Comparison of Short-term and Long-term Outcomes after Different Reconstructions between Totally Laparoscopic Distal Gastrectomy and Laparoscopic-assisted Distal Gastrectomy for Gastric Cancer: a Retrospective Analysis at a High-volume Center.

Background: The short-term and long-term outcomes of laparoscopic-assisted distal gastrectomy (LADG) and totally laparoscopic distal gastrectomy (TLDG) have been subject to controversy with various reconstruction techniques of Billroth-I, Billroth-II, Roux-en-Y, and Uncut. This study aims to compare the short-term and long-term outcomes of LADG and TLDG as well as the outcomes of different anastomoses. Methods: This study enrolled patients with gastric cancer at the First Affiliated Hospital of Nanjing Medical University (NMUH) between 2017 and 2021. Postoperative complications were classified according to the Clavien-Dindo grade. Exclusion criteria included metachronous and synchronous malignancy and palliative surgery. The Kaplan-Meier analysis was applied to assess 5-year prognosis between two groups. Results: This study included 1221 cases with an overall complication rate of 17.37% for LADG, which was significantly higher than TLDG's 10.72%. The incidence of anastomosis-related complications was 4.79% for LADG and 1.13% lower for TLDG. LADG and TLDG did not show significant difference for Grade III-V complications and resected lymph nodes. The postoperative stay was shorter for TLDG than LADG, and R-Y had a longer postoperative stay than B-II and Uncut after combining LADG and TLDG. The operation time was shorter in TLDG cases than that in LADG cases. The 5-year OS of the TLDG group was not significantly better than that of the LADG group. Conclusion: TLDG is superior in overall complication rate, anastomosis-related complication rate, postoperative stay and operation time to LADG. No difference of OS was observed between LADG and TLDG. Four anastomoses had no convincing evidence of being superior in complications rates, post-op stay, and harvested lymph nodes to each other.

Multi-omics landscapes reveal heterogeneity in long COVID patients characterized with enhanced neutrophil activity.

BACKGROUND: Omicron variant impacts populations with its rapid contagiousness, and part of patients suffered from persistent symptoms termed as long COVID. The molecular and immune mechanisms of this currently dominant global variant leading to long COVID remain unclear, due to long COVID heterogeneity across populations. METHODS: We recruited 66 participants in total, 22 out of 66 were healthy control without COVID-19 infection history, and 22 complaining about long COVID symptoms 6 months after first infection of Omicron, referred as long COVID (LC) Group. The left ones were defined as non-long COVID (NLC) Group. We profiled them via plasma neutralizing antibody titer, SARS-CoV-2 viral load, transcriptomic and proteomics screening, and machine learning. RESULTS: No serum residual SARS-CoV-2 was observed in the participants 6 months post COVID-19 infection. No significant difference in neutralizing antibody titers was found between the long COVID (LC) Group and the non-long COVID (NLC) Group. Transcriptomic and proteomic profiling allow the stratification of long COVID into neutrophil function upregulated (NU-LC) and downregulated types (ND-LC). The NU-LC, identifiable through a refined set of 5 blood gene markers (ABCA13, CEACAM6, CRISP3, CTSG and BPI), displays evidence of relatively higher neutrophil counts and function of degranulation than the ND-LC at 6 months after infection, while recovered at 12 months post COVID-19. CONCLUSION: The transcriptomic and proteomic profiling revealed heterogeneity among long COVID patients. We discovered a subgroup of long COVID population characterized by neutrophil activation, which might associate with the development of psychiatric symptoms and indicate a higher inflammatory state. Meanwhile, a cluster of 5 genes was manually curated as the most potent discriminators of NU-LC from long COVID population. This study can serve as a foundational exploration of the heterogeneity in the pathogenesis of long COVID and assist in therapeutic targeting and detailed epidemiological investigation of long COVID.

Pokemon inhibits Bim transcription to promote the proliferation, anti-anoikis, invasion, histological grade, and dukes stage of colorectal neoplasms.

