Parallel processing model for low-dose computed tomography image denoising.

Low-dose computed tomography (LDCT) has gained increasing attention owing to its crucial role in reducing radiation exposure in patients. However, LDCT-reconstructed images often suffer from significant noise and artifacts, negatively impacting the radiologists' ability to accurately diagnose. To address this issue, many studies have focused on denoising LDCT images using deep learning (DL) methods. However, these DL-based denoising methods have been hindered by the highly variable feature distribution of LDCT data from different imaging sources, which adversely affects the performance of current denoising models. In this study, we propose a parallel processing model, the multi-encoder deep feature transformation network (MDFTN), which is designed to enhance the performance of LDCT imaging for multisource data. Unlike traditional network structures, which rely on continual learning to process multitask data, the approach can simultaneously handle LDCT images within a unified framework from various imaging sources. The proposed MDFTN consists of multiple encoders and decoders along with a deep feature transformation module (DFTM). During forward propagation in network training, each encoder extracts diverse features from its respective data source in parallel and the DFTM compresses these features into a shared feature space. Subsequently, each decoder performs an inverse operation for multisource loss estimation. Through collaborative training, the proposed MDFTN leverages the complementary advantages of multisource data distribution to enhance its adaptability and generalization. Numerous experiments were conducted on two public datasets and one local dataset, which demonstrated that the proposed network model can simultaneously process multisource data while effectively suppressing noise and preserving fine structures. The source code is available at https://github.com/123456789ey/MDFTN .

Partial Hepatectomy and Ablation for Survival of Early-Stage Hepatocellular Carcinoma Patients: A Bayesian Emulation Analysis.

Partial hepatectomy and ablation therapy are two widely used surgical procedures for localized early-stage hepatocellular carcinoma (HCC) patients. This article aimed to evaluate their relative effectiveness in terms of overall survival. An emulation analysis approach was first developed based on the Bayesian technique. We estimated propensity scores via Bayesian logistic regression and adopted a weighted Bayesian Weibull accelerated failure time (AFT) model incorporating prior information contained in the published literature. With the Surveillance, Epidemiology, and End Results (SEER)-Medicare data, an emulated target trial with rigorously defined inclusion/exclusion criteria and treatment regimens for early-stage HCC patients over 66 years old was developed. For the main cohort with tumor size less than or equal to 5 cm, a total of 1146 patients were enrolled in the emulated trial, with 301 and 845 in the partial hepatectomy and ablation arms, respectively. The analysis suggested ablation to be significantly associated with inferior overall survival (hazard ratio [HR] = 1.35; 95% credible interval [CrI]: 1.14, 1.60). For the subgroup with tumor size less than or equal to 3 cm, there was no significant difference in overall survival between the two arms (HR = 1.15; 95% CrI: 0.88, 1.52). Overall, the comparative treatment effect of ablation and partial hepatectomy on survival remains inconclusive. This finding may provide further insight into HCC clinical treatment.

Arginine catabolism is essential to polymyxin dependence in Acinetobacter baumannii.

Polymyxins are often the only effective antibiotics against the "Critical" pathogen Acinetobacter baumannii. Worryingly, highly polymyxin-resistant A. baumannii displaying dependence on polymyxins has emerged in the clinic, leading to diagnosis and treatment failures. Here, we report that arginine metabolism is essential for polymyxin-dependent A. baumannii. Specifically, the arginine degradation pathway was significantly altered in polymyxin-dependent strains compared to wild-type strains, with critical metabolites (e.g., L-arginine and L-glutamate) severely depleted and expression of the astABCDE operon significantly increased. Supplementation of arginine increased bacterial metabolic activity and suppressed polymyxin dependence. Deletion of astA, the first gene in the arginine degradation pathway, decreased phosphatidylglycerol and increased phosphatidylethanolamine levels in the outer membrane, thereby reducing the interaction with polymyxins. This study elucidates the molecular mechanism by which arginine metabolism impacts polymyxin dependence in A. baumannii, underscoring its critical role in improving diagnosis and treatment of life-threatening infections caused by "undetectable" polymyxin-dependent A. baumannii.

