Extrasynaptic GABAA receptors in central medial thalamus mediate anesthesia in rats.

Neuronal depression in the thalamus underlies anesthetic-induced loss of consciousness, while the precise sub-thalamus nuclei and molecular targets involved remain to be elucidated. The present study investigated the role of extrasynaptic GABAA receptors in the central medial thalamic nucleus (CM) in anesthesia induced by gaboxadol (THIP) and diazepam (DZP) in rats. Local lesion of the CM led to a decrease in the duration of loss of righting reflex induced by THIP and DZP. CM microinjection of THIP but not DZP induced anesthesia. The absence of righting reflex in THIP-treated rats was consistent with the increase of low frequency oscillations in the delta band in the medial prefrontal cortex. CM microinjection of GABAA receptor antagonist SR95531 significantly attenuated the anesthesia induced by systemically-administered THIP, but not DZP. Moreover, the rats with declined expression of GABAA receptor δ-subunit in the CM were less responsive to THIP or DZP. These findings explained a novel mechanism of THIP-induced loss of consciousness and highlighted the role of CM extrasynaptic GABAA receptors in mediating anesthesia.

Effects of Telemedicine on Informal Caregivers of Patients in Palliative Care: Systematic Review and Meta-Analysis.

Background: Telemedicine technology is a rapidly developing field that shows immense potential for improving medical services. In palliative care, informal caregivers assume the primary responsibility in patient care and often face challenges such as increased physical and mental stress and declining health. In such cases, telemedicine interventions can provide support and improve their health outcomes. However, research findings regarding the use of telemedicine among informal caregivers are controversial, and the efficacy of telemedicine remains unclear. Objective: This study aimed to evaluate the impacts of telemedicine on the burden, anxiety, depression, and quality of life of informal caregivers of patients in palliative care. Methods: A systematic literature search was conducted using the PubMed, Embase, Web of Science, CENTRAL, PsycINFO, CINAHL Plus with Full Text, CBM, CNKI, WanFang, and VIP databases to identify relevant randomized controlled trials published from inception to March 2023. Two authors independently screened the studies and extracted the relevant information. The methodological quality of the included studies was assessed using the Cochrane risk-of-bias tool. Intervention effects were estimated and sensitivity analysis was conducted using Review Manager 5.4, whereas 95% prediction intervals (PIs) were calculated using R (version 4.3.2) and RStudio. Results: A total of 9 randomized controlled trials were included in this study. The meta-analysis indicated that telemedicine has reduced the caregiving burden (standardized mean differences [SMD] -0.49, 95% CI -0.72 to -0.27; P<.001; 95% PI -0.86 to -0.13) and anxiety (SMD -0.23, 95% CI -0.40 to -0.06; P=.009; 95% PI -0.98 to 0.39) of informal caregivers; however, it did not affect depression (SMD -0.21, 95% CI -0.47 to 0.05; P=.11; 95% PI -0.94 to 0.51) or quality of life (SMD 0.35, 95% CI -0.20 to 0.89; P=.21; 95% PI -2.15 to 2.85). Conclusions: Although telemedicine can alleviate the caregiving burden and anxiety of informal caregivers, it does not significantly reduce depression or improve their quality of life. Further high-quality, large-sample studies are needed to validate the effects of telemedicine. Furthermore, personalized intervention programs based on theoretical foundations are required to support caregivers.

MoViT: Memorizing Vision Transformers for Medical Image Analysis.

