Simultaneously Regulated Highly Polarized and Long-Lived Valley Excitons in WSe2/GaN Heterostructures.

Interlayer excitons, with prolonged lifetimes and tunability, hold potential for advanced optoelectronics. Previous research on the interlayer excitons has been dominated by two-dimensional heterostructures. Here, we construct WSe2/GaN composite heterostructures, in which the doping concentration of GaN and the twist angle of bilayer WSe2 are employed as two ingredients for the manipulation of exciton behaviors and polarizations. The exciton energies in monolayer WSe2/GaN can be regulated continuously by the doping levels of the GaN substrate, and a remarkable increase in the valley polarizations is achieved. Especially in a heterostructure with 4°-twisted bilayer WSe2, a maximum polarization of 38.9% with a long lifetime is achieved for the interlayer exciton. Theoretical calculations reveal that the large polarization and long lifetime are attributed to the high exciton binding energy and large spin flipping energy during depolarization in bilayer WSe2/GaN. This work introduces a distinctive member of the interlayer exciton with a high degree of polarization and a long lifetime.

Manipulations of Electronic and Spin States in Co-Quantum Dot/WS2 Heterostructure on a Metal-Dielectric Composite Substrate by Controlling Interfacial Carriers.

Charge and spin are two intrinsic attributes of carriers governing almost all of the physical processes and operation principles in materials. Here, we demonstrate the manipulation of electronic and spin states in designed Co-quantum dot/WS2 (Co-QDs/WS2) heterostructures by employing a metal-dielectric composite substrate and via scanning tunneling microscope. By repeatedly scanning under a unipolar bias, switching the bias polarity, or applying a pulse through nonmagnetic or magnetic tips, the Co-QDs morphologies exhibit a regular and reproducible transformation between bright and dark dots. First-principles calculations reveal that these tunable characters are attributed to the variation of density of states and the transition of magnetic anisotropy energy induced by carrier accumulation. It also suggests that the metal-dielectric composite substrate is successful in creating the interfacial potential for carrier accumulation and realizes the electrically controllable modulations. These results will promote the exploration of electron-matter interactions in quantum systems and provide an innovative way to facilitate the development of spintronics.

Chirality-Dependent Valley Polarization in Magnetic van der Waals Heterostructures via Spin-Selective Charge Transfer.

Magnetic proximity interaction provides a promising route to manipulate the spin and valley degrees of freedom in van der Waals heterostructures. Here, we report a control of valley pseudospin in the WS2/MoSe2 heterostructure by utilizing the magnetic proximity effect of few-layered CrBr3 and, for the first time, observe a substantial difference in valley polarization of intra/interlayer excitons under different circularly polarized laser excitations, referred to as chirality-dependent valley polarization. Theoretical and experimental results reveal that the spin-selective charge transfer between MoSe2 and CrBr3, as well as between MoSe2 and WS2, is mostly responsible for the chiral feature of valley polarization in comparison with the proximity exchange field. This means that a long-distance manipulation of exciton behaviors in multilayer heterostructures can be achieved through spin-selective charge transfer. This work marks a significant advancement in the control of spin and valley pseudospin in multilayer structures.

Joint Exposure to Ambient Air Pollutants, Genetic Risk, and Ischemic Stroke: A Prospective Analysis in UK Biobank.

BACKGROUND: Our primary objective was to assess the association between joint exposure to various air pollutants and the risk of ischemic stroke (IS) and the modification of the genetic susceptibility. METHODS: This observational cohort study included 307 304 British participants from the United Kingdom Biobank, who were stroke-free and possessed comprehensive baseline data on genetics, air pollutant exposure, alcohol consumption, and dietary habits. All participants were initially enrolled between 2006 and 2010 and were followed up until 2022. An air pollution score was calculated to assess joint exposure to 5 ambient air pollutants, namely particulate matter with diameters equal to or <2.5 µm, ranging from 2.5 to 10 µm, equal to or <10 µm, as well as nitrogen oxide and nitrogen dioxide. To evaluate individual genetic risk, a polygenic risk score for IS was calculated for each participant. We adjusted for demographic, social, economic, and health covariates. Cox regression models were utilized to estimate the associations between air pollution exposure, polygenic risk score, and the incidence of IS. RESULTS: Over a median follow-up duration of 13.67 years, a total of 2476 initial IS events were detected. The hazard ratios (95% CI) of IS for per 10 µg/m3 increase in particulate matter with diameters equal to or <2.5 µm, ranging from 2.5 to 10 µm, equal to or <10 µm, nitrogen dioxide, and nitrogen oxide were 1.73 (1.33-2.14), 1.24 (0.88-1.70), 1.13 (0.89-1.33), 1.03 (0.98-1.08), and 1.04 (1.02-1.07), respectively. Furthermore, individuals in the highest quintile of the air pollution score exhibited a 29% to 66% higher risk of IS compared with those in the lowest quintile. Notably, participants with both high polygenic risk score and air pollution score had a 131% (95% CI, 85%-189%) greater risk of IS than participants with low polygenic risk score and air pollution score. CONCLUSIONS: Our findings suggested that prolonged joint exposure to air pollutants may contribute to an increased risk of IS, particularly among individuals with elevated genetic susceptibility to IS.

