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Abstract Background Duck circovirus (DuCV) infections commonly induce immunosuppression and secondary infections in ducks, resulting in significant economic losses in the duck breeding industry. Currently, effective vaccines and treatments for DuCV have been lacking. Therefore, rapid, specific, and sensitive detection methods are crucial for preventing and controlling DuCV. Methods A lateral flow strip (LFS) detection method was developed using recombinase polymerase amplification (RPA) and clustered regularly interspaced short palindromic repeats (CRISPR)/CRISPR-associated protein 12a (Cas12a). The RPA-CRISPR/Cas12a-LFS targeted the DuCV replication protein (Rep) and was operated at 37 ℃ and allowed for visual interpretation without requiring sophisticated equipment. Results The results revealed that the reaction time of RPA-CRISPR/Cas12a-LFS is only 45 min. This method achieved a low detection limit of 2.6 gene copies. Importantly, this method demonstrated high specificity and no cross-reactivity with six other avian viruses. In a study involving 97 waterfowl samples, the Rep RPA-CRISPR/Cas12a-LFS showed 100% consistency and agreement with real-time quantitative polymerase chain reaction. Conclusion These findings underscored the potential of this user-friendly, rapid, sensitive, and accurate detection method for on-site DuCV detection.

Abstract Background Prompt identification of fracture healing is crucial for medical decision-making and clinical research. This study aims to assess the effectiveness of the Radiographic Union Score for Hip (RUSH) in subtrochanteric fractures and determine the optimal scoring threshold for fracture healing. Methods We obtained 94 sets of X-ray films from the postoperative follow-up of 35 patients who underwent intramedullary nail fixation for subtrochanteric fractures. Six orthopedic trauma surgeons evaluated the imaging data and determined fracture healing based on subjective judgment. They then scored the X-ray images using the RUSH form. After four weeks, the same observers re-evaluated 47 randomly selected sets of radiographs. Subsequently, the observers and study designer conducted a consensus meeting where they agreed on whether the fractures had healed. We used Fleiss kappa to evaluate agreement among observers based on subjective impressions, and the intraclass correlation coefficient assessed RUSH score consistency. Regression analysis examined the relationship between scores and fracture healing consensus using a receiver operator characteristic (ROC) curve. Results Based on overall impressions, the agreement level among orthopedic trauma surgeons for determining the healing status of subtrochanteric fractures was moderate (Kappa = 0.564, 95% CI: 0.511–0.616). However, utilizing the RUSH scoring system can improve the consistency to a substantial level of agreement (ICC = 0.748, 95% CI: 0.663–0.817). Regarding intraobserver reliability, there were significant differences among orthopedic trauma surgeons in judging fracture healing based on overall impressions. However, using the RUSH scoring form, the attending group achieved better intraobserver consistency than the resident group. Regression analysis and ROC curve analysis revealed a strong correlation between the RUSH total score, cortical bone score, cancellous bone score, and consensus on fracture healing. The AUC values are 0.769 (95% CI: 0.670–0.868), 0.779 (95% CI: 0.681–0.876), and 0.771 (95% CI: 0.674–0.867), respectively. Conclusions Our study highlights the effectiveness of the RUSH scoring system in enhancing interobserver agreement and intraobserver consistency when evaluating subtrochanteric fracture healing in orthopedic trauma surgeons. We propose setting 21 points as the threshold for the simplified RUSH scoring system to determine fracture healing. This system includes only eight items related to cortical bone, with a total score of 24 points.