The synergy of long-range dependencies from transformers and local representations of image content from convolutional neural networks (CNNs) has led to advanced architectures and increased performance for various medical image analysis tasks due to their complementary benefits. However, compared with CNNs, transformers require considerably more training data, due to a larger number of parameters and an absence of inductive bias. The need for increasingly large datasets continues to be problematic, particularly in the context of medical imaging, where both annotation efforts and data protection result in limited data availability. In this work, inspired by the human decision-making process of correlating new "evidence" with previously memorized "experience", we propose a Memorizing Vision Transformer (MoViT) to alleviate the need for large-scale datasets to successfully train and deploy transformer-based architectures. MoViT leverages an external memory structure to cache history attention snapshots during the training stage. To prevent overfitting, we incorporate an innovative memory update scheme, attention temporal moving average, to update the stored external memories with the historical moving average. For inference speedup, we design a prototypical attention learning method to distill the external memory into smaller representative subsets. We evaluate our method on a public histology image dataset and an in-house MRI dataset, demonstrating that MoViT applied to varied medical image analysis tasks, can outperform vanilla transformer models across varied data regimes, especially in cases where only a small amount of annotated data is available. More importantly, MoViT can reach a competitive performance of ViT with only 3.0% of the training data. In conclusion, MoViT provides a simple plug-in for transformer architectures which may contribute to reducing the training data needed to achieve acceptable models for a broad range of medical image analysis tasks.

Prognosis and chemotherapy drug sensitivity in liver hepatocellular carcinoma through a disulfidptosis-related lncRNA signature.

Disulfidptosis, a new type of regulated cell death associated with the actin cytoskeleton, provides a new therapeutic tool for cancers. The direct relationship between disulfidptosis-related lncRNAs(DRLs) in liver hepatocellular carcinoma(HCC) remains unclear. We acquired transcriptomic data, corresponding clinical data, and tumor mutation data of HCC from the TCGA database. First of all, DRLs were determined through correlation analysis. Then, a prognostic model containing six DRLs was created by adopting univariate Cox regression, LASSO algorithm and multivariate Cox regression analysis. Based on the model, 424 HCC patients were divided into high- and low-risk groups. Next, we structured ROC curves and PCA through combining the model and clinical data. Enrichment analysis and immune infiltration analysis were adopted to further explore the relationship between the model and prognosis. In addition, we explored the relationship between the model and tumor mutation burden (TMB). There were significant differences between high- and low- risk groups, and patients in the high-risk group showed poor prognosis. Enrichment analysis suggested that metabolic progress was obviously different between the two groups. According to the analysis of immune infiltration, there were several differences in immune cells, function, and checkpoints. Patients with high-risk and high TMB demonstrated the least favorable prognosis. The two risk groups both manifested visiblly in chemotherapy drug sensitivity. To sum up, we set up a DRL-based signature and that may provide a predictable value for the prognosis and use of chemotherapy drugs for HCC patients.

A novel phage putative depolymerase, Depo16, has specific activity against K1 capsular-type Klebsiella pneumoniae.

Klebsiella pneumoniae, especially hypervirulent K. pneumoniae (hvKP), is a common opportunistic pathogen that often causes hospital- and community-acquired infections. Capsular polysaccharide (CPS) is an important virulence factor of K. pneumoniae. Some phages encode depolymerases that can recognize and degrade bacterial polysaccharides. In this study, the lytic bacteriophage vB_KpnP_ZK1 (abbreviated as ZK1) was isolated using serotype K1 hvKP as the host. Although amino acid sequence BLAST analysis indicated that the tail fiber protein Depo16 of phage ZK1 showed no significant similarity to any reported phage depolymerases, it displayed enzymatic activities that are characteristic of phage depolymerases. After expression and purification, Depo16 could efficiently remove the capsular polysaccharide layer that surrounds the surface of serotype K1 K. pneumoniae. Although no bactericidal activity was detected, Depo16 makes serotype K1 K. pneumoniae sensitive to peritoneal macrophages (PMs). In addition, in a mouse bacteremia model of serotype K1 K. pneumoniae, 25 µg of Depo16 was effective in significantly prolonging survival. Depo16 treatment can reduce the bacterial load in blood and major tissues and alleviate tissue damage in mice. This indicates that the putative depolymerase Depo16 is a potential antibacterial agent against serotype K1 K. pneumoniae infections.IMPORTANCEKlebsiella pneumoniae often causes hospital-acquired infections and community-acquired infections. Capsular polysaccharide (CPS) is one of the crucial virulence factors of K. pneumoniae. K1 and K2 capsular-type K. pneumoniae strains are the most prevalent serotypes of hypervirulent K. pneumoniae (hvKP). In this study, a novel K. pneumoniae phage named vB_KpnP_ZK1 was isolated, and its putative depolymerase Depo16 showed low homology with other reported phage depolymerases. Depo16 can specifically degrade the K. pneumoniae K1 capsule making this serotype sensitive to peritoneal macrophages. More importantly, Depo16 showed a significant therapeutic effect in a mouse bacteremia model caused by serotype K1 K. pneumoniae. Thus, Depo16 is a potential antibacterial agent to combat serotype K1 K. pneumoniae infections.