[Spatio-temporal Evolution of Drought and Its Impact on Key Resources in the Important Ecological Functional Area: A Case Study in the Yellow River Basin].

This study explored the characteristics of spatial and temporal changes in drought in the Yellow River Basin from 2001 to 2020 based on TVPDI, surface runoff, vegetation net primary productivity, and grain yield data. Further, the effects of drought on water resources, grain resources, and vegetation resources were also analyzed using data spatialization methods, slope trend analysis, and Pearson correlation analysis. The results showed that:① The spatial distribution of drought in the Yellow River basin was stepped from southeast to northwest, and 60.6 % of the basin was in drought. The overall trend of drought in the basin was decreasing annually, and 94 % of the basin was gradually changing from drought to wet conditions, and the trend of drought from spring to winter decreased first and then increased. ② From the spatial and temporal changes in important resources in the basin, 53 % of the key surface runoff areas showed an increasing trend and were mainly located in the southwest of the basin; the net primary productivity (NPP) of vegetation and grain yield of food resources also showed an increasing trend. ③ Drought and the three types of resources showed significant spatial correlations, and the higher the degree of drought, the more significant the effects on surface runoff, vegetation productivity, and grain yield. However, the important resources in areas that had become wetter in recent years had not increased significantly, which indicated that the effects of drought on the three types of important resources had a time lag, and their lags had significant differences in spatial distribution and geographical differentiation patterns. This study has important theoretical implications for agricultural production, drought mitigation, and ecological conservation in the Yellow River Basin.

Gut microbiota, a key to understanding the knowledge gaps on micro-nanoplastics-related biological effects and biodegradation.

Micro-nanoplastics (MNPs) pollution as a global environmental issue has received increasing interest in recent years. MNPs can enter and accumulate in the organisms including human beings mainly via ingestion and inhalation, and large amounts of foodborne MNPs have been frequently detected in human intestinal tracts and fecal samples. MNPs regulate the structure composition and metabolic functions of gut microbiota, which may cause the imbalance of intestinal ecosystems of the hosts and further mediate the occurrence and development of various diseases. In addition, a growing number of MNPs-degrading strains have been isolated from organismal feces. MNPs-degraders colonize the plastic surfaces and form the biofilms, and the long-chain polymers of MNPs can be biologically depolymerized into short chains. In general, MNPs are gradually degraded into small molecule substances (e.g., N2, CH4, H2O, and CO2) via a series of enzymatic catalyses, mainly including biodeterioration, fragmentation, assimilation, and mineralization. In this review, we outline the current progress of MNPs effects on gut microbiota and MNPs degradation by gut microbiota, which provide a certain theoretical basis for fully understanding the knowledge gaps on MNPs-related biological effect and biodegradation.

Modelling lung infection with Klebsiella pneumoniae after murine traumatic brain injury.

Pneumonia is a common comorbidity in patients with severe traumatic brain injury (TBI), and is associated with increased morbidity and mortality. In this study, we established a model of intratracheal Klebsiella pneumoniae administration in young adult male and female mice, at 4 days following an experimental TBI, to investigate how K. pneumoniae infection influences acute post-TBI outcomes. A dose-response curve determined the optimal dose of K. pneumoniae for inoculation (1 x 10^6 colony forming units), and administration at 4 days post-TBI resulted in transient body weight loss and sickness behaviors (hypoactivity and acute dyspnea). K. pneumoniae infection led to an increase in pro-inflammatory cytokines in serum and bronchoalveolar lavage fluid at 24 h post-infection, in both TBI and sham (uninjured) mice. By 7 days, when myeloperoxidase + neutrophil numbers had returned to baseline in all groups, lung histopathology was observed with an increase in airspace size in TBI + K. pneumoniae mice compared to TBI + vehicle mice. In the brain, increased neuroinflammatory gene expression was observed acutely in response to TBI, with an exacerbated increase in Ccl2 and Hmox1 in TBI + K. pneumoniae mice compared to either TBI or K. pneumoniae alone. However, the presence of neuroinflammatory immune cells in the injured brain, and the extent of damage to cortical and hippocampal brain tissue, was comparable between K. pneumoniae and vehicle-treated mice by 7 days. Examination of the fecal microbiome across a time course did not reveal any pronounced effects of either injury or K. pneumoniae on bacterial diversity or abundance. Together, these findings demonstrate that K. pneumoniae lung infection after TBI induces an acute and transient inflammatory response, primarily localized to the lungs with some systemic effects. However, this infection had minimal impact on secondary injury processes in the brain following TBI. Future studies are needed to evaluate the potential longer-term consequences of this dual-hit insult.