PURPOSE: This study aims to determine whether Pokemon regulates Bim activity in colorectal carcinoma (CRC) carcinogenesis. METHODS: Clinical tissue samples were analyzed to detect the expression and clinicopathological significance of Pokemon and Bim in CRC. Proliferation, apoptosis, and invasion assays were conducted to identify the regulatory effect of Pokemon on Bim. The combined treatment effects of Pokemon knockdown and diamminedichloroplatinum (DDP) were also examined. RESULTS: Immunohistochemical analysis of 80 samples of colorectal epithelia (CRE), 80 cases of colorectal adenoma (CRA), and 160 of CRC samples revealed protein expression rates of 23.8%, 38.8%, and 70.6% for Pokemon, and 88.8%, 73.8%, and 31.9% for Bim, respectively. A significant negative correlation was observed between Pokemon and Bim expression across the CRE, CRA, and CRC lesion stages. In CRC, higher Pokemon and lower Bim expression correlated with higher histological grades, advanced Dukes stages, and increased cancer invasion. In both LoVo and HCT116 cells, overexpression of Pokemon significantly reduced Bim expression, leading to increased proliferation, resistance to anoikis, and cell invasion. Additionally, Pokemon overexpression significantly decreased DDP-induced Bim expression, reduction of anti-apoptosis and invasion, whereas Pokemon knockdown resulted in the opposite effects. CONCLUSION: These findings suggest that Pokemon inhibits Bim transcription, thereby promoting CRC proliferation, resistance to apoptosis, invasion, and advancing histological grade and Dukes staging. Pokemon knockdown enhances the therapeutic efficacy of DDP in the treatment of CRC.

Real-world effectiveness and safety of Baloxavir Marboxil or Oseltamivir in outpatients with uncomplicated influenza A: an ambispective, observational, multi-center study.

Introduction: Baloxavir Marboxil is a per oral small-molecule antiviral for the treatment of influenza. While the efficacy and safety of Baloxavir Marboxil have been thoroughly characterized across an extensive clinical trial, studies on the effectiveness of Baloxavir Marboxil in a real-world setting are still scarce. Methods: We conducted an ambispective, observational, multi-center study that enrolled uncomplicated in-fluenza outpatients treated with Baloxavir Marboxil or Oseltamivir in East China. The primary endpoint was time from treatment to alleviation of all influenza symptoms (TTAIS). The secondary endpoints included time from treatment to alleviation of fever (TTAF) and household transmission during the duration of influenza. Results: A total of 509 patients were enrolled. The median TTAIS in the Baloxavir Marboxil group and the Oseltamivir group was 28.0 h (IQR, 20.0 to 50.0) and 48.0 h (IQR, 30.0 to 67.0), respectively. The median TTAF in the Baloxavir Marboxil group and the Oseltamivir group was 18 h (IQR, 10.0-24.0) and 30.0 h (IQR, 19.0-48.0). In the COX multivariable analysis, Baloxavir Marboxil reduced the duration of influenza symptoms (HR = 1.36 [95%CI:1.12-1.64], p = 0.002) and the duration of fever (HR = 1.93 [95%CI:1.48-2.52], p < 0.001) compared to Oseltamivir. When antiviral drugs were given within 12-48 h after symptom onset, the Baloxavir Marboxil group had a significantly shorter TTAIS compared to the Oseltamivir group. There was no significant difference in the rate of adverse events between the two group (p = 0.555). Discussion: Baloxavir Marboxil was superior to Oseltamivir in alleviating influenza symptoms in outpatients with uncomplicated influenza. Our findings suggested that compared to Oseltamivir, Baloxavir Marboxil might be more appropriate for patients with influenza 12- 48 h after symptom onset.

The reduced frequency of CD39+CD73+ B cell subsets in SLE patients is correlated with disease activity.