An integrative pan-cancer bioinformatics analysis of MSRB1 and its association with tumor immune microenvironment, prognosis, and immunotherapy.

Methionine sulfoxide reductase B1 (MSRB1) is involved in the development and immune regulation of multiple tumors. However, the role of MSRB1 in the tumor microenvironment and its potential as a therapeutic target remain largely unknown. In this study, MSRB1 expression patterns were evaluated using pan-cancer RNA sequencing data from multiple cell lines, tissues, and single cells. The pan-cancer prognostic role of MSRB1 was assessed and the association between MSRB1 expression and certain cancer characteristics was analyzed. We showed that MSRB1 expression levels were increased in several types of cancer (P < 0.05) and in certain cell types (macrophages, dendritic cells, and malignant tumor cells). The upregulation of MSRB1 expression was due to DNA copy number amplification. Furthermore, MSRB1 was significantly associated with the activation of immune pathways (P < 0.05, NES > 0), immune cell infiltration, and expression of immune checkpoint molecules. In addition, high expression of MSRB1 was found in a series of in vivo and in vitro immunotherapy response models (P < 0.05), and showed resistance to most targeted drugs. Our results indicated that MSRB1 may regulate the tumor immune microenvironment through an immunoresponse and potentially influence cancer development. This could make it a promising predictive biomarker and therapeutic target for precise tumor immunotherapy.

Intra- and peritumoral radiomics features based on multicenter automatic breast volume scanner for noninvasive and preoperative prediction of HER2 status in breast cancer: a model ensemble research.

The aim to investigate the predictive efficacy of automatic breast volume scanner (ABVS), clinical and serological features alone or in combination at model level for predicting HER2 status. The model weighted combination method was developed to identify HER2 status compared with single data source model method and feature combination method. 271 patients with invasive breast cancer were included in the retrospective study, of which 174 patients in our center were randomized into the training and validation sets, and 97 patients in the external center were as the test set. Radiomics features extracted from the ABVS-based tumor, peritumoral 3 mm region, and peritumoral 5 mm region and clinical features were used to construct the four types of the optimal single data source models, Tumor, R3mm, R5mm, and Clinical model, respectively. Then, the model weighted combination and feature combination methods were performed to optimize the combination models. The proposed weighted combination models in predicting HER2 status achieved better performance both in validation set and test set. For the validation set, the single data source model, the feature combination model, and the weighted combination model achieved the highest area under the curve (AUC) of 0.803 (95% confidence interval [CI] 0.660-947), 0.739 (CI 0.556,0.921), and 0.826 (95% CI 0.689,0.962), respectively; with the sensitivity and specificity were 100%, 62.5%; 81.8%, 66.7%; 90.9%,75.0%; respectively. For the test set, the single data source model, the feature combination model, and the weighted combination model attained the best AUC of 0.695 (95% CI 0.583, 0.807), 0.668 (95% CI 0.555,0.782), and 0.700 (95% CI 0.590,0.811), respectively; with the sensitivity and specificity were 86.1%, 41.9%; 61.1%, 71.0%; 86.1%, 41.9%; respectively. The model weighted combination was a better method to construct a combination model. The optimized weighted combination models composed of ABVS-based intratumoral and peritumoral radiomics features and clinical features may be potential biomarkers for the noninvasive and preoperative prediction of HER2 status in breast cancer.