Feature shared multi-decoder network using complementary learning for Photon counting CT ring artifact suppression.

BACKGROUND: Photon-counting computed tomography (Photon counting CT) utilizes photon-counting detectors to precisely count incident photons and measure their energy. These detectors, compared to traditional energy integration detectors, provide better image contrast and material differentiation. However, Photon counting CT tends to show more noticeable ring artifacts due to limited photon counts and detector response variations, unlike conventional spiral CT. OBJECTIVE: To comprehensively address this issue, we propose a novel feature shared multi-decoder network (FSMDN) that utilizes complementary learning to suppress ring artifacts in Photon counting CT images. METHODS: Specifically, we employ a feature-sharing encoder to extract context and ring artifact features, facilitating effective feature sharing. These shared features are also independently processed by separate decoders dedicated to the context and ring artifact channels, working in parallel. Through complementary learning, this approach achieves superior performance in terms of artifact suppression while preserving tissue details. RESULTS: We conducted numerous experiments on Photon counting CT images with three-intensity ring artifacts. Both qualitative and quantitative results demonstrate that our network model performs exceptionally well in correcting ring artifacts at different levels while exhibiting superior stability and robustness compared to the comparison methods. CONCLUSIONS: In this paper, we have introduced a novel deep learning network designed to mitigate ring artifacts in Photon counting CT images. The results illustrate the viability and efficacy of our proposed network model as a new deep learning-based method for suppressing ring artifacts.

PfbZIP85 Transcription Factor Mediates ω-3 Fatty Acid-Enriched Oil Biosynthesis by Down-Regulating PfLPAT1B Gene Expression in Plant Tissues.

The basic leucine zipper (bZIP) transcription factor (TF) family is one of the biggest TF families identified so far in the plant kingdom, functioning in diverse biological processes including plant growth and development, signal transduction, and stress responses. For Perilla frutescens, a novel oilseed crop abundant in polyunsaturated fatty acids (PUFAs) (especially α-linolenic acid, ALA), the identification and biological functions of bZIP members remain limited. In this study, 101 PfbZIPs were identified in the perilla genome and classified into eleven distinct groups (Groups A, B, C, D, E, F, G, H, I, S, and UC) based on their phylogenetic relationships and gene structures. These PfbZIP genes were distributed unevenly across 18 chromosomes, with 83 pairs of them being segmental duplication genes. Moreover, 78 and 148 pairs of orthologous bZIP genes were detected between perilla and Arabidopsis or sesame, respectively. PfbZIP members belonging to the same subgroup exhibited highly conserved gene structures and functional domains, although significant differences were detected between groups. RNA-seq and RT-qPCR analysis revealed differential expressions of 101 PfbZIP genes during perilla seed development, with several PfbZIPs exhibiting significant correlations with the key oil-related genes. Y1H and GUS activity assays evidenced that PfbZIP85 downregulated the expression of the PfLPAT1B gene by physical interaction with the promoter. PfLPAT1B encodes a lysophosphatidate acyltransferase (LPAT), one of the key enzymes for triacylglycerol (TAG) assembly. Heterogeneous expression of PfbZIP85 significantly reduced the levels of TAG and UFAs (mainly C18:1 and C18:2) but enhanced C18:3 accumulation in both seeds and non-seed tissues in the transgenic tobacco lines. Furthermore, these transgenic tobacco plants showed no significantly adverse phenotype for other agronomic traits such as plant growth, thousand seed weight, and seed germination rate. Collectively, these findings offer valuable perspectives for understanding the functions of PfbZIPs in perilla, particularly in lipid metabolism, showing PfbZIP85 as a suitable target in plant genetic improvement for high-value vegetable oil production.