BACKGROUND: Systemic lupus erythematosus (SLE) is a heterogeneous autoimmune disease characterized by immune mechanisms dysregulation, leading to the production of diverse autoantibodies. However, the immune pathways underlying B-cell function and phenotypic abnormalities related to SLE pathogenesis remain incompletely understood. OBJECTIVE: To explore new markers of SLE activity and potential targets for SLE immunotherapy. METHODS: Collect peripheral blood mononuclear cells (PBMCs) from SLE patients and healthy controls (HC). Use flow cytometry to detect CD39 and CD73 expression on B cell subsets and enzyme-linked immunosorbent assay (ELISA) to measure adenosine (ADO) concentrations in SLE patients' serum. Compare CD39+CD73+ B cell subsets frequency and ADO concentrations in SLE patients and HC group. Additionally, analyze the correlation between CD39+CD73+ B cell subsets frequency and clinical laboratory parameters. RESULTS: CD39 and CD73 are simultaneously highly expressed on CD19+ B cell subsets, with significantly lower frequency of CD39+CD73+ B cell subsets in SLE patients compared to HC group. This frequency negatively correlates with Systemic Lupus Erythematosus Disease Activity Index (SLEDAI), C-reactive protein (CRP), and anti-double-stranded DNA (anti-dsDNA) antibodies, while positively correlating with IgM and prothrombin time (PT). Additionally, the frequency of CD39+CD73+ B cell subsets is significantly negatively correlated with IL-6 and IFN-α. In vitro cell experiments demonstrate that adenosine significantly inhibits R848-induced inflammatory cytokine production in a dose-dependent manner. CONCLUSION: The frequency of CD39+CD73+ B cell subsets of SLE patients is decreased, correlating with clinical laboratory parameters and disease activity. Simultaneously, ADO concentration in the patients' serum is reduced. The CD39+CD73+ B cell/ADO pathway may represent a novel immunotherapy strategy for SLE.

Lightweight tomato ripeness detection algorithm based on the improved RT-DETR.

Tomatoes, widely cherished for their high nutritional value, necessitate precise ripeness identification and selective harvesting of mature fruits to significantly enhance the efficiency and economic benefits of tomato harvesting management. Previous studies on intelligent harvesting often focused solely on identifying tomatoes as the target, lacking fine-grained detection of tomato ripeness. This deficiency leads to the inadvertent harvesting of immature and rotten fruits, resulting in economic losses. Moreover, in natural settings, uneven illumination, occlusion by leaves, and fruit overlap hinder the precise assessment of tomato ripeness by robotic systems. Simultaneously, the demand for high accuracy and rapid response in tomato ripeness detection is compounded by the need for making the model lightweight to mitigate hardware costs. This study proposes a lightweight model named PDSI-RTDETR to address these challenges. Initially, the PConv_Block module, integrating partial convolution with residual blocks, replaces the Basic_Block structure in the legacy backbone to alleviate computing load and enhance feature extraction efficiency. Subsequently, a deformable attention module is amalgamated with intra-scale feature interaction structure, bolstering the capability to extract detailed features for fine-grained classification. Additionally, the proposed slimneck-SSFF feature fusion structure, merging the Scale Sequence Feature Fusion framework with a slim-neck design utilizing GSConv and VoVGSCSP modules, aims to reduce volume of computation and inference latency. Lastly, by amalgamating Inner-IoU with EIoU to formulate Inner-EIoU, replacing the original GIoU to expedite convergence while utilizing auxiliary frames enhances small object detection capabilities. Comprehensive assessments validate that the PDSI-RTDETR model achieves an average precision mAP50 of 86.8%, marking a 3.9% enhancement over the original RT-DETR model, and a 38.7% increase in FPS. Furthermore, the GFLOPs of PDSI-RTDETR have been diminished by 17.6%. Surpassing the baseline RT-DETR and other prevalent methods regarding precision and speed, it unveils its considerable potential for detecting tomato ripeness. When applied to intelligent harvesting robots in the future, this approach can improve the quality of tomato harvesting by reducing the collection of immature and spoiled fruits.