3D Fractal Dimension Analysis: Prognostic Value of Right Ventricular Trabecular Complexity in Participants with Arrhythmogenic Cardiomyopathy.

BACKGROUND: Arrhythmogenic cardiomyopathy (ACM) is characterized by progressive myocardial fibro-fatty infiltration accompanied by trabecular disarray. Traditionally, two-dimensional (2D) instead of 3D fractal dimension (FD) analysis has been used to evaluate trabecular disarray. However, the prognostic value of trabecular disorder assessed by 3D FD measurement remains unclear. PURPOSE: To investigate the prognostic value of right ventricular trabecular complexity in ACM patients using 3D FD analysis based on cardiac MR cine images. STUDY TYPE: Retrospective. POPULATION: 85 ACM patients (mean age: 45 ± 17 years, 52 male). FIELD STRENGTH/SEQUENCE: 3.0T/cine imaging, T2-short tau inversion recovery (T2-STIR), and late gadolinium enhancement (LGE). ASSESSMENT: Using cine images, RV (right ventricular) volumetric and functional parameters were obtained. RV trabecular complexity was measured with 3D fractal analysis by box-counting method to calculate 3D-FD. Cox and logistic regression models were established to evaluate the prognostic value of 3D-FD for major adverse cardiac events (MACE). STATISTICAL TESTS: Cox regression and logistic regression to explore the prognostic value of 3D-FD. C-index, time-dependent receiver operating characteristic (ROC) curves and area under the ROC curve (AUC) to evaluate the incremental value of 3D-FD. Intraclass correlation coefficient for interobserver variability. P < 0.05 indicated statistical significance. RESULTS: 26 MACE were recorded during the 60 month follow-up (interquartile range: 48-67 months). RV 3D-FD significantly differed between ACM patients with MACE (2.67, interquartile range: 2.51 ~ 2.81) and without (2.52, interquartile range: 2.40 ~ 2.67) and was a significant independent risk factor for MACE (hazard ratio, 1.02; 95% confidence interval: 1.01, 1.04). In addition, prognostic model fitness was significantly improved after adding 3D-FD to RV global longitudinal strain, LV involvement, and 5-year risk score separately. DATA CONCLUSION: The myocardial trabecular complexity assessed through 3D FD analysis was found associated with MACE and provided incremental prognostic value beyond conventional ACM risk factors. EVIDENCE LEVEL: 4 TECHNICAL EFFICACY: Stage 1.

Mechanism study of social media overload on health self-efficacy and anxiety.

Previous studies have found that people's excessive use of social media may have a negative impact on their crisis coping skills. This study focuses on the effects of social media overload (information overload, communication overload and social overload) on anxiety and health self-efficacy during the COVID-19 pandemic. The study used an online questionnaire and partial least squares structural equation modeling (PLS-SEM) to obtain data (816 valid samples), analyze the data and validate the model. The findings revealed that social media overload (information overload, communication overload and social overload) triggers increased social media fatigue, which in turn leads to the development of anxiety and the consequence of diminished health self-efficacy; social media fatigue plays a partially mediating role between social media and health self-efficacy and a fully mediating role between communication overload and health self-efficacy. The results of this study can inform the development of crisis communication strategies during public health emergencies.

Relevant Research of Inflammatory Cytokines Spectrum in Peripheral Blood of Sudden Hearing Loss.

OBJECTIVE: To investigate whether there is a correlation between the inflammatory state and the pathogenesis and clinical features of sudden hearing loss (SHL) by studying the expression of inflammation-related cytokines in the peripheral blood of patients with SHL. METHODS: In this work, we analyzed the cytokine profiles of 48 analytes in 38 patients with SHL compared to 38 healthy donors using a multiplex immunoassay. This study used appropriate statistical methods to screen for inflammatory cytokines associated with the pathogenesis of SHL, to analyze their network correlation, and to analyze the relationship between clinical features of SHL and inflammatory cytokines. RESULTS: Several cytokines, including CTACK, Eotaxin, HGF, INF-α2, IFN-ß, IL-1ß, IL-1ra, IL-2Rα, IL-4, IL-7, IL-8, IL-9, IL-10, IL-12(p40), IL-13, MIG, ß-NGF, SCF, and TNF-α, exhibited significantly higher levels in the peripheral blood of the SHL group compared to the control group. An inflammatory network composed of multiple cytokines, including IL-1ß, is a risk factor for the development of SHL. CONCLUSION: This study identified several inflammatory cytokines with elevated expression, which may be linked with the onset of SHL. The results of this study also provide a basis for the theoretical hypothesis of inflammation in SHL. LEVEL OF EVIDENCE: III Laryngoscope, 2024.