Impact of Metal Salt Oxidants and Preparation Technology on Efficacy of Bacterial Cellulose/Polypyrrole Flexible Conductive Fiber Membranes.

In this study, we investigated the preparation and characterization of flexible conductive fiber membranes (BC/PPy) using different metal salt oxidants on bacterial cellulose (BC) and pyrrole (Py) in the in situ polymerization and co-blended methods, respectively. The effects of these oxidants, namely, ferric chloride hexahydrate (FeCl3·6H2O) and silver nitrate (AgNO3), on the structural characterization, conductivity, resistance value and thermal stability of the resulting materials were assessed by scanning electron microscopy (SEM), Fourier transform infrared (FTIR) spectroscopy, Raman spectroscopy and X-ray photoelectron spectroscopy (XPS). A comparative study revealed that the BC/PPy conductive fiber membrane prepared using FeCl3·6H2O as the oxidant had a resistance value of 12 Ω, while the BC/PPy conductive fiber membrane prepared using AgNO3 as the oxidant had an electrical resistance value of 130 Ω. The conductivity of the same molar ratio of BC/PPy prepared using FeCl3·6H2O as an oxidant was 10 times higher than that of the BC/PPy prepared using AgNO3 as an oxidant. Meanwhile, the resistance values of the conductive fiber membranes prepared from BC and PPy by the co-blended method were much higher than the BC/PPy prepared by in situ polymerization. SEM and XPS analyses revealed that when FeCl3·6H2O was used as the oxidant, the Fe-doped polypyrrole conductive particles could form uniform and dense conductive layers on the BC nanofiber surfaces. These two metal salt oxidants demonstrated differences in the binding sites between PPy and BC.

Association between liver enzymes and type 2 diabetes: a real-world study.

Aim: This study aimed to examine the association of liver enzymes, including alanine aminotransferase (ALT), aspartate aminotransferase (AST), and gamma-glutamyl-transferase (GGT), with type 2 diabetes (T2D) risk, particularly their dose-response relationship. Methods: This cross-sectional study enrolled participants aged >20 years old who underwent physical examination at our local hospital from November 2022 to May 2023. A generalized additive model (GAM) was fit to assess the dose-response relationship between liver enzymes and T2D risk. Furthermore, data from the UK Biobank (n=217,533) and National Health and Nutrition Examination Survey (NHANES 2011-2018; n= 15,528) were analyzed to evaluate whether the dose-response relationship between liver enzymes and T2D differed by population differences. Results: A total of 14,100 participants were included (1,155 individuals with T2D and 12,945 individuals without diabetes) in the analysis. GAM revealed a non-linear relationship between liver enzymes and T2D risk (P non-linear < 0.001). Specifically, T2D risk increased with increasing ALT and GGT levels (range, <50 IU/L) and then plateaued when ALT and GGT levels were >50 IU/L. Elevated AST within a certain range (range, <35 IU/L) decreased the risk of T2D, whereas mildly elevated AST (>35 IU/L) became a risk factor for T2D. The UK Biobank and NHANES data analysis also showed a similar non-linear pattern between liver enzymes and T2D incidence. Conclusion: Liver enzymes were non-linearly associated with T2D risk in different populations, including China, the UK, and the US. Elevated ALT and GGT levels, within a certain range, could increase T2D risk. More attention should be given to liver enzyme levels for early lifestyle intervention and early T2D prevention. Further studies are necessary to explore the mechanism of the non-linear association between liver enzymes and T2D risk.

Circulating Tumor Reactive KIR+CD8+ T cells Suppress Anti-Tumor Immunity in Patients with Melanoma.