Efficacy and safety of different chemotherapy regimens concurrent with radiotherapy in the treatment of locally advanced cervical cancer.

BACKGROUND: Evaluate the efficacy and safety of different chemotherapy regimens concurrent with radiotherapy in treating locally advanced cervical cancer (LACC). METHODS: Retrospective data was collected from LACC patients who were treated at our institution. These patients were categorized into three groups: the single-agent cisplatin (DDP) chemoradiotherapy group, the paclitaxel plus cisplatin (TP) chemoradiotherapy group, and the nanoparticle albumin-bound (nab-) paclitaxel combined with cisplatin (nPP) chemoradiotherapy group. The primary endpoints were overall survival (OS) and progression-free survival (PFS) and the secondary endpoints were objective response rate (ORR) and incidence of adverse events (AEs). RESULTS: A total of 124 patients were enrolled (32 in the DDP group, 41 in the TP group, and 51 in the nPP group). There were differences in OS (P = 0.041, HR 0.527, 95% CI 0.314-0.884) and PFS (P = 0.003, HR 0.517, 95% CI 0.343-0.779) between the three groups. Notably, the 2-year OS rate was significantly higher in the nPP group compared to the DDP group (92.2% vs. 85.4%, P = 0.012). The 2-year PFS rates showed a marked increase in the TP group (78.0% vs. 59.4%, P = 0.048) and the nPP group (88.2% vs. 59.4%, P = 0.001) relative to the DPP group, with multiple comparisons indicating that the 2-year PFS rate was significantly superior in the nPP group versus the DDP group (88.2% vs. 59.4%, P = 0.001). Moreover, the ORR was also significantly higher in the nPP group than in the DDP group (P = 0.013); and no statistically significant differences were found in the incidence of AEs among the groups (P > 0.05). CONCLUSIONS: In LACC treatment, the two cisplatin-based doublet chemotherapy regimens are associated with better outcomes, with the nab-paclitaxel plus cisplatin regimen showing better efficacy than the paclitaxel plus cisplatin regimen. Furthermore, the AEs associated with these regimens were deemed tolerable. These findings could provide a reference for the clinical treatment of LACC. However, further prospective studies are needed to verify it.

An [18F]FDG PET/3D-ultrashort echo time MRI-based radiomics model established by machine learning facilitates preoperative assessment of lymph node status in non-small cell lung cancer.

OBJECTIVES: To develop an [18F]FDG PET/3D-UTE model based on clinical factors, three-dimensional ultrashort echo time (3D-UTE), and PET radiomics features via machine learning for the assessment of lymph node (LN) status in non-small cell lung cancer (NSCLC). METHODS: A total of 145 NSCLC patients (training, 101 cases; test, 44 cases) underwent whole-body [18F]FDG PET/CT and chest [18F]FDG PET/MRI were enrolled. Preoperative clinical factors and 3D-UTE, CT, and PET radiomics features were analyzed. The Mann-Whitney U test, LASSO regression, and SelectKBest were used for feature extraction. Five machine learning algorithms were used to establish prediction models, which were evaluated by the area under receiver-operator characteristic (ROC), DeLong test, calibration curves, and decision curve analysis (DCA). RESULTS: A prediction model based on random forest, consisting of four clinical factors, six 3D-UTE, and six PET radiomics features, was used as the final model for PET/3D-UTE. The AUCs of this model were 0.912 and 0.791 in the training and test sets, respectively, which not only showed different degrees of improvement over individual models such as clinical, 3D-UTE, and PET (AUC-training = 0.838, 0.834, and 0.828, AUC-test = 0.756, 0.745, and 0.768, respectively) but also achieved the similar diagnostic efficacy as the optimal PET/CT model (AUC-training = 0.890, AUC-test = 0.793). The calibration curves and DCA indicated good consistency (C-index, 0.912) and clinical utility of this model, respectively. CONCLUSION: The [18F]FDG PET/3D-UTE model based on clinical factors, 3D-UTE, and PET radiomics features using machine learning methods could noninvasively assess the LN status of NSCLC. CLINICAL RELEVANCE STATEMENT: A machine learning model of 18F-fluorodeoxyglucose positron emission tomography/ three-dimensional ultrashort echo time could noninvasively assess the lymph node status of non-small cell lung cancer, which provides a novel method with less radiation burden for clinical practice. KEY POINTS: • The 3D-UTE radiomics model using the PLS-DA classifier was significantly associated with LN status in NSCLC and has similar diagnostic performance as the clinical, CT, and PET models. • The [18F]FDG PET/3D-UTE model based on clinical factors, 3D-UTE, and PET radiomics features using the RF classifier could noninvasively assess the LN status of NSCLC and showed improved diagnostic performance compared to the clinical, 3D-UTE, and PET models. • In the assessment of LN status in NSCLC, the [18F]FDG PET/3D-UTE model has similar diagnostic efficacy as the [18F]FDG PET/CT model that incorporates clinical factors and CT and PET radiomics features.