The association between activation of the ERK1/2-NF-κB signaling pathway by TIMP2 expression and chronic renal allograft dysfunction in the CRAD rat model.

PURPOSE: The tissue inhibitor of metalloproteinase 2 (TIMP2), a natural inhibitor of matrix metalloproteinase (MMP), regulates inflammation, fibrosis, and cell proliferation. Chronic renal allograft dysfunction (CRAD) is a primary factor affecting the long-term survival of renal allografts. We assessed whether up-regulation of TIMP2 expression may affect the ERK1/2-NF-κB signaling pathway and CRAD development. METHODS: Lewis rats received orthotopic F344 kidney allografts to establish the classical CRAD model. The treatment group was injected with a lentivirus encoding a TIMP2-targeting small hairpin (sh)RNA (LTS) at 5 × 108 TU/ml monthly after kidney transplantation. A second CRAD group was injected with a lentivirus TIMP2-control vector (LTC). After 12 weeks, blood, urine, and kidney tissue were harvested to evaluate renal function and pathological examinations. Hematoxylin and eosin staining, Masson staining, and Periodic acid-Schiff staining were performed for renal histopathological evaluation according to the Banff criteria. TIMP2, phospho (p)-ERK1/2, p-p65 (NF-κB) expression levels were measured via immunohistochemical and Western blot analyses. RESULTS: Compared to the F344 and Lewis control groups, the expression of TIMP2, p-ERK1/2, and p-p65 were significantly higher in the CRAD and CRAD+LTC renal tissues (p < 0.05). There were also increased levels of serum creatinine, nitrogen, and 24 h urinary protein in these two groups (p < 0.05). Typical histopathological changes of CRAD were observed in the CRAD and CRAD+LTC groups. Administration of LTS effectively decreased the expression of TIMP2, p-ERK1/2, and p-P65, and reduced interstitial fibrosis and macrophage infiltration in the treatment group (p < 0.05). Additionally, MCP1 and ICAM-1, which are downstream cytokines of the NF-κB pathway, were also inhibited in the renal rat kidney from the LTS group (p < 0.05). Furthermore, renal function was well preserved in the LTS group compared to the CRAD group and CRAD+LTC group. CONCLUSION: A decrease of TIMP2 can alleviate the progression of inflammation in CRAD via inhibition of the ERK1/2-NF-κB signaling pathway.

Clinical Efficacy and Safety of Colistin Sulfate in the Treatment of Carbapenem-Resistant Organism Infections in Patients with Hematological Diseases.

INTRODUCTION: Carbapenem-resistant organisms (CRO) have emerged as a significant worldwide issue. However, the availability of efficacious antibiotics for treating CRO infections remains limited. Polymyxins, including colistin sulfate, represent the last-line therapeutic option against CRO infections. This study aims to retrospectively evaluate the clinical effectiveness and safety of colistin sulfate in managing CRO infections among patients with hematological diseases. METHODS: Between April 2022 and January 2023, a total of 118 hematological patients diagnosed with CRO infection were treated with colistin sulfate at Suzhou Hongci Hospital of Hematology. The assessment encompassed the clinical efficacy, bacterial clearance rate, adverse reactions, and 30-day all-cause mortality. RESULTS: The study found that the total effective rate of colistin sulfate in the treatment of CRO infection was 74.6%, with a bacterial clearance rate of 72.6%. Throughout the treatment, nephrotoxicity occurred in 7.6% of cases, neurotoxicity in 2.5% of cases, and the 30-day all-cause mortality rate was 22.9%. Multivariate logistic analysis revealed that the treatment course and combination medication with other antimicrobials were independent factors affecting the clinical efficacy of colistin sulfate. CONCLUSION: Our study demonstrates that the treatment of colistin sulfate can achieve high clinical efficacy and microbial responses, with a low risk of nephrotoxicity. This study provides evidence of the positive clinical efficacy and safety of colistin sulfate treatment in these patients. High-quality randomized controlled trials are still needed to further confirm the beneficial role of colistin sulfate.

