Suicide among lymphoma patients.

BACKGROUND: Higher suicide rates were observed in patients diagnosed with lymphoma. In this study, we accurately identified patients with high-risk lymphoma for suicide by constructing a nomogram with a view to effective interventions and reducing the risk of suicide. METHODS: 235,806 patients diagnosed with lymphoma between 2000 and 2020 were picked from the Surveillance, Epidemiology, and End Results (SEER) database and randomly divided into training (N = 165,064) and validation set (N = 70,742). A combination of the Least absolute shrinkage and selection operator (LASSO) and Cox proportional hazards regression identified the predictors that constructed the nomogram. To assess the discrimination, calibration, clinical applicability, and generalization of this nomogram, we implemented receiver operating characteristic curves (ROC), calibration curves, decision curve analysis (DCA), and internal validation. The robustness of the results was assessed by the competing risks regression model. RESULTS: Age at diagnosis, gender, ethnicity, marital status, stage, surgery, radiotherapy, and annual household income were key predictors of suicide in lymphoma patients. A nomogram was created to visualize the risk of suicide after a lymphoma diagnosis. The c-index for the training set was 0.773, and the validation set was 0.777. The calibration curve for the nomogram fitted well with the diagonal and the clinical decision curve indicated its clinical benefit. LIMITATION: The effects of unmeasured and unnoticed biases and confounders were difficult to eliminate due to retrospective studies. CONCLUSION: A convenient and reliable model has been constructed that will help to individualize and accurately quantify the risk of suicide in patients diagnosed with lymphoma.

Comparison of the RF-CL and CACS-CL models to estimate the pretest probability of obstructive coronary artery disease and predict prognosis in patients with stable chest pain and diabetes mellitus.

Background: The most appropriate tool for estimating the pretest probability (PTP) of obstructive coronary artery disease (CAD) in patients with diabetes mellitus (DM) and stable chest pain (SCP) remains unknown. Therefore, we aimed to validate and compare two recent models, namely, the risk factor-weighted clinical likelihood (RF-CL) model and coronary artery calcium score (CACS)-weighted clinical likelihood (CACS-CL) model, in these patient populations. Methods: A total of 1,245 symptomatic patients with DM, who underwent CACS and coronary computed tomographic angiography (CCTA) scan, were identified and followed up. PTP of obstructive CAD for each patient was estimated using the RF-CL model and CACS-CL model, respectively. Area under the receiver operating characteristic curve (AUC), net reclassification improvement (NRI), and integrated discrimination improvement (IDI) were used to assess the performance of models. The associations of major adverse cardiovascular events (MACE) with risk groups were evaluated using Cox proportional hazards regression. Results: Compared with the RF-CL model, the CACS-CL model revealed a larger AUC (0.856 vs. 0.782, p = 0.0016), positive IDI (12%, p < 0.0001) and NRI (34%, p < 0.0001), stronger association to MACE (hazard ratio: 0.26 vs. 0.38) and less discrepancy between observed and predicted probabilities, resulting in a more effective risk assessment to optimize downstream clinical management. Conclusion: Among patients with DM and SCP, the incorporation of CACS into the CACS-CL model resulted in a more accurate estimation for PTP and prediction of MACE. Utilizing the CACS-CL model, instead of the RF-CL model, might have greater potential to avoid unnecessary and omissive cardiovascular imaging testing with minimal cost.

Manganese Dioxide/Gold-based Active Tumor Targeting Nanoprobes for Enhancing Photodynamic and Low-Temperature-Photothermal Combination Therapy in Lung Cancer.

Tumor drug resistance caused by the tumor microenvironment is an extremely difficult problem for researchers to solve. Nanoplatforms that integrate diagnosis and treatment have great advantages in tumor treatment, but the design and synthesis of simple and efficient nanoplatforms still face tremendous challenges. In this study, a novel Mn/Au@ir820/GA-CD133 nanoprobe was developed. The manganese dioxide/gold particles were prepared by coprecipitation/assembly, chemically coupled with CD133 antibody, and finally loaded with the photosensitive drug IR820 and the heat shock protein inhibitor Ganetespib. The nanoprobe demonstrated good tumor-targeting ability, increased the level of singlet oxygen produced from laser irradiation by effectively alleviating tumor hypoxia, and decreased the threshold of heat tolerance by downregulating the expression of HSP90 in tumor tissues. This nanoprobe successfully inhibited the growth and progression of tumor tissues in a tumor-bearing mouse model by improving the effectiveness of photodynamic and low-temperature photothermal combination therapy.

