Adapters' corresponding subspaces to focus on exploring different atomic and chemical bond knowledge, thus maintaining a small number of trainable parameters and enhancing the interpretability of the model. Moreover, we furnish a comprehensive interpretability analysis, imparting valuable insights into the chemical area. HFPA outperforms over seven physiology and toxicity tasks and achieves state-of-the-art results in three physical chemistry tasks. We also test ten additional molecular datasets, demonstrating the robustness and broad applicability of HFPA.

Complement Biosensors Identify a Classical Pathway Stimulus in Complement-Mediated Hemolytic Uremic Syndrome.

Complement-mediated hemolytic uremic syndrome (CM-HUS) is a thrombotic microangiopathy characterized by germline variants or acquired antibodies to complement proteins and regulators. Building upon our prior experience with the modified Ham (mHam) assay for ex vivo diagnosis of complementopathies, we have developed an array of cell-based complement "biosensors'' by selective removal of complement regulatory proteins (CD55 and CD59, CD46, or a combination thereof) in an autonomously bioluminescent HEK293 cell line. These biosensors can be used as a sensitive method for diagnosing CM-HUS and monitoring therapeutic complement blockade. Using specific complement pathway inhibitors, this model identifies IgM-driven classical pathway stimulus during both acute disease and in many patients during clinical remission. This provides a potential explanation for ~50% of CM-HUS patients who lack an alternative pathway "driving" variant and suggests at least a subset of CM-HUS is characterized by a breakdown of IgM immunologic tolerance. Key Points: CM-HUS has a CP stimulus driven by polyreactive IgM, addressing the mystery of why 40% of CM-HUS lack complement specific variantsComplement biosensors and the bioluminescent mHam can be used to aid in diagnosis of CM-HUS and monitor complement inhibitor therapy.

A rare case report: multiple intrahepatic masses in a pediatric patient with citrin deficiency.

Deficiency of citrin, the liver-type aspartate-glutamate carrier, arises from biallelic mutations of the gene SLC25A13. Although citrin deficiency (CD) is associated with higher risk of hepatocellular carcinoma (HCC) in adult patients, this association remains inconclusive in pediatric cases. The patient in this paper had been diagnosed to have CD by SLC25A13 analysis at the age 10 months, and then in response to dietary therapy, her prolonged jaundice and marked hepatosplenomegaly resolved gradually. However, she was referred to the hospital once again due to recurrent abdominal distention for 2 weeks at her age 4 years and 9 months, when prominently enlarged liver and spleen were palpated, along with a strikingly elevated serum alpha-fetoprotein (AFP) level of 27605 ng/mL as well as a large mass in the right liver lobe and a suspected tumor thrombus within the portal vein on enhanced computed tomography. After 4 rounds of adjuvant chemotherapy, right hepatic lobectomy and portal venous embolectomy were performed at her age 5 years and 3 months, and metastatic hepatoblastoma was confirmed by histopathological analysis. Afterwards, the patient underwent 5 additional cycles of chemotherapy and her condition remained stable for 7 months after surgery. Unfortunately, hepatoblastoma recurred in the left lobe at the age 5 years and 10 months, which progressed rapidly into liver failure, and led to death at the age 6 years and 1 month. As far as we know, this is the the first case of hepatoblastoma in a patient with CD, raising the possibility of an association between these two conditions.

Deep learning-based spinal canal segmentation of computed tomography image for disease diagnosis: A proposed system for spinal stenosis diagnosis.

BACKGROUND: Lumbar disc herniation was regarded as an age-related degenerative disease. Nevertheless, emerging reports highlight a discernible shift, illustrating the prevalence of these conditions among younger individuals. METHODS: This study introduces a novel deep learning methodology tailored for spinal canal segmentation and disease diagnosis, emphasizing image processing techniques that delve into essential image attributes such as gray levels, texture, and statistical structures to refine segmentation accuracy. RESULTS: Analysis reveals a progressive increase in the size of vertebrae and intervertebral discs from the cervical to lumbar regions. Vertebrae, bearing weight and safeguarding the spinal cord and nerves, are interconnected by intervertebral discs, resilient structures that counteract spinal pressure. Experimental findings demonstrate a lack of pronounced anteroposterior bending during flexion and extension, maintaining displacement and rotation angles consistently approximating zero. This consistency maintains uniform anterior and posterior vertebrae heights, coupled with parallel intervertebral disc heights, aligning with theoretical expectations. CONCLUSIONS: Accuracy assessment employs 2 methods: IoU and Dice, and the average accuracy of IoU is 88% and that of Dice is 96.4%. The proposed deep learning-based system showcases promising results in spinal canal segmentation, laying a foundation for precise stenosis diagnosis in computed tomography images. This contributes significantly to advancements in spinal pathology understanding and treatment.

Naringin attenuates Actinobacillus pleuropneumoniae-induced acute lung injury via MAPK/NF-κB and Keap1/Nrf2/HO-1 pathway.

Actinobacillus pleuropneumoniae (APP) causes porcine pleuropneumonia (PCP), which is clinically characterized by acute hemorrhagic, necrotizing pneumonia, and chronic fibrinous pneumonia. Although many measures have been taken to prevent the disease, prevention and control of the disease are becoming increasingly difficult due to the abundance of APP sera, weak vaccine cross-protection, and increasing antibiotic resistance in APP. Therefore, there is an urgent need to develop novel drugs against APP infection to prevent the spread of APP. Naringin (NAR) has been reported to have an excellent therapeutic effect on pulmonary diseases, but its therapeutic effect on lung injury caused by APP is not apparent. Our research has shown that NAR was able to alleviate APP-induced weight loss and quantity of food taken and reduce the number of WBCs and NEs in peripheral blood in mice; pathological tissue sections showed that NAR was able to prevent and control APP-induced pathological lung injury effectively; based on the establishment of an in vivo/in vitro model of APP inflammation, it was found that NAR was able to play an anti-inflammatory role through inhibiting the MAPK/NF-κB signaling pathway and exerting anti-inflammatory effects; additionally, NAR activating the Nrf2 signalling pathway, increasing the secretion of antioxidant enzymes Nqo1, CAT, and SOD1, inhibiting the secretion of oxidative damage factors NOS2 and COX2, and enhancing the antioxidant stress ability, thus playing an antioxidant role. In summary, NAR can relieve severe lung injury caused by APP by reducing excessive inflammatory response and improving antioxidant capacity.

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).

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.

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].

[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].

[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].

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.

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.

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.

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.

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.

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.