Copper (II) complex supported on magnetic nanoparticles as a novel nanocatalyst for the synthesis of imidazo[1,2-a]pyridines.

Research on the synthesis of imidazo[1,2-a]pyridines has gained great importance among synthetic chemists because there have been numerous reports of their biological and medicinal activities. In this respect, we fabricated CuCl2 immobilized on Fe3O4 nanoparticles modified with 1,10-phenanthroline-5,6-diol [Fe3O4@Diol/Phen-CuCl2] and investigated its catalytic activity for the preparation of imidazo[1,2-a]pyridine derivatives through one-pot three-component reaction of 2-aminopyridines, aldehydes and terminal alkynes under ecofriendly conditions. FT-IR spectroscopy, EDX, SEM, TEM, XRD, TGA, VSM and ICP-OES techniques employed in order to identify the structure of the as-constructed Fe3O4@Diol/Phen-CuCl2 nanocatalyst. This catalytic system has a series of advantages such as the synthesis of imidazo[1,2-a]pyridine products with high yields in suitable time, performing the reactions in an environmentally friendly solvent (PEG), easy preparation of the catalyst with a simple method, and the recyclability of the Fe3O4@Diol/Phen-CuCl2 nanocatalyst.

CEST and nuclear Overhauser enhancement imaging with deep learning-extrapolated semisolid magnetization transfer reference: Scan-rescan reproducibility and reliability studies.

PURPOSE: To develop a novel MR physics-driven, deep-learning, extrapolated semisolid magnetization transfer reference (DeepEMR) framework to provide fast, reliable magnetization transfer contrast (MTC) and CEST signal estimations, and to determine the reproducibility and reliability of the estimates from the DeepEMR. METHODS: A neural network was designed to predict a direct water saturation and MTC-dominated signal at a certain CEST frequency offset using a few high-frequency offset features in the Z-spectrum. The accuracy, scan-rescan reproducibility, and reliability of MTC, CEST, and relayed nuclear Overhauser enhancement (rNOE) signals estimated from the DeepEMR were evaluated on numerical phantoms and in heathy volunteers at 3 T. In addition, we applied the DeepEMR method to brain tumor patients and compared tissue contrast with other CEST calculation metrics. RESULTS: The DeepEMR method demonstrated a high degree of accuracy in the estimation of reference MTC signals at ±3.5 ppm for APT and rNOE imaging, and computational efficiency (˜190-fold) compared with a conventional fitting approach. In addition, the DeepEMR method achieved high reproducibility and reliability (intraclass correlation coefficient = 0.97, intersubject coefficient of variation = 3.5%, and intrasubject coefficient of variation = 1.3%) of the estimation of MTC signals at ±3.5 ppm. In tumor patients, DeepEMR-based amide proton transfer images provided higher tumor contrast than a conventional MT ratio asymmetry image, particularly at higher B1 strengths (>1.5 µT), with a distinct delineation of the tumor core from normal tissue or peritumoral edema. CONCLUSION: The DeepEMR approach is feasible for measuring clean APT and rNOE effects in longitudinal and cross-sectional studies with low scan-rescan variability.

Deficiency of BCAT2-mediated branched-chain amino acid catabolism promotes colorectal cancer development.