Effective anti-tumor immunity is largely driven by cytotoxic CD8+ T cells that can specifically recognize tumor antigens. However, the factors which ultimately dictate successful tumor rejection remain poorly understood. Here we identify a subpopulation of CD8+ T cells which are tumor antigen-specific in patients with melanoma but resemble KIR+CD8+ T cells with a regulatory function (Tregs). These tumor antigen-specific KIR+CD8+ T cells are detectable in both the tumor and the blood, and higher levels of this population are associated with worse overall survival. Our findings therefore suggest that KIR+CD8+ Tregs are tumor antigen-specific but uniquely suppress anti-tumor immunity in patients with melanoma.

Dietary novel alkaline protease from Bacillus licheniformis improves broiler meat nutritional value and modulates intestinal microbiota and metabolites.

BACKGROUND: Different types of exogenous protease supplements have a positive impact on animal performance, but their effects on the nutritional value of meat and the gut microbial community of broilers have not been extensively studied. The objective of this investigation was to determine the impact of supplementation with a novel alkaline protease derived from Bacillus licheniformis (at doses of 0, 100, 200, 300, and 400 g/t) on the fatty acid and amino acid profiles, inosine monophosphate (IMP) levels, total volatile basic nitrogen (TVB-N) content found within the breast muscle, as well as the impact on the cecal microbiota and metabolites. RESULTS: Supplementation with 200-400 g/t of the novel protease resulted in a significant elevation in the concentration of essential amino acids (P < 0.001), flavor amino acids (P < 0.001), and total protein (P = 0.013) within the breast muscle. Results derived from the 16S rRNA sequencing and untargeted metabolomics analysis of the cecal content revealed that the novel protease reshaped the cecal microbial and metabolite profiles. In particular, it led to increased relative abundances of Bacteroides, Lactobacillus, Alistipes, and Eubacterium, while simultaneously causing a reduction in the metabolites of D-lactic acid and malonic acid. Moreover, correlation analyses unveiled significant relationships between distinct microbes and metabolites with the contents of IMP, fatty acids, and amino acids in the broiler's breast muscle. CONCLUSION: In summary, the novel protease regulated the intestinal microbial community and metabolism, thereby inducing changes in the compositions of fatty acids and amino acids profiles, as well as IMP levels in broiler meat. These alterations significantly contributed to the enhancement of the nutritional value and flavor of the meat.

Clinical Evidence of the Relationship Between Alanine Aminotransferase and Diabetic Kidney Disease.

Aim: Multiple studies have investigated the association between alanine aminotransferase (ALT) and diabetes mellitus (DM); however, only a few studies have specifically examined the relationship between ALT and diabetic kidney disease (DKD). This study aimed to investigate the relationship between ALT and DKD using clinical data. Methods: A cross-sectional study was conducted on 668 individuals that included non-DM (N=281), DM without DKD (N=160), and DKD (N=227) patients. A generalized additive model (GAM) was used to examine the dose-response relationship between ALT and DKD risk. We also analyzed the data from the US National Health and Nutrition Examination Survey (NHANES) 2015-2018 using the same statistical methods; 4481, 1110, and 671 individuals were included in the non-DM, DM without DKD, and DKD groups, respectively. Results: The changes in ALT activity among the non-DM, DM without DKD, and DKD groups showed a similar pattern in both our clinical data and the NHANES dataset. ALT activity increases with the onset of DM, whereas ALT activity decreases when DM progresses to DKD. The GAM revealed a nonlinear U-shaped relationship between ALT and DKD risk in the two datasets, and the lowest range of ALT was 40-50 IU/L. Both lower (<40 IU/L) and higher (>50 IU/L) ALT activity were found to be positively associated with DKD risk. Conclusion: A U-shaped nonlinear association between ALT and DKD was found in our clinical data and NHANES data. DKD risk was increased by both lower or higher ALT activity. To confirm the causality of nonlinear relationship, larger prospective studies or Mendelian randomization analysis are required.

PD-1 Blockade Induces Reactivation of Nonproductive T-Cell Responses Characterized by NF-κB Signaling in Patients with Pancreatic Cancer.