Abstract We present SpliceTransformer (SpTransformer), a deep-learning framework that predicts tissue-specific RNA splicing alterations linked to human diseases based on genomic sequence. SpTransformer outperforms all previous methods on splicing prediction. Application to approximately 1.3 million genetic variants in the ClinVar database reveals that splicing alterations account for 60% of intronic and synonymous pathogenic mutations, and occur at different frequencies across tissue types. Importantly, tissue-specific splicing alterations match their clinical manifestations independent of gene expression variation. We validate the enrichment in three brain disease datasets involving over 164,000 individuals. Additionally, we identify single nucleotide variations that cause brain-specific splicing alterations, and find disease-associated genes harboring these single nucleotide variations with distinct expression patterns involved in diverse biological processes. Finally, SpTransformer analysis of whole exon sequencing data from blood samples of patients with diabetic nephropathy predicts kidney-specific RNA splicing alterations with 83% accuracy, demonstrating the potential to infer disease-causing tissue-specific splicing events. SpTransformer provides a powerful tool to guide biological and clinical interpretations of human diseases.

ObjectiveThis retrospective study aimed to explore the association and clinical value of sequential organ failure assessment (SOFA) score on the predictors of adverse events in patients with unruptured abdominal aortic aneurysms (AAA).MethodsA total of 322 patients from Medical Information Mart for Intensive Care IV database were enrolled. Logistic regression was conducted to explore the association between SOFA and primary outcome (need for surgery, NFS). Receiver operating characteristic (ROC) and nomogram analyses were used to assess its performance for predicting NFS. We also explored the association and clinical value of SOFA on secondary outcomes including hospital length of stay (LOS), ICU-LOS, and in-hospital mortality by linear and logistic regression analyses, generalized additive model, ROC, and decision curve analysis.ResultsTotally 291 patients underwent the surgery. High SOFA score significantly correlated with NFS both in crude and adjusted models (all PConclusionsSOFA score was related to the NFS and can be regarded as a useful indicator for predicting the NFS in patients with AAA.

Abstract Two‐dimensional Ruddlesden‐Popper (2D RP) layered metal‐halide perovskites have garnered increasing attention due to their favorable optoelectronic properties and enhanced stability in comparison to their three‐dimensional counterparts. Nevertheless, precise control over the crystal orientation of 2D RP perovskite films remains challenging, primarily due to the intricacies associated with the solvent evaporation process. In this study, we introduce a novel approach known as reverse‐cool annealing (RCA) for the fabrication of 2D RP perovskite films. This method involves a sequential annealing process at high and low temperatures for wet perovskite films. The resulting RCA‐based perovskite films show the smallest root‐mean‐square value of 23.1 nm, indicating a minimal surface roughness and a notably compact and smooth surface morphology. The low defect density in these 2D RP perovskite films with exceptional crystallinity suppresses non‐radiative recombination, leading to a minimal non‐radiative open‐circuit voltage loss of 149 mV. Moreover, the average charge lifetime in these films is extended to 56.3 ns, thanks to their preferential growth along the out‐of‐plane direction. Consequently, the leading 2D RP perovskite solar cell achieves an impressive power conversion efficiency of 17.8% and an open‐circuit voltage of 1.21 V. Additionally, the stability of the 2D RP perovskite solar cell, even without encapsulation, exhibits substantial improvement, retaining 97.4% of its initial efficiency after 1000 hours under a nitrogen environment. The RCA strategy presents a promising avenue for advancing the commercial prospects of 2D RP perovskite solar cells.

Ammonia-N stress is a significant environmental factor that adversely affects the health and productivity of aquaculture species. This study investigates the effects of ammonia-N stress on the shrimp Penaeus monodon through a combination of biochemical, histological, transcriptomic, and metabolomic analyses. Shrimp were exposed to ammonia-N stress for 12 and 96 hours, and key markers of oxidative stress, nitrogen metabolism, immune response, and overall health were assessed. The results showed that prolonged ammonia-N exposure causes significant hepatopancreatic damage, including atrophy and deformation. Transcriptomic analysis revealed significant changes in gene expression related to apoptosis, immune response, and key metabolic pathways, with particular emphasis on the disruption of innate immune signaling and defense mechanisms. Metabolomic analysis identified disruptions in nucleotide turnover, antioxidant defenses, and fundamental metabolic processes. These findings suggest that ammonia-N stress induces a multifaceted stress response in shrimp, involving oxidative stress, immune activation, and metabolic disturbances. Understanding these immune-related and metabolic mechanisms provides valuable insights into the molecular responses of crustaceans to environmental stress, laying the foundation for assessing the ecological risk of ammonia-N and identifying potential immunological biomarkers for monitoring and mitigating its adverse effects in aquaculture systems.