Ion Tunnel Matrix Initiated Oriented Attachment for Highly Utilized Zn Anodes.

Metallic zinc is an ideal anode for aqueous energy storage; however, Zn anodes suffer from nonhomogeneous deposition, low reversibility, and dendrite formation; these lead to an overprovision of zinc metal in full cells. Herein, oriented-attachment-regulated Zn stacking initiated through a trapping-then-planting process with a high zinc utilization rate (ZUR) is reported. Due to the isometric topology features of cubic-type Prussian blue analog (PBA), the initial Zn plating occurs at specific sites with equal spacing of ≈5 Å in the direction perpendicular to the substrate; the trace amount of zinc ions trapped in tunnel matrix provides nuclei for the oriented attachment of Zn (002) deposits. As a result, the PBA-decorated substrate delivers high reversibility of dendrite-free zinc plating/stripping for more than 6600 cycles (1320 h) and achieves an average Coulombic efficiency (CE) of 99.5% at 5 mA cm-2 with 100% ZUR. Moreover, the anode-limited full cell with a low negative-positive electrode ratio (N/P) of 1.2 can be operated stably for 360 cycles, displaying an energy density of 214 Wh kg-1 ; this greatly exceeds commercial aqueous batteries. This work provides a proof of concept design of metal anodes with a high utilization ratio and a practical method for developing high-energy-density batteries.

FECC-Net: A Novel Feature Enhancement and Context Capture Network Based on Brain MRI Images for Lesion Segmentation.

In recent years, the increasing incidence of morbidity of brain stroke has made fast and accurate segmentation of lesion areas from brain MRI images important. With the development of deep learning, segmentation methods based on the computer have become a solution to assist clinicians in early diagnosis and treatment planning. Nevertheless, the variety of lesion sizes in brain MRI images and the roughness of the boundary of the lesion pose challenges to the accuracy of the segmentation algorithm. Current mainstream medical segmentation models are not able to solve these challenges due to their insufficient use of image features and context information. This paper proposes a novel feature enhancement and context capture network (FECC-Net), which is mainly composed of an atrous spatial pyramid pooling (ASPP) module and an enhanced encoder. In particular, the ASPP model uses parallel convolution operations with different sampling rates to enrich multi-scale features and fully capture image context information in order to process lesions of different sizes. The enhanced encoder obtains deep semantic features and shallow boundary features in the feature extraction process to achieve image feature enhancement, which is helpful for restoration of the lesion boundaries. We divide the pathological image into three levels according to the number of pixels in the real mask area and evaluate FECC-Net on an open dataset called Anatomical Tracings of Lesions After Stroke (ATLAS). The experimental results show that our FECC-Net outperforms mainstream methods, such as DoubleU-Net and TransUNet. Especially in small target tasks, FECC-Net is 4.09% ahead of DoubleU-Net on the main indicator DSC. Therefore, FECC-Net is encouraging and can be relied upon for brain MRI image applications.

circ_0004140 promotes LUAD tumor progression and immune resistance through circ_0004140/miR-1184/CCL22 axis.

Lung adenocarcinoma (LUAD) is a highly prevalent cancer with high mortality. Immune resistance and tumor metastasis are the pivotal factors for the promotion of LUAD. CircRNAs have been revealed a crucial pre-clinical diagnostic and therapeutic potentials in LUAD. Herein, we identify a novel circRNA (circ_0004140), derived from the oncogene YAP1, which is up-regulated in LUAD. The high expression of circ_0004140 is correlated with poor prognosis and CTL cells dysfunction in LUAD patients. Knockdown of circ_0004140 regulated LUAD cells proliferation, migration, and apoptosis. Mechanistically, circ_0004140 served as a sponge of miR-1184 targeting C-C motif chemokine ligand 22(CCL22). Overexpression of CCL22 reversed the inhibitory effect induced by si-circ_0004140 on cells proliferation and migration. Moreover, we also revealed that elevated circ_ooo4140 was related to cytotoxic lymphocyte exhaustion, and a combination therapy of C-021 (CCL22/CCR4 axis inhibitor) and anti-PD-1 attenuated LUAD promotion and immune resistance. In conclusion, circ_0004140 may drive resistance to anti-PD-1 immunotherapy, providing a novel potential therapeutic target for LUAD treatment.