OBJECTIVE: Branched-chain amino acid (BCAA) metabolism is involved in the development of colorectal cancer (CRC); however, the underlying mechanism remains unclear. Therefore, this study investigates the role of BCAA metabolism in CRC progression. METHODS: Dietary BCAA was administered to both azoxymethane-induced and azoxymethane/dextran sodium sulfate-induced CRC mouse models. The expression of genes related to BCAA metabolism was determined using RNA sequencing. Adjacent tissue samples, obtained from 58 patients with CRC, were subjected to quantitative real-time PCR and immunohistochemical analysis. Moreover, the suppressive role of branched-chain aminotransferase 2 (BCAT2) in cell proliferation, apoptosis, and xenograft mouse models was investigated. Alterations in BCAAs and activation of downstream pathways were also assessed using metabolic analysis and western blotting. RESULTS: High levels of dietary BCAA intake promoted CRC tumorigenesis in chemical-induced CRC and xenograft mouse models. Both the mRNA and protein levels of BCAT2 were decreased in tumor tissues of patients with CRC compared to those in normal tissues. Proliferation assays and xenograft models confirmed the suppressive role of BCAT2 in CRC progression. Furthermore, the accumulation of BCAAs caused by BCAT2 deficiency facilitated the chronic activation of mTORC1, thereby mediating the oncogenic effect of BCAAs. CONCLUSION: BCAT2 deficiency promotes CRC progression through inhibition of BCAAs metabolism and chronic activation of mTORC1.

Deep Learning for Discrimination of Hypertrophic Cardiomyopathy and Hypertensive Heart Disease on MRI Native T1 Maps.

BACKGROUND: Native T1 and radiomics were used for hypertrophic cardiomyopathy (HCM) and hypertensive heart disease (HHD) differentiation previously. The current problem is that global native T1 remains modest discrimination performance and radiomics requires feature extraction beforehand. Deep learning (DL) is a promising technique in differential diagnosis. However, its feasibility for discriminating HCM and HHD has not been investigated. PURPOSE: To examine the feasibility of DL in differentiating HCM and HHD based on T1 images and compare its diagnostic performance with other methods. STUDY TYPE: Retrospective. POPULATION: 128 HCM patients (men, 75; age, 50 years ± 16) and 59 HHD patients (men, 40; age, 45 years ± 17). FIELD STRENGTH/SEQUENCE: 3.0T; Balanced steady-state free precession, phase-sensitive inversion recovery (PSIR) and multislice native T1 mapping. ASSESSMENT: Compare HCM and HHD patients baseline data. Myocardial T1 values were extracted from native T1 images. Radiomics was implemented through feature extraction and Extra Trees Classifier. The DL network is ResNet32. Different input including myocardial ring (DL-myo), myocardial ring bounding box (DL-box) and the surrounding tissue without myocardial ring (DL-nomyo) were tested. We evaluate diagnostic performance through AUC of ROC curve. STATISTICAL TESTS: Accuracy, sensitivity, specificity, ROC, and AUC were calculated. Independent t test, Mann-Whitney U-test and Chi-square test were adopted for HCM and HHD comparison. P < 0.05 was considered statistically significant. RESULTS: DL-myo, DL-box, and DL-nomyo models showed an AUC (95% confidential interval) of 0.830 (0.702-0.959), 0.766 (0.617-0.915), 0.795 (0.654-0.936) in the testing set. AUC of native T1 and radiomics were 0.545 (0.352-0.738) and 0.800 (0.655-0.944) in the testing set. DATA CONCLUSION: The DL method based on T1 mapping seems capable of discriminating HCM and HHD. Considering diagnostic performance, the DL network outperformed the native T1 method. Compared with radiomics, DL won an advantage for its high specificity and automated working mode. LEVEL OF EVIDENCE: 4 TECHNICAL EFFICACY STAGE: 2.

A-D-A Type Nonfullerene Acceptors Synthesized by Core Segmentation and Isomerization for Realizing Organic Solar Cells with Low Nonradiative Energy Loss.