PURPOSE: Pancreatic ductal adenocarcinoma (PDAC) trials have evaluated CTLA-4 and/or PD-(L)1 blockade in patients with advanced disease in which bulky tumor burden and limited time to develop antitumor T cells may have contributed to poor clinical efficacy. Here, we evaluated peripheral blood and tumor T cells from patients with PDAC receiving neoadjuvant chemoradiation plus anti-PD-1 (pembrolizumab) versus chemoradiation alone. We analyzed whether PD-1 blockade successfully reactivated T cells in the blood and/or tumor to determine whether lack of clinical benefit could be explained by lack of reactivated T cells versus other factors. EXPERIMENTAL DESIGN: We used single-cell transcriptional profiling and TCR clonotype tracking to identify TCR clonotypes from blood that match clonotypes in the tumor. RESULTS: PD-1 blockade increases the flux of TCR clonotypes entering cell cycle and induces an IFNγ signature like that seen in patients with other GI malignancies who respond to PD-1 blockade. However, these reactivated T cells have a robust signature of NF-κB signaling not seen in cases of PD-1 antibody response. Among paired samples between blood and tumor, several of the newly cycling clonotypes matched activated T-cell clonotypes observed in the tumor. CONCLUSIONS: Cytotoxic T cells in the blood of patients with PDAC remain sensitive to reinvigoration by PD-1 blockade, and some have tumor-recognizing potential. Although these T cells proliferate and have a signature of IFN exposure, they also upregulate NF-κB signaling, which potentially counteracts the beneficial effects of anti-PD-1 reinvigoration and marks these T cells as non-productive contributors to antitumor immunity. See related commentary by Lander and DeNardo, p. 474.

Polysaccharide-Targeting Lipid Nanoparticles to Kill Gram-Negative Bacteria.

The rapid increase and spread of Gram-negative bacteria resistant to many or all existing treatments threaten a return to the preantibiotic era. The presence of bacterial polysaccharides that impede the penetration of many antimicrobials and protect them from the innate immune system contributes to resistance and pathogenicity. No currently approved antibiotics target the polysaccharide regions of microbes. Here, describe monolaurin-based niosomes, the first lipid nanoparticles that can eliminate bacterial polysaccharides from hypervirulent Klebsiella pneumoniae, are described. Their combination with polymyxin B shows no cytotoxicity in vitro and is highly effective in combating K. pneumoniae infection in vivo. Comprehensive mechanistic studies have revealed that antimicrobial activity proceeds via a multimodal mechanism. Initially, lipid nanoparticles disrupt polysaccharides, then outer and inner membranes are destabilized and destroyed by polymyxin B, resulting in synergistic cell lysis. This novel lipidic nanoparticle system shows tremendous promise as a highly effective antimicrobial treatment targeting multidrug-resistant Gram-negative pathogens.

Utilizing Ceftazidime/Avibactam Therapeutic Drug Monitoring in the Treatment of Neurosurgical Meningitis Caused by Difficult-to-Treat Resistant Pseudomonas aeruginosa and KPC-Producing Enterobacterales.