Alterations of pathogen transmission patterns and attenuated immune stimulation might be the cause of increased adult respiratory infections cases in 2023, results from a multi-center study in mainland China.

Background: Several respiratory infections outbreaks have been observed in mainland China after reduction of non-pharmaceutical interventions. Other countries have seen increases in respiratory infections outside typical seasons post-COVID-19, warranting investigation into underlying causes. Methods: We established monitoring networks for suspected respiratory infection in 14 tertiary hospitals nationwide. PCR for SARS-CoV-2, influenza A and B were performed on 3708 respiratory specimens and deep sequencing were conducted to identify co-infections or newly emerging microbes in 2023. Viral evolutionary analysis was completed. We retrospectively detected serum antibody level for various respiratory pathogens from 4324 adults without respiratory infections over 7 years to observe its dynamic curves. Findings: SARS-CoV-2 and influenza A were the main pathogens during outbreaks in 2023, bacterial-virus and bacterial-bacterial co-infections were most detected, but community co-infections didn't significantly increase pneumonia incidence. Different SARS-CoV-2 and influenza variants were present in different outbreaks, and no novel pathogens were found. The epidemiological patterns of influenza A, COVID-19 and etc. were altered, exhibiting characteristics of being "staggered" compared to most global regions, and potentially led to "overlapping prevalence". Binding antibody testing showed regular fluctuation, without significant decrease against common respiratory pathogens in adults. Influenza A antibody stimulation was attenuated during the 2023 outbreak. Conclusions: "Misaligned" alteration in seasonal respiratory disease patterns possibly caused combined epidemics, leading to cases spike in China, 2023. In adults, antibody levels didn't show significant decline, but reduced immune response to influenza during 2020-2023 emphasizes the need for consistent vaccination during pandemics.

Transfection of Babesia duncani: A Genetic Toolbox of this Pathogen to Advance Babesia Biology.

Human babesiosis is a tick-borne disease caused by Babesia pathogens. The disease, which presents with malaria-like symptoms, can be life-threatening, especially in individuals with weakened immune systems and the elderly. The worldwide prevalence of human babesiosis has been gradually rising, prompting alarm among public health experts. In other pathogens, genetic techniques have proven to be valuable tools for conducting functional studies to understand the importance of specific genes in development and pathogenesis as well as to validate novel cellular targets for drug discovery. Genetic manipulation methods have been established for several non-human Babesia and Theileria species and, more recently, have begun to be developed for human Babesia parasites. We have previously reported the development of a method for genetic manipulation of the human pathogen Babesia duncani. This method is based on positive selection using the hDHFR gene as a selectable marker, whose expression is regulated by the ef-1aB promoter, along with homology regions that facilitate integration into the gene of interest through homologous recombination. Herein, we provide a detailed description of the steps needed to implement this strategy in B. duncani to study gene function. It is anticipated that the implementation of this method will significantly improve our understanding of babesiosis and facilitate the development of novel and more effective therapeutic strategies for the treatment of human babesiosis. Key features This protocol provides an effective means of transfection of B. duncani, enabling genetic manipulation and editing to gain further insights into its biology and pathogenesis. The protocol outlined here for the electroporation of B. duncani represents an advancement over previous methods used for B. bovis [1]. Improvements include higher volume of culture used during the electroporation step and an enhancement in the number of electroporation pulses. These modifications likely enhance the efficiency of gene editing in B. duncani, allowing for quicker and more effective selection of transgenic parasites.

Identification and characterization of a new species of Taxus - Taxus qinlingensis by multiple taxonomic methods.