Performance of node reporting and data system (node-RADS): a preliminary study in cervical cancer.

BACKGROUND: Node Reporting and Data System (Node-RADS) was proposed and can be applied to lymph nodes (LNs) across all anatomical sites. This study aimed to investigate the diagnostic performance of Node-RADS in cervical cancer patients. METHODS: A total of 81 cervical cancer patients treated with radical hysterectomy and LN dissection were retrospectively enrolled. Node-RADS evaluations were performed by two radiologists on preoperative MRI scans for all patients, both at the LN level and patient level. Chi-square and Fisher's exact tests were employed to evaluate the distribution differences in size and configuration between patients with and without LN metastasis (LNM) in various regions. The receiver operating characteristic (ROC) and the area under the curve (AUC) were used to explore the diagnostic performance of the Node-RADS score for LNM. RESULTS: The rates of LNM in the para-aortic, common iliac, internal iliac, external iliac, and inguinal regions were 7.4%, 9.3%, 19.8%, 21.0%, and 2.5%, respectively. At the patient level, as the NODE-RADS score increased, the rate of LNM also increased, with rates of 26.1%, 29.2%, 42.9%, 80.0%, and 90.9% for Node-RADS scores 1, 2, 3, 4, and 5, respectively. At the patient level, the AUCs for Node-RADS scores > 1, >2, > 3, and > 4 were 0.632, 0.752, 0.763, and 0.726, respectively. Both at the patient level and LN level, a Node-RADS score > 3 could be considered the optimal cut-off value with the best AUC and accuracy. CONCLUSIONS: Node-RADS is effective in predicting LNM for scores 4 to 5. However, the proportions of LNM were more than 25% at the patient level for scores 1 and 2, which does not align with the expected very low and low probability of LNM for these scores.

TRIM34 modulates influenza virus-activated programmed cell death by targeting Z-DNA-binding protein 1 for K63-linked polyubiquitination.

Z-DNA-binding protein 1 (ZBP1) is an innate sensor of influenza A virus (IAV) that participates in IAV-induced programmed cell death. Nevertheless, little is known about the upstream signaling pathways regulating ZBP1. We found that a member of the tripartite motif (TRIM) family, TRIM34, interacted with ZBP1 to promote its K63-linked polyubiquitination. Using a series of genetic approaches, we provide in vitro and in vivo evidence indicating that IAV triggered cell death and inflammatory responses via dependent on TRIM34/ZBP1 interaction. TRIM34 and ZBP1 expression and interaction protected mice from death during IAV infection owing to reduced inflammatory responses and epithelial damage. Additionally, analysis of clinical samples revealed that TRIM34 associates with ZBP1 and mediates ZBP1 polyubiquitination in vivo. Higher levels of proinflammatory cytokines correlated with higher levels of ZBP1 in IAV-infected patients. Taken together, we conclude that TRIM34 serves as a critical regulator of IAV-induced programmed cell death by mediating the K63-linked polyubiquitination of ZBP1.

Short-axis PET image quality improvement based on a uEXPLORER total-body PET system through deep learning.