Background: Central nervous system (CNS) infections caused by Klebsiella pneumoniae carbapenemase (KPC)-producing Enterobacterales and difficult-to-treat resistant (DTR) Pseudomonas aeruginosa represent a formidable clinical challenge. Antimicrobial regimens that efficiently penetrate the cerebrospinal fluid (CSF) and achieve sufficient concentrations associated with microbiologic and clinical cure are limited. We evaluated therapy with ceftazidime-avibactam (CAZ-AVI) in order to guide precise dosing in the treatment of CNS infections. Methods: Therapeutic drug monitoring (TDM) was performed in 3 patients with health care-associated ventriculitis and meningitis (HAVM) using CAZ-AVI 2.5 g infused intravenously every 8 hours as standard and extended infusion. Simultaneous CSF and plasma samples were obtained throughout the dosing interval in each patient. Concentrations of CAZ and AVI were determined by liquid chromatography/mass spectrometry. Results: Bacterial identification revealed KPC-producing Klebsiella pneumoniae (KPC-Kp), DTR Pseudomonas aeruginosa, and KPC-producing Enterobacter cloacae (KPC-Ent.c). All isolates were resistant to carbapenems. The minimum inhibitory concentrations (MICs) of CAZ-AVI were 0.25/4, 4/4, and 0.25/4 µg/mL, respectively. CAZ and AVI concentrations were determined in CSF samples ranging from 29.0 to 15.0 µg/mL (CAZ component) and 4.20 to 0.92 µg/mL (AVI component), respectively. AVI achieved concentrations ≥1 µg/mL in 11 out of 12 CSF samples collected throughout the dosing interval. Clinical and microbiologic cure were attained in all patients. Conclusions: Postinfusion concentrations of CAZ-AVI were measured in plasma and CSF samples obtained from 3 patients with complicated CNS infections caused by antimicrobial-resistant isolates. The measured concentrations revealed that standard CAZ and AVI exposures sufficiently attained values correlating to 50% fT > MIC, which are associated with efficient bacterial killing.

Comparative Effectiveness Analysis of Lumpectomy and Mastectomy for Elderly Female Breast Cancer Patients: A Deep Learning-based Big Data Analysis.

Objectives: To evaluate the comparative effectiveness of treatments, a randomized clinical trial remains the gold standard but can be challenged by a high cost, a limited sample size, an inability to fully reflect the real world, and feasibility concerns. The objective is to showcase a big data approach that takes advantage of large electronic medical record (EMR) data to emulate clinical trials. To overcome the limitations of regression analysis, a deep learning-based analysis pipeline was developed. Study Design and Setting: Lumpectomy (breast-conserving surgery) and mastectomy are the two most commonly used surgical procedures for early-stage female breast cancer patients. An emulation trial was designed using the Surveillance, Epidemiology, and End Results (SEER)-Medicare data to evaluate their relative effectiveness in overall survival. The analysis pipeline consisted of a propensity score step, a weighted survival analysis step, and a bootstrap inference step. Results: A total of 65,997 subjects were enrolled in the emulated trial, with 50,704 and 15,293 in the lumpectomy and mastectomy arms, respectively. The two surgery procedures had comparable effects in terms of overall survival (survival year change = 0.08, 95% confidence interval (CI): -0.08, 0.25) for the elderly SEER-Medicare early-stage female breast cancer patients. Conclusion: This study demonstrated the power of "mining large EMR data + deep learning-based analysis," and the proposed analysis strategy and technique can be potentially broadly applicable. It provided convincing evidence of the comparative effectiveness of lumpectomy and mastectomy.

Diagnostic value of WB-DWI versus 18F-FDG PET/CT for the detection of multiple myeloma.

Background: Whole-body diffusion-weighted imaging (WB-DWI) is commonly used for the detection of multiple myeloma (MM). Comparative data on the efficiency of WB-DWI compared with F-18 fluoro-2-deoxy-d-glucose positron emission tomography-computed tomography (18F-FDG PET/CT) to detect MM is limited. Methods: This was a retrospective, single-center study of 22 patients with MM enrolled from January 2018 to December 2019. All patients underwent WB-DWI and 18F-FDG PET/CT. Pathological and clinical manifestations, as well as radiologic follow-up, were used for diagnosis. The overall accuracy, sensitivity, specificity, positive predictive value, and negative predictive value of both methods were compared. The apparent diffusion coefficient (ADC) values of MM lesions and false-positive lesions were estimated. Results: A total of 214 MM bone lesions were evaluated. There was no significant difference in the accuracy of WB-DWI and PET/CT (86.92 versus 88.32%). Though WB-DWI had a higher sensitivity (99.26% versus84.56%) and PET-CT had a higher specificity (96.10% versus 64.56%), these differences were not statistically significant. There was a statistically significant difference in PPV (83.33% versus 96.64%) and NPV (98.08% versus 77.89%) of WB-DWI and PET/CT, respectively. The ADC value for MM lesions was significantly lower than that for false-positive lesions (P < 0.001). Receiver operating curve analysis showed that the AUC was 0.846, and when the cut-off value was 0.745 × 10-3 mm2/s, the sensitivity and specificity were 86.3 and 83.4%, respectively, which distinguished MM lesions from non-MM lesions. Conclusion: WB-DWI and PET-CT scans have similar overall accuracy for detecting MM lesions. The higher PPV of PET-CT and NPV of WB-DWI make them complementary imaging modalities. The ADC value for MM lesions is significantly lower than that for false-positive lesions. An ADC cutoff value of 0.745 × 10-3 mm2/s results in sensitivity and specificity of 86.3 and 83.4%, respectively.