BACKGROUND: The taxonomy of Taxus Linn. remains controversial due to its continuous phenotypic variation and unstable topology, thus adversely affecting the formulation of scientific conservation strategies for this genus. Recently, a new ecotype, known as Qinling type, is mainly distributed in the Qinling Mountains and belongs to a monophyletic group. Here, we employed multiple methods including leaf phenotype comparison (leaf shapes and microstructure), DNA barcoding identification (ITS + trnL-trnF + rbcL), and niche analysis to ascertain the taxonomic status of the Qinling type. RESULTS: Multiple comparisons revealed significant differences in the morphological characters (length, width, and length/width ratio) among the Qinling type and other Taxus species. Leaf anatomical analysis indicated that only the Qinling type and T. cuspidata had no papilla under the midvein or tannins in the epicuticle. Phylogenetic analysis of Taxus indicated that the Qinling type belonged to a monophyletic group. Moreover, the Qinling type had formed a relatively independent niche, it was mainly distributed around the Qinling Mountains, Ta-pa Mountains, and Taihang Mountains, situated at an elevation below 1500 m. CONCLUSIONS: Four characters, namely leaf curvature, margin taper, papillation on midvein, and edges were put forward as primary indexes for distinguishing Taxus species. The ecotype Qingling type represented an independent evolutionary lineage and formed a unique ecological niche. Therefore, we suggested that the Qingling type should be treated as a novel species and named it Taxus qinlingensis Y. F. Wen & X. T. Wu, sp. nov.

MIECF: Multi-faceted information extraction and cross-mixture fusion for multimodal aspect-based sentiment analysis.

Aspect-level sentiment analysis within multimodal contexts, focusing on the precise identification and interpretation of sentiment attitudes linked to the target aspect across diverse data modalities, remains a focal research area that perpetuates the advancement of discourse and innovation in artificial intelligence. However, most existing methods tend to focus on extracting visual features from only one facet, such as face expression, which ignores the value of information from other key facets, such as the textual information presented by the image modality, resulting in information loss. To overcome the aforementioned constraint, we put forth a novel approach designated as Multi-faceted Information Extraction and Cross-mixture Fusion (MIECF) for Multimodal Aspect-based Sentiment Analysis. Our approach captures more comprehensive visual information in the image and integrates these local and global key features from multiple facets. Local features, such as facial expressions and textual features, provide direct and rich emotional cues. By contrast, the global feature often reflects the overall emotional atmosphere and context. To enhance the visual representation, we designed a Cross-mixture Fusion method to integrate this local and global multimodal information. In particular, the method establishes semantic relationships between local and global features to eliminate ambiguity brought by single-facet information and achieve more accurate contextual understanding, providing a richer and more precise manner for sentiment analysis. The experimental findings indicate that our proposed approach achieves a leading level of performance, resulting in an Accuracy of 79.65 % on the Twitter-2015 dataset, and Macro-F1 scores of 75.90 % and 73.11 % for the Twitter-2015 and Twitter-2017 datasets, respectively.

Isolation and identification of hoylesella marshii causing pleural infection: a case report.

BACKGROUND: Hoylesella marshii can be isolated from human oral cavities affected by dental pulp and periodontal infections, as well as from the dental plaque of healthy individuals, making it a common bacterium within the oral microbiota. However, its role in causing pleural infections in humans is rare. CASE PRESENTATION: A case of purulent pleural effusion occurred shortly after discharge in an elderly patient who had undergone surgery for gastric cancer. The infection was identified as being caused by an obligate anaerobe through laboratory culture, and was further identified as Hoylesella marshii causing pleural infection through 16 S rRNA gene sequence analysis. Susceptibility testing guided precise treatment with cefoperazone-sulbactam and metronidazole. The patient's clinical symptoms improved rapidly, laboratory test indicators gradually returned to normal, and the patient ultimately recovered. CONCLUSION: Hoylesella marshii can cause pleural infections in humans. Clinical microbiology laboratories should pay special attention to the cultivation of obligate anaerobes when routine aerobic cultures do not show bacterial growth but bacteria are visible on smear staining, and when conventional identification methods fail to identify the bacterium, analysis based on the highly conserved 16 S rRNA gene sequence can accurately and specifically identify the bacterium, guiding clinicians in formulating precise anti-infection strategies.