Reducing non-radiative recombination energy loss (ΔEnonrad ) in organic solar cells (OSCs) has been considered an effective method to improve device efficiency. In this study, the backbone of PTBTT-4F/4Cl is divided into D1-D2-D3 segments and reconstructed. The isomerized TPBTT-4F/4Cl obtains stronger intramolecular charge transfer (ICT), thus leading to elevated highest occupied molecular orbital (HOMO) energy level and reduced bandgap (Eg ). According to ELoss  = Eg- qVOC , the reduced Eg and enhanced open circuit voltage (VOC ) result in lower ELoss , indicating that ELoss has been effectively suppressed in the TPBTT-4F/4Cl based devices. Furthermore, compared to PTBTT derivatives, the isomeric TPBTT derivatives exhibit more planar molecular structure and closer intermolecular stacking, thus affording higher crystallinity of the neat films. Therefore, the reduced energy disorder and corresponding lower Urbach energy (Eu ) of the TPBTT-4F/4Cl blend films lead to low ELoss and high charge-carrier mobility of the devices. As a result, benefitting from synergetic control of molecular stacking and energetic offsets, a maximum power conversion efficiency (PCE) of 15.72% is realized from TPBTT-4F based devices, along with a reduced ΔEnonrad of 0.276 eV. This work demonstrates a rational method of suppressing VOC loss and improving the device performance through molecular design engineering by core segmentation and isomerization.

ReconFormer: Accelerated MRI Reconstruction Using Recurrent Transformer.

The accelerating magnetic resonance imaging (MRI) reconstruction process is a challenging ill-posed inverse problem due to the excessive under-sampling operation in k -space. In this paper, we propose a recurrent Transformer model, namely ReconFormer, for MRI reconstruction, which can iteratively reconstruct high-fidelity magnetic resonance images from highly under-sampled k -space data (e.g., up to 8× acceleration). In particular, the proposed architecture is built upon Recurrent Pyramid Transformer Layers (RPTLs). The core design of the proposed method is Recurrent Scale-wise Attention (RSA), which jointly exploits intrinsic multi-scale information at every architecture unit as well as the dependencies of the deep feature correlation through recurrent states. Moreover, benefiting from its recurrent nature, ReconFormer is lightweight compared to other baselines and only contains 1.1 M trainable parameters. We validate the effectiveness of ReconFormer on multiple datasets with different magnetic resonance sequences and show that it achieves significant improvements over the state-of-the-art methods with better parameter efficiency. The implementation code and pre-trained weights are available at https://github.com/guopengf/ReconFormer.

CHEK2 deficiency increase the response to PD-1 inhibitors by affecting the tumor immune microenvironment.

Immune checkpoint blockade (ICB) therapy has improved treatment effects in multiple cancers. Gene mutations in the DNA damage repair pathway (DDR) may cause genomic instability and may relate to the efficacy of ICB. Checkpoint kinase 2 (CHEK2) and polymerase epsilon (POLE) are important genes in the DDR. In this study, we aimed to study the impact of CHEK2 deficiency mutations on the response to ICB. We found that tumors with CHEK2 mutations had a significantly higher tumor mutational burden (TMB) compared to those with CHEK2-WT in a pancancer database. We noted that CHEK2 deficiency mutations potentiated the anti-tumor effect of anti-PD-1 therapy in MC38 and B16 tumor-bearing mice with the decrease of tumor volume and tumor weight after anti-PD-1 treatment. Mechanistically, CHEK2 deficiency tumors were with the increased cytotoxic CD8+ T-cell infiltration, especially cytotoxic CD8+ T cells, and modulated the tumor-immune microenvironment with an upregulated immune inflammatory pathway and antigen presentation pathway after anti-PD-1 treatment. Furthermore, murine models with POLE mutations confirmed that CHEK2 deficiency shaped similar mutational and immune landscapes as POLE mutations after anti-PD-1 treatment. Taken together, our results demonstrated that CHEK2 deficiency mutations may increase the response to ICB (eg. anti-PD-1) by influencing the tumor immune microenvironment. This indicated that CHEK2 deficiency mutations were a potentially predictive biomarker and CHEK2 deficiency may potentiate response to immunotherapy.