Genome-Wide Analysis of Glycerol-3-Phosphate Acyltransferase (GPAT) Family in Perilla frutescens and Functional Characterization of PfGPAT9 Crucial for Biosynthesis of Storage Oils Rich in High-Value Lipids.

Glycerol-3-phosphate acyltransferase (GPAT) catalyzes the first step in triacylglycerol (TAG) biosynthesis. However, GPAT members and their functions remain poorly understood in Perilla frutescens, a special edible-medicinal plant with its seed oil rich in polyunsaturated fatty acids (mostly α-linolenic acid, ALA). Here, 14 PfGPATs were identified from the P. frutescens genome and classified into three distinct groups according to their phylogenetic relationships. These 14 PfGPAT genes were distributed unevenly across 11 chromosomes. PfGPAT members within the same subfamily had highly conserved gene structures and four signature functional domains, despite considerable variations detected in these conserved motifs between groups. RNA-seq and RT-qPCR combined with dynamic analysis of oil and FA profiles during seed development indicated that PfGPAT9 may play a crucial role in the biosynthesis and accumulation of seed oil and PUFAs. Ex vivo enzymatic assay using the yeast expression system evidenced that PfGPAT9 had a strong GPAT enzyme activity crucial for TAG assembly and also a high substrate preference for oleic acid (OA, C18:1) and ALA (C18:3). Heterogeneous expression of PfGPAT9 significantly increased total oil and UFA (mostly C18:1 and C18:3) levels in both the seeds and leaves of the transgenic tobacco plants. Moreover, these transgenic tobacco lines exhibited no significant negative effect on other agronomic traits, including plant growth and seed germination rate, as well as other morphological and developmental properties. Collectively, our findings provide important insights into understanding PfGPAT functions, demonstrating that PfGPAT9 is the desirable target in metabolic engineering for increasing storage oil enriched with valuable FA profiles in oilseed crops.

Intertumoral lineage diversity and immunosuppressive transcriptional programs in well-differentiated gastroenteropancreatic neuroendocrine tumors.

Neuroendocrine tumors (NETs) are rare cancers that most often arise in the gastrointestinal tract and pancreas. The fundamental mechanisms driving gastroenteropancreatic (GEP)-NET growth remain incompletely elucidated; however, the heterogeneous clinical behavior of GEP-NETs suggests that both cellular lineage dynamics and tumor microenvironment influence tumor pathophysiology. Here, we investigated the single-cell transcriptomes of tumor and immune cells from patients with gastroenteropancreatic NETs. Malignant GEP-NET cells expressed genes and regulons associated with normal, gastrointestinal endocrine cell differentiation, and fate determination stages. Tumor and lymphoid compartments sparsely expressed immunosuppressive targets commonly investigated in clinical trials, such as the programmed cell death protein-1/programmed death ligand-1 axis. However, infiltrating myeloid cell types within both primary and metastatic GEP-NETs were enriched for genes encoding other immune checkpoints, including VSIR (VISTA), HAVCR2 (TIM3), LGALS9 (Gal-9), and SIGLEC10. Our findings highlight the transcriptomic heterogeneity that distinguishes the cellular landscapes of GEP-NET anatomic subtypes and reveal potential avenues for future precision medicine therapeutics.