Spectral optimization using fast kV switching and filtration for photon counting CT with realistic detector responses: a simulation study.

Purpose: Photon counting CT (PCCT) provides spectral measurements for material decomposition. However, the image noise (at a fixed dose) depends on the source spectrum. Our study investigates the potential benefits from spectral optimization using fast kV switching and filtration to reduce noise in material decomposition. Approach: The effect of the input spectra on noise performance in both two-basis material decomposition and three-basis material decomposition was compared using Cramer-Rao lower bound analysis in the projection domain and in a digital phantom study in the image domain. The fluences of different spectra were normalized using the CT dose index to maintain constant dose levels. Four detector response models based on Si or CdTe were included in the analysis. Results: For single kV scans, kV selection can be optimized based on the imaging task and object size. Furthermore, our results suggest that noise in material decomposition can be substantially reduced with fast kV switching. For two-material decomposition, fast kV switching reduces the standard deviation (SD) by ∼ 10 % . For three-material decomposition, greater noise reduction in material images was found with fast kV switching (26.2% for calcium and 25.8% for iodine, in terms of SD), which suggests that challenging tasks benefit more from the richer spectral information provided by fast kV switching. Conclusions: The performance of PCCT in material decomposition can be improved by optimizing source spectrum settings. Task-specific tube voltages can be selected for single kV scans. Also, our results demonstrate that utilizing fast kV switching can substantially reduce the noise in material decomposition for both two- and three-material decompositions, and a fixed Gd filter can further enhance such improvements for two-material decomposition.

An improved deep learning network for image detection and its application in Dendrobii caulis decoction piece.

In recent years, with the increasing demand for high-quality Dendrobii caulis decoction piece, the identification of D. caulis decoction piece species has become an urgent issue. However, the current methods are primarily designed for professional quality control and supervision. Therefore, ordinary consumers should not rely on these methods to assess the quality of products when making purchases. This research proposes a deep learning network called improved YOLOv5 for detecting different types of D. caulis decoction piece from images. In the main architecture of improved YOLOv5, we have designed the C2S module to replace the C3 module in YOLOv5, thereby enhancing the network's feature extraction capability for dense and small targets. Additionally, we have introduced the Reparameterized Generalized Feature Pyramid Network (RepGFPN) module and Optimal Transport Assignment (OTA) operator to more effectively integrate the high-dimensional and low-dimensional features of the network. Furthermore, a new large-scale dataset of Dendrobium images has been established. Compared to other models with similar computational complexity, improved YOLOv5 achieves the highest detection accuracy, with an average mAP@.05 of 96.5%. It is computationally equivalent to YOLOv5 but surpasses YOLOv5 by 2 percentage points in terms of accuracy.

MT1G induces lipid droplet accumulation through modulation of H3K14 trimethylation accelerating clear cell renal cell carcinoma progression.

BACKGROUND: Lipid droplet formation is a prominent histological feature in clear cell renal cell carcinoma (ccRCC), but the significance and mechanisms underlying lipid droplet accumulation remain unclear. METHODS: Expression and clinical significance of MT1G in ccRCC were analyzed by using TCGA data, GEO data and scRNASeq data. MT1G overexpression or knockdown ccRCC cell lines were constructed and in situ ccRCC model, lung metastasis assay, metabolomics and lipid droplets staining were performed to explore the role of MT1G on lipid droplet accumulation in ccRCC. RESULTS: Initially, we observed low MT1G expression in ccRCC tissues, whereas high MT1G expression correlated with advanced disease stage and poorer prognosis. Elevated MT1G expression promoted ccRCC growth and metastasis both in vitro and in vivo. Mechanistically, MT1G significantly suppressed acylcarnitine levels and downstream tricarboxylic acid (TCA) cycle activity, resulting in increased fatty acid and lipid accumulation without affecting cholesterol metabolism. Notably, MT1G inhibited H3K14 trimethylation (H3K14me3) modification. Under these conditions, MT1G-mediated H3K14me3 was recruited to the CPT1B promoter through direct interaction with specific promoter regions, leading to reduced CPT1B transcription and translation. CONCLUSIONS: Our study unveils a novel mechanism of lipid droplet accumulation in ccRCC, where MT1G inhibits CPT1B expression through modulation of H3K14 trimethylation, consequently enhancing lipid droplet accumulation and promoting ccRCC progression. Graphical abstract figure Schematic diagram illustrating MT1G/H3K14me3/CPT1B-mediated lipid droplet accumulation promoted ccRCC progression via FAO inhibition.