PURPOSE: The axial field of view (AFOV) of a positron emission tomography (PET) scanner greatly affects the quality of PET images. Although a total-body PET scanner (uEXPLORER) with a large AFOV is more sensitive, it is more expensive and difficult to widely use. Therefore, we attempt to utilize high-quality images generated by uEXPLORER to optimize the quality of images from short-axis PET scanners through deep learning technology while controlling costs. METHODS: The experiments were conducted using PET images of three anatomical locations (brain, lung, and abdomen) from 335 patients. To simulate PET images from different axes, two protocols were used to obtain PET image pairs (each patient was scanned once). For low-quality PET (LQ-PET) images with a 320-mm AFOV, we applied a 300-mm FOV for brain reconstruction and a 500-mm FOV for lung and abdomen reconstruction. For high-quality PET (HQ-PET) images, we applied a 1940-mm AFOV during the reconstruction process. A 3D Unet was utilized to learn the mapping relationship between LQ-PET and HQ-PET images. In addition, the peak signal-to-noise ratio (PSNR) and structural similarity index measure (SSIM) were employed to evaluate the model performance. Furthermore, two nuclear medicine doctors evaluated the image quality based on clinical readings. RESULTS: The generated PET images of the brain, lung, and abdomen were quantitatively and qualitatively compatible with the HQ-PET images. In particular, our method achieved PSNR values of 35.41 ± 5.45 dB (p < 0.05), 33.77 ± 6.18 dB (p < 0.05), and 38.58 ± 7.28 dB (p < 0.05) for the three beds. The overall mean SSIM was greater than 0.94 for all patients who underwent testing. Moreover, the total subjective quality levels of the generated PET images for three beds were 3.74 ± 0.74, 3.69 ± 0.81, and 3.42 ± 0.99 (the highest possible score was 5, and the minimum score was 1) from two experienced nuclear medicine experts. Additionally, we evaluated the distribution of quantitative standard uptake values (SUV) in the region of interest (ROI). Both the SUV distribution and the peaks of the profile show that our results are consistent with the HQ-PET images, proving the superiority of our approach. CONCLUSION: The findings demonstrate the potential of the proposed technique for improving the image quality of a PET scanner with a 320 mm or even shorter AFOV. Furthermore, this study explored the potential of utilizing uEXPLORER to achieve improved short-axis PET image quality at a limited economic cost, and computer-aided diagnosis systems that are related can help patients and radiologists.

Amide Proton Transfer-Weighted Imaging and Multiple Models Intravoxel Incoherent Motion-Based 18 F-FDG PET/MRI for Predicting Progression-Free Survival in Non-Small Cell Lung Cancer.

BACKGROUND: Amide proton transfer-weighted imaging (APTWI) and multiple models intravoxel incoherent motion (IVIM) based 18 F-FDG PET/MR could reflect the microscopic information of the tumor from multiple perspectives. However, its value in the prognostic assessment of non-small cell lung cancer (NSCLC) still needs to be further explored. PURPOSE: To determine whether pretreatment APTWI, mono-, bi-, and stretched-exponential model IVIM, and 18 F-FDG PET-derived parameters of the primary lesion may be associated with progression-free survival (PFS) in NSCLC. STUDY TYPE: Prospective. POPULATION: Seventy-seven patients (mean age, 62 years, range, 20-81 years) with 37 men and 40 women were included. FIELD STRENGTH/SEQUENCE: 3.0 T 18 F-FDG PET/MRI, single shot echo planar imaging sequences for IVIM and fast spin-echo sequences with magnetization transfer pulses for APTWI. ASSESSMENT: Patient clinical characteristics (age, sex, smoke, subtype, TNM stage, and surgery), PFS (chest CT every 3 months, median follow-up was 18 months, range, 4-27 months), and APTWI (MTRasym(3.5 ppm)), IVIM (ADCstand , D, D*, f, DDC, and α), and 18 F-FDG PET (SUVmax , MTV, and TLG) parameters were recorded. STATISTICAL TESTS: Proportional hazards model, concordance index, calibration curve, decision curve analysis (DCA), and Log-rank test. A P value <0.05 was considered statistically significant. RESULTS: Histological subtype, TNM stage, MTV, D*, and MTRasym(3.5 ppm) were all independent predictors of PFS. A prediction model based on these predictors was developed with a C-index of 0.895 (95% CI: 0.839-0.951), which was significantly superior to each of the above predictors alone (C-index = 0.629, 0.707, 0.692, 0.678, and 0.558, respectively). The calibration curve and DCA indicated good consistency and clinical utility of the prediction model, respectively. Log-rank test results showed a significant difference in PFS between the high- and low-risk groups. DATA CONCLUSION: APTWI and multiple models IVIM based 18 F-FDG PET/MRI can be used for PFS assessment in NSCLC. EVIDENCE LEVEL: 3 TECHNICAL EFFICACY: Stage 2.

Deep learning-based dynamic PET parametric Ki image generation from lung static PET.

OBJECTIVES: PET/CT is a first-line tool for the diagnosis of lung cancer. The accuracy of quantification may suffer from various factors throughout the acquisition process. The dynamic PET parametric Ki provides better quantification and improve specificity for cancer detection. However, parametric imaging is difficult to implement clinically due to the long acquisition time (~ 1 h). We propose a dynamic parametric imaging method based on conventional static PET using deep learning. METHODS: Based on the imaging data of 203 participants, an improved cycle generative adversarial network incorporated with squeeze-and-excitation attention block was introduced to learn the potential mapping relationship between static PET and Ki parametric images. The image quality of the synthesized images was qualitatively and quantitatively evaluated by using several physical and clinical metrics. Statistical analysis of correlation and consistency was also performed on the synthetic images. RESULTS: Compared with those of other networks, the images synthesized by our proposed network exhibited superior performance in both qualitative and quantitative evaluation, statistical analysis, and clinical scoring. Our synthesized Ki images had significant correlation (Pearson correlation coefficient, 0.93), consistency, and excellent quantitative evaluation results with the Ki images obtained in standard dynamic PET practice. CONCLUSIONS: Our proposed deep learning method can be used to synthesize highly correlated and consistent dynamic parametric images obtained from static lung PET. KEY POINTS: • Compared with conventional static PET, dynamic PET parametric Ki imaging has been shown to provide better quantification and improved specificity for cancer detection. • The purpose of this work was to develop a dynamic parametric imaging method based on static PET images using deep learning. • Our proposed network can synthesize highly correlated and consistent dynamic parametric images, providing an additional quantitative diagnostic reference for clinicians.