Due to the substantial production and use of antibiotics, they inevitably remain in aquatic environments, posing a serious threat to aquatic ecosystems. However, there are currently no criteria of antibiotics for ecological risk in the water environment. In the present study, three types of antibiotics (tetracyclines, sulfonamides and quinolones) that are often detected in water environments were investigated. Toxicity data regarding bacteria, algae, plants, invertebrates and vertebrates were selected, and the species sensitivity distribution was used to obtain the ecological risk criteria of antibiotics to aquatic organisms. Animals are the least sensitive to antibiotics. The overall toxicity of antibiotics is most sensitive to bacteria and cyanobacteria, followed by green algae and plants. The recommended ecological criteria for tetracyclines, quinolones, and sulfonamides are 22, 17, and 94 μg/L, respectively. Ofloxacin needs to be used with caution because it has a small acute predicted no-effect concentration (PNEC) of 0.6 μg/L. The ecological risk criterion for chronic toxicity of total antibiotics was determined to be 1.4 μg/L. The PNECs measured for the quinolone, tetracycline, and sulfonamide antibiotics were 0.5, 2.2, and 2.4 μg/L, respectively. Norfloxacin had the highest chronic toxicity zone of 353, indicating that chronic poisoning is most likely to occur. Moreover, there was an exponential correlation between acute PNEC and chronic PNEC. In addition, a quantitative structure–activity relationship model was constructed for acute ecological risk criteria of antibiotics to aquatic organisms. These findings can expand the ecological risk threshold data on the effects of antibiotics on aquatic organisms, and provide a theoretical basis for the environmental risk assessment of antibiotics.

Abstract Peroxisomal l-bifunctional enzyme (EHHADH) plays a role in the classic peroxisomal fatty acid β-oxidation pathway; however, the relationship between EHHADH expression and diabetic kidney disease has not been well understood. Here, we found that endogenous EHHADH levels were strongly correlated with the progression and severity of diabetic nephropathy in T2D patients. EHHADH knockout mice exhibited worsened renal tubular injury in diabetic mice. Furthermore, EHHADH is a modulator of pexophagy. In renal tubular epithelial cells (RTECs) in vitro, the knockdown of EHHADH induced a dramatic loss of peroxisomes. The loss of peroxisomes in EHHADH-deficient RTECs was restored by either an autophagic inhibitor 3-methyladenine or bafilomycin A1 both in vitro and in vivo. NBR1 was required for pexophagy in EHHADH-knockdown cells, where the level of reactive oxygen species (ROS) was increased, while inhibition of ROS blocked pexophagy. In summary, our findings revealed EHHADH deficiency accelerated renal injury in DKD as a modulator of pexophagy.

This study aimed to investigate the effect of ultrasound treatment times (30 min and 60 min) and levels of quinoa protein (QPE) addition (1 % and 2 %) on the quality of Chinese style reduced-salt pork meatballs, commonly known as lion’s head. The water-holding capacity (WHC), gel and rheology characteristics, and protein conformation were assessed. The results indicated that extending the ultrasound treatment time and elevating the quinoa protein content caused conspicuous improvements (P

Abstract Many experiments have been performed to study the heating properties of concrete under microwave irradiation. Microwave provides the non-uniform heating process, which cannot be reflected clearly through the experimental investigations. In this paper, a theoretical method is presented to investigate the electromagnetic-thermal coupling process of double-layer cylindrical concrete under microwave heating. The wave transmission and reflection were considered. An analytic solution is presented to predict transient heating process within a 3-dimensional double-layer concrete model induced by microwave heating. The inner aggregate is a microwave high loss material and the outer mortar was microwave low loss medium. Poynting theorem was employed to calculate the electric field distribution and microwave energy loss within concrete. Transient heat transfer process with an internal microwave heat source was investigated based on the classical heat transfer theory by employing integral transform technique. The results indicate that microwave heating effect depend on the concrete size, dielectric properties as well as microwave energy input. The temperature gradient was formed at the mortar-aggregate interface, which varied with the microwave heating parameters inputs. The analytical study will provide significant insight to promote the understanding of electric and temperature field in the two-layer composite concrete materials under microwave heating.

Abstract Background Gastric cancer (GC) lacks serum biomarkers with clinical diagnostic value. Multi-omics analysis is an important approach to discovering cancer biomarkers. This study aimed to identify and validate serum biomarkers for GC diagnosis by cross-analysis of proteomics and transcriptomics datasets. Methods A cross-omics analysis was performed to identify overlapping differentially expressed genes (DEGs) between our previous aptamer-based GC serum proteomics dataset and the GC tissue RNA-Seq dataset in The Cancer Genome Atlas (TCGA) database, followed by lasso regression and random forest analysis to select key overlapping DEGs as candidate biomarkers for GC. The mRNA levels and diagnostic performance of these candidate biomarkers were analyzed in the original and independent GC datasets to select valuable candidate biomarkers. The valuable candidate biomarkers were subjected to bioinformatics analysis to select those closely associated with the biological behaviors of GC as potential biomarkers. The clinical diagnostic value of the potential biomarkers was validated using serum samples, and their expression levels and functions in GC cells were validated using in vitro cell experiments. Results Four candidate biomarkers (ILF2, PGM2L1, CHD7, and JCHAIN) were selected. Their mRNA levels differed significantly between tumor and normal tissues and showed different diagnostic performances for GC, with areas under the receiver operating characteristic curve (AUROCs) of 0.629–0.950 in the TCGA dataset and 0.736–0.840 in the Gene Expression Omnibus (GEO) dataset. In the bioinformatics analysis, only ILF2 (interleukin enhancer-binding factor 2) gene levels were associated with immune cell infiltration, some checkpoint gene expression, chemotherapy sensitivity, and immunotherapy response. Serum levels of ILF2 were higher in GC patients than in controls, with an AUROC of 0.944 for the diagnosis of GC, and it was also detected in the supernatants of GC cells. Knockdown of ILF2 by siRNA significantly reduced the proliferation and colony formation of GC cells. Overexpression of ILF2 significantly promotes the proliferation and colony formation of gastric cancer cells. Conclusions Trans-omics analysis of proteomics and transcriptomics is an efficient approach for discovering serum biomarkers, and ILF2 is a potential diagnostic biomarker and therapeutic target of gastric cancer.

Fenneropenaeus merguiensis is one of the largest species of penaeid shrimp. It has a wide distribution of germplasm resources in the South China Sea and the southeastern coastal regions of China, yet its germplasm characteristics remain insufficiently understood. Therefore, we conducted analyses of basic nutritional components, amino acids, fatty acids, antioxidant indices, and genetic diversity in three F. merguiensis populations (FmRP, FmSZ, FmSY). The results showed a significant difference in ash content, with FmSZ having the highest ash content (1.77 g/100 g) (p < 0.05). A total of 17 amino acids were detected, and FmSZ exhibited higher concentrations of most amino acids, especially essential amino acids (p < 0.05). The analysis of fatty acid composition revealed significant differences between the populations, with FmSY potentially having a nutritional advantage overall (p < 0.05). In terms of antioxidant capacity and genetic diversity, there were no significant differences in total antioxidant capacity (T-AOC), catalase (CAT), or superoxide dismutase (SOD) among the populations (p > 0.05). However, genetic analysis indicated that FmSZ had the lowest inbreeding coefficient and relatively higher genetic diversity. In conclusion, among the three F. merguiensis populations, FmSZ has relatively higher nutritional quality and genetic diversity, and F. merguiensis is a high-quality aquatic product worth promoting. The analysis and characterization