Causal relationship between anti-inflammatory drugs and cancer: a pan-cancer study with Mendelian randomization.

Background: Numerous epidemiological studies have elucidated the intricate connection between inflammation and cancer, highlighting how sustained inflammatory responses can fuel carcinogenesis by fostering proliferation, angiogenesis, and metastasis, while dampening immune responses and sensitivity to chemotherapy. Previous clinical investigations have underscored the potential of anti-inflammatory medications in either preventing or mitigating tumor formation. Here, the causal relationship between anti-inflammatory drugs and cancer was further explored through Mendelian randomization studies. Methods: Employing Mendelian randomization, we scrutinized the causal links between three anti-inflammatory drugs-NSAIDs, Aspirin, and Anilide-and 37 types of cancer. We primarily utilized inverse variance weighting (IVW) as the primary analytical approach to delineate the causal association between these drugs and cancer types. Concurrently, sensitivity analyses were conducted to ascertain the absence of horizontal pleiotropy and heterogeneity. Results: Our investigation revealed a discernible causal relationship between certain anti-inflammatory drugs and a subset of cancers, albeit without a pervasive impact across all cancer types. Specifically, NSAIDs exhibited a risk-reducing effect on non-small cell lung cancer (OR: 0.76, 95% CI: 0.59-0.97, p-value: 0.03) and gastric cancer (OR: 0.57, 95% CI: 0.34-0.98, p-value: 0.04). Conversely, aspirin was associated with an increased risk of oral malignant tumors (OR: 2.18, 95% CI: 1.13-4.21, p-value: 0.02). Notably, no statistically significant findings were observed for anilide drugs (p < 0.05). Conclusion: We identified several cancers with potential causal links to NSAIDs, including non-small cell lung cancer and gastric cancer. Despite our extensive analysis, we did not identify a substantial causal relationship between the use of anti-inflammatory drugs and the development of various cancers.

A haplotype-resolved genome assembly of Malus domestica 'Red Fuji'.

The 'Red Fuji' apple (Malus domestica), is one of the most important and popular economic crops worldwide in the fruit industry. Using PacBio HiFi long reads and Hi-C reads, we assembled a high-quality haplotype-resolved genome of 'Red Fuji', with sizes of 668.7 and 668.8 Mb, and N50 sizes of 34.1 and 31.4 Mb. About 97.2% of sequences were anchored in 34 chromosomes. We annotated both haploid genomes, identifying a total of 95,439 protein-coding genes in the two haplotype genomes, with 98% functional annotation. The haplotype-resolved genome of 'Red Fuji' apple stands as a precise benchmark for an array of analyses, such as comparative genomics, transcriptomics, and allelic expression studies. This comprehensive resource is paramount in unraveling variations in allelic expression, advancing quality improvements, and refining breeding efforts.

Subclone from CT26 resistant to anti-PD-1 therapy associated with increased expression of genes related to glucocorticoids.