Chronic kidney disease in a giant panda (Ailuropoda melanoleuca): a case report.

BACKGROUND: Chronic kidney disease (CKD) is a common cause of morbidity and mortality in captive wildlife species. However, CKD has been rarely documented in giant pandas. CASE PRESENTATION: The following report describes a case of an eight-year-old female giant panda showing clinical signs of epistaxis, bloody diarrhea, polyuria, azotemia and anemia. The animal died despite of supportive treatments. Necropsy was performed. Grossly, both kidneys were shrunken and scarred with pallor. Subcutis edema and petechia on the epicardium of the heart were observed. The tissue samples were made into paraffin sections and stained by H.E and special staining including Periodic Acid-Schiff (PAS), von Kossa, Masson's trichrome, Phosphotungstic acid-hematoxylin (PTAH), and Congo red. Histopathology examination revealed severe chronic tubulointerstitial nephritis with marked interstitial fibrosis, glomerulosclerosis, tubular atrophy and calcification in kidneys, and acute necrotizing hemorrhagic myocarditis with calcification in heart. Other lesions included intestinal hemorrhage, hepatic fatty degeneration and necrosis with hemosiderin, and splenic hemosiderin. CONCLUSIONS: In summary, chronic kidney disease was finally diagnosed based on the association of clinical, gross, and histopathological findings. Heart failure secondary to CKD is the leading cause of death in this giant panda. The potential cause of CKD in this animal is possibly due to long term and uncontrolled hypertension. Blood pressure monitoring is essential in establishing the diagnosis and management of hypertension in giant panda.

Radiomics-based machine learning approach in differentiating fibro-adipose vascular anomaly from venous malformation.

BACKGROUND: As a complex vascular malformation, fibro-adipose vascular anomaly was first proposed in 2014. Its overlap with other vascular malformations regarding imaging and clinical features often leads to misdiagnosis and improper management. OBJECTIVE: To construct a radiomics-based machine learning model to help radiologists differentiate fibro-adipose vascular anomaly from common venous malformations. MATERIALS AND METHODS: We retrospectively analyzed 178 children, adolescents and young adults with vascular malformations (41 fibro-adipose vascular anomaly and 137 common vascular malformation cases) who underwent MRI before surgery between May 2012 to January 2021. We extracted radiomics features from T1-weighted images and fat-saturated (FS) T2-weighted images and further selected features through least absolute shrinkage and selection operator (LASSO) and Boruta methods. We established eight weighted logistic regression classification models based on various combinations of feature-selection strategies (LASSO or Boruta) and sequence types (single- or multi-sequence). Finally, we evaluated the performance of each model by the mean area under the receiver operating characteristics curve (ROC-AUC), sensitivity and specificity in 10 runs of repeated k-fold (k = 10) cross-validation. RESULTS: Two multi-sequence models based on axial FS T2-W, coronal FS T2-W and axial T1-W images showed promising performance. The LASSO-based multi-sequence model achieved an AUC of 97%±3.8, a sensitivity of 94%±12.4 and a specificity of 89%±9.0. The Boruta-based multi-sequence model achieved an AUC of 97%±3.7, a sensitivity of 95%±10.5 and a specificity of 87%±9.0. CONCLUSION: The radiomics-based machine learning model can provide a promising tool to help distinguish fibro-adipose vascular anomaly from common venous malformations.

Galunisertib plus neoadjuvant chemoradiotherapy in patients with locally advanced rectal cancer: a single-arm, phase 2 trial.

BACKGROUND: TGF-ß is an immunosuppressive cytokine that is upregulated in colorectal cancer. TGF-ß blockade improved response to chemoradiotherapy in preclinical models of colorectal adenocarcinoma. We aimed to test the hypothesis that adding the TGF-ß type I receptor kinase inhibitor galunisertib to neoadjuvant chemoradiotherapy would improve pathological complete response rates in patients with locally advanced rectal cancer. METHODS: This was an investigator-initiated, single-arm, phase 2 study done in two medical centres in Portland (OR, USA). Eligible patients had previously untreated, locally advanced, rectal adenocarcinoma, stage IIA-IIIC or IV as per the American Joint Committee on Cancer; Eastern Cooperative Oncology Group status 0-2; and were aged 18 years or older. Participants completed two 14-day courses of oral galunisertib 150 mg twice daily, before and during fluorouracil-based chemoradiotherapy (intravenous fluorouracil 225 mg/m2 over 24 h daily 7 days per week during radiotherapy or oral capecitabine 825 mg/m2 twice per day 5 days per week during radiotherapy; radiotherapy consisted of 50·4-54·0 Gy in 28-30 fractions). 5-9 weeks later, patients underwent response assessment. Patients with a complete response could opt for non-operative management and proceed to modified FOLFOX6 (intravenous leucovorin 400 mg/m2 on day 1, intravenous fluorouracil 400 mg/m2 on day 1 then 2400 mg/m2 over 46 h, and intravenous oxaliplatin 85 mg/m2 on day 1 delivered every 2 weeks for eight cycles) or CAPEOX (intravenous oxaliplatin 130 mg/m2 on day 1 and oral capecitabine 1000 mg/m2 twice daily for 14 days every 3 weeks for four cycles). Patients with less than complete response underwent surgical resection. The primary endpoint was complete response rate, which was a composite of pathological complete response in patients who proceeded to surgery, or clinical complete response maintained at 1 year after last therapy in patients with non-operative management. Safety was a coprimary endpoint. Both endpoints were assessed in the intention-to-treat population. This study is registered with ClinicalTrials.gov, NCT02688712, and is active but not recruiting. FINDINGS: Between Oct 19, 2016, and Aug 31, 2020, 38 participants were enrolled. 25 (71%) of the 35 patients who completed chemoradiotherapy proceeded to total mesorectal excision surgery, five (20%) of whom had pathological complete responses. Ten (29%) patients had non-operative management, three (30%) of whom ultimately chose to have total mesorectal excision. Two (67%) of those three patients had pathological complete responses. Of the remaining seven patients in the non-operative management group, five (71%) had clinical complete responses at 1 year after their last modified FOLFOX6 infusion. In total, 12 (32% [one-sided 95% CI ≥19%]) of 38 patients had a complete response. Common grade 3 adverse events during treatment included diarrhoea in six (16%) of 38 patients, and haematological toxicity in seven (18%) patients. Two (5%) patients had grade 4 adverse events, one related to chemoradiotherapy-induced diarrhoea and dehydration, and the other an intraoperative ischaemic event. No treatment-related deaths occurred. INTERPRETATION: The addition of galunisertib to neoadjuvant chemoradiotherapy in patients with locally advanced rectal cancer improved the complete response rate to 32%, was well tolerated, and warrants further assessment in randomised trials. FUNDING: Eli Lilly via ExIST program, The Providence Foundation.

Identifying the individual metabolic abnormities from a systemic perspective using whole-body PET imaging.

INTRODUCTION: Distinct physiological states arise from complex interactions among the various organs present in the human body. PET is a non-invasive modality with numerous successful applications in oncology, neurology, and cardiology. However, while PET imaging has been applied extensively in detecting focal lesions or diseases, its potential in detecting systemic abnormalities is seldom explored, mostly because total-body imaging was not possible until recently. METHODS: In this context, the present study proposes a framework capable of constructing an individual metabolic abnormality network using a subject's whole-body 18F-FDG SUV image and a normal control database. The developed framework was evaluated in the patients with lung cancer, the one discharged after suffering from Covid-19 disease, and the one that had gastrointestinal bleeding with the underlying cause unknown. RESULTS: The framework could successfully capture the deviation of these patients from healthy subjects at the level of both system and organ. The strength of the altered network edges revealed the abnormal metabolic connection between organs. The overall deviation of the network nodes was observed to be highly correlated to the organ SUV measures. Therefore, the molecular connectivity of glucose metabolism was characterized at a single subject level. CONCLUSION: The proposed framework represents a significant step toward the use of PET imaging for identifying metabolic dysfunction from a systemic perspective. A better understanding of the underlying biological mechanisms and the physiological interpretation of the interregional connections identified in the present study warrant further research.

Parametric image generation with the uEXPLORER total-body PET/CT system through deep learning.

PURPOSE: Total-body dynamic positron emission tomography/computed tomography (PET/CT) provides much sensitivity for clinical imaging and research, bringing new opportunities and challenges regarding the generation of total-body parametric images. This study investigated parametric [Formula: see text] images directly generated from static PET images without an image-derived input function on a 2-m total-body PET/CT scanner (uEXPLORER) using a deep learning model to significantly reduce the dynamic scanning time and improve patient comfort. METHODS: [Formula: see text]F-Fluorodeoxyglucose ([Formula: see text]F-FDG) 2-m total-body PET/CT image pairs were acquired for 200 patients (scanned once) with two protocols: one parametric PET image (60 min, 0[Formula: see text]60 min) and one static PET image (10 min, range of 50[Formula: see text]60 min). A deep learning model was implemented to predict parametric [Formula: see text] images from the static PET images. Evaluation metrics, including the peak signal-to-noise ratio (PSNR), structural similarity index measure (SSIM), and normalized mean square error (NMSE), were calculated for a 10-fold cross-validation assessment. Moreover, image quality was assessed by two nuclear medicine physicians in terms of clinical readings. RESULTS: The synthetic parametric PET images were qualitatively and quantitatively consistent with the reference images. In particular, the global mean SSIM between the synthetic and reference parametric [Formula: see text] images exceeded 0.9 across all test patients. On the other hand, the overall subjective quality of the synthetic parametric PET images was 4.00 ± 0.45 (the highest possible rating is 5) according to the two expert nuclear medicine physicians. CONCLUSION: The findings illustrated the feasibility of the proposed technique and its potential to reduce the required scanning duration for 2-m total-body dynamic PET/CT systems. Moreover, this study explored the potential of direct parametric image generation with uEXPLORER. Deep learning technologies may output high-quality synthetic parametric images, and the validation of clinical applications and the interpretability of network models still need further research in future works.

Evaluation of Amide Proton Transfer-Weighted Imaging for Lung Cancer Subtype and Epidermal Growth Factor Receptor: A Comparative Study With Diffusion and Metabolic Parameters.

BACKGROUND: Lung cancer is one of the most devastating diseases worldwide, and it is meaningful to assess the subtype and epidermal growth factor receptor (EGFR) status in lung cancer noninvasively by imaging methods. PURPOSE: To differentiate noninvasively small cell lung cancer (SCLC) from nonsmall cell lung cancer (NSCLC), and EGFR mutation-type from wild-type NSCLC by comparing amide proton transfer-weighted imaging (APTWI), diffusion-weighted imaging (DWI), and 2-[18 F]-fluoro-2-deoxy-d-glucose positron emission tomography (18 F-FDG PET). STUDY TYPE: Prospective. POPULATION: A total of 99 patients with lung cancer. FIELD STRENGTH/SEQUENCE: APTWI and DWI at 18 F-FDG PET/MRI 3.0 T. ASSESSMENT: The apparent diffusion coefficient (ADC), magnetization transfer ratio asymmetry (MTRasym [3.5 ppm]), maximum standardized uptake value (SUVmax ), metabolic tumor volume (MTV), and total lesion glycolysis (TLG) were calculated and compared. STATISTICAL TESTS: Individual sample t-test, Mann-Whitney U test, Logistic regression, and P < 0.05 were considered statistically significant. RESULTS: In NSCLC, MTRasym (3.5 ppm), MTV, and TLG were significantly lower and ADC was significantly higher than in SCLC; MTRasym (3.5 ppm) was significantly higher and SUVmax , MTV, and TLG were significantly lower in EGFR mutation-type NSCLC than in EGFR wild-type NSCLC. In the identification of SCLC and NSCLC, MTRasym (3.5 ppm), ADC, and MTV were independent predictors, the AUCs of the combination of independent predictors, MTV, TLG, MTRasym (3.5 ppm), and ADC were 0.942, 0.875, 0.843, 0.814, and 0.687, respectively. In the identification of EGFR mutation-type and wild-type NSCLC, MTRasym (3.5 ppm) and MTV were independent predictors, the AUCs of the combination of independent predictors, TLG, MTV, MTRasym (3.5 ppm), and SUVmax were 0.919, 0.834, 0.813, 0.795, and 0.771, respectively. DATA CONCLUSION: In the noninvasive assessment of lung cancer subtype and EGFR status, APTWI has similar utility to diffusion and metabolic parameters. LEVEL OF EVIDENCE: 2 TECHNICAL EFFICACY STAGE: 2.

Analysis of CT signs, radiomic features and clinical characteristics for delta variant COVID-19 patients with different vaccination status.

OBJECTIVE: To explore the characteristics of peripheral blood, high resolution computed tomography (HRCT) imaging and the radiomics signature (RadScore) in patients infected with delta variant virus under different coronavirus disease (COVID-19) vaccination status. METHODS: 123 patients with delta variant virus infection collected from November 1, 2021 to March 1, 2022 were analyzed retrospectively. According to COVID-19 vaccination Status, they were divided into three groups: Unvaccinated group, partially vaccinated group and full vaccination group. The peripheral blood, chest HRCT manifestations and RadScore of each group were analyzed and compared. RESULTS: The mean lymphocyte count 1.22 ± 0.49 × 10^9/L, CT score 7.29 ± 3.48, RadScore 0.75 ± 0.63 in the unvaccinated group; The mean lymphocyte count 1.55 ± 0.70 × 10^9/L, CT score 5.27 ± 2.72, RadScore 1.03 ± 0.46 in the partially vaccinated group; The mean lymphocyte count 1.87 ± 0.70 × 10^9/L, CT score 3.59 ± 3.14, RadScore 1.23 ± 0.29 in the fully vaccinated group. There were significant differences in lymphocyte count, CT score and RadScore among the three groups (all p < 0.05); Compared with the other two groups, the lung lesions in the unvaccinated group were more involved in multiple lobes, of which 26 cases involved the whole lung. CONCLUSIONS: Through the analysis of clinical features, pulmonary imaging features and radiomics, we confirmed the positive effect of COVID-19 vaccine on pulmonary inflammatory symptoms and lymphocyte count (immune system) during delta mutant infection.

Plasmacytoid Dendritic Cell-Derived Type I Interferon Is Involved in Helicobacter pylori Infection-Induced Differentiation of Schlafen 4-Expressing Myeloid-Derived Suppressor Cells.

During chronic infection with Helicobacter pylori, Schlafen 4-expressing myeloid-derived suppressor cells (SLFN4+ MDSCs) create a microenvironment favoring intestinal metaplasia and neoplastic transformation. SLFN4 can be induced by alpha interferon (IFN-α), which is mainly secreted from plasmacytoid dendritic cells (pDCs). This study tested the hypothesis that Helicobacter pylori infection promotes SLFN4+ MDSC differentiation by inducing pDCs to secrete IFN-α. C57BL/6 mice were gavaged with H. pylori, and infection lasted 2, 4, or 6 months. Mouse pDCs were isolated from bone marrow of wild-type C57BL/6J mice. The results showed that H. pylori infection increased the number of SLFN4+ MDSCs by inducing IFN-α expression in mice. Further mechanistic experiments unraveled that IFN-α induced SLFN4 transcription by binding to the Slfn4 promoter. Furthermore, H. pylori infection stimulated pDCs to secrete IFN-α by activating the TLR9-MyD88-IRF7 pathway. Collectively, Helicobacter pylori infection promotes SLFN4+ MDSC differentiation by inducing secretion of IFN-α from pDCs.

Multiplex immunofluorescence to measure dynamic changes in tumor-infiltrating lymphocytes and PD-L1 in early-stage breast cancer.

BACKGROUND: The H&E stromal tumor-infiltrating lymphocyte (sTIL) score and programmed death ligand 1 (PD-L1) SP142 immunohistochemistry assay are prognostic and predictive in early-stage breast cancer, but are operator-dependent and may have insufficient precision to characterize dynamic changes in sTILs/PD-L1 in the context of clinical research. We illustrate how multiplex immunofluorescence (mIF) combined with statistical modeling can be used to precisely estimate dynamic changes in sTIL score, PD-L1 expression, and other immune variables from a single paraffin-embedded slide, thus enabling comprehensive characterization of activity of novel immunotherapy agents. METHODS: Serial tissue was obtained from a recent clinical trial evaluating loco-regional cytokine delivery as a strategy to promote immune cell infiltration and activation in breast tumors. Pre-treatment biopsies and post-treatment tumor resections were analyzed by mIF (PerkinElmer Vectra) using an antibody panel that characterized tumor cells (cytokeratin-positive), immune cells (CD3, CD8, CD163, FoxP3), and PD-L1 expression. mIF estimates of sTIL score and PD-L1 expression were compared to the H&E/SP142 clinical assays. Hierarchical linear modeling was utilized to compare pre- and post-treatment immune cell expression, account for correlation of time-dependent measurement, variation across high-powered magnification views within each subject, and variation between subjects. Simulation methods (Monte Carlo, bootstrapping) were used to evaluate the impact of model and tissue sample size on statistical power. RESULTS: mIF estimates of sTIL and PD-L1 expression were strongly correlated with their respective clinical assays (p < .001). Hierarchical linear modeling resulted in more precise estimates of treatment-related increases in sTIL, PD-L1, and other metrics such as CD8+ tumor nest infiltration. Statistical precision was dependent on adequate tissue sampling, with at least 15 high-powered fields recommended per specimen. Compared to conventional t-testing of means, hierarchical linear modeling was associated with substantial reductions in enrollment size required (n = 25➔n = 13) to detect the observed increases in sTIL/PD-L1. CONCLUSION: mIF is useful for quantifying treatment-related dynamic changes in sTILs/PD-L1 and is concordant with clinical assays, but with greater precision. Hierarchical linear modeling can mitigate the effects of intratumoral heterogeneity on immune cell count estimations, allowing for more efficient detection of treatment-related pharmocodynamic effects in the context of clinical trials. TRIAL REGISTRATION: NCT02950259 .