BACKGROUND: Although the use of anti-PD-1 antibodies has fundamentally changed traditional cancer treatment, most patients are resistant to anti-PD-1 treatment. Glucocorticoids (GCs) play an important role in tumorigenesis and tumor progression, but the role of endogenous GCs in resistance to anti-PD-1 antibody therapy remains unclear. METHODS: Single cell-derived cell lines (SCDCLs) were generated from a colorectal cancer cell line (CT26) using limiting dilution. We analyzed tumor tissues from anti-PD-1 antibody-treated and untreated mice inoculated with SCDCLs via transcriptome sequencing and flow cytometry to detect pathway activity and immune cell composition changes in the tumor microenvironment. RESULTS: Five SCDCLs were inoculated into wild-type BALB/c mice (all tumorigenic). Single-cell clone (SCC)-2 exhibited the slowest growth rates both in vivo and in vitro compared to other single-cell clones, and better long-term survival than SCC1 and CT26. Flow cytometry showed that SCC2 tumor-bearing mice exhibited significantly higher infiltration of T cells within the tumor tissue, and higher expression of PD-1 on these T cells than the other groups in vivo. However, the SCC2 group showed no response to anti-PD-1 therapy. Transcriptome analysis revealed that the SCC2 group exhibited increased expression of genes related to GC (Hsd11b1, Sgk3, Tgfbr2, and Il7r) compared to SCC2-anti-PD-1 treated tumors. CONCLUSIONS: GC pathway activation is related to resistance to anti-PD-1 therapy.

Can carbon emissions trading policy promote product bargaining power increases for high-carbon enterprises? Evidence from China.

Carbon emissions trading policies play a crucial role in facilitating the transition to high-end products within high-carbon enterprises. Nevertheless, current empirical analyses of the carbon emissions trading market exhibit a lack of precision and are susceptible to bias in their findings. Limited research has been conducted on the influence of product quality as a potential constraint on the impact of carbon trading on product bargaining power. This study presents a double-difference model utilizing data on emission-control enterprises in China's carbon market to examine the influence of the carbon emissions trading mechanism on the bargaining power of high-carbon products. Empirical analysis is conducted using financial data from listed companies in China spanning the years 2010 to 2020. The findings indicate that the implementation of carbon emissions trading policies has a dampening impact on the product bargaining power of high-carbon enterprises. Moreover, carbon emissions trading policies have heterogeneous effects on the product bargaining power of high-carbon firms with different life cycles, with mature high-carbon firms receiving a boost and declining high-carbon firms receiving a dampening effect. Mechanism test finds that the incomplete transmission effect of cost shocks resulting from carbon emissions trading policies has negatively affect the product bargaining power of high-carbon enterprises. Further research finds that product quality is a key factor in determining the effect of the carbon emissions trading policy, and that the impact of the carbon emissions trading policy on the bargaining power of products of high-carbon firms takes on a "U" shape due to product quality. Once the product quality exceeds the bottleneck value of 0.5956, the policy significantly increases the bargaining power of products. The study confirms that the establishment of carbon markets can effectively increase the bargaining power of superior products. These results offer a comprehensive theoretical and practical foundation for nations to advance the development of carbon markets and facilitate the achievement of sustainable development by high-carbon enterprises.

HortGenome Search Engine, a universal genomic search engine for horticultural crops.

Horticultural crops comprising fruit, vegetable, ornamental, beverage, medicinal and aromatic plants play essential roles in food security and human health, as well as landscaping. With the advances of sequencing technologies, genomes for hundreds of horticultural crops have been deciphered in recent years, providing a basis for understanding gene functions and regulatory networks and for the improvement of horticultural crops. However, these valuable genomic data are scattered in warehouses with various complex searching and displaying strategies, which increases learning and usage costs and makes comparative and functional genomic analyses across different horticultural crops very challenging. To this end, we have developed a lightweight universal search engine, HortGenome Search Engine (HSE; http://hort.moilab.net), which allows for the querying of genes, functional annotations, protein domains, homologs, and other gene-related functional information of more than 500 horticultural crops. In addition, four commonly used tools, including 'BLAST', 'Batch Query', 'Enrichment analysis', and 'Synteny Viewer' have been developed for efficient mining and analysis of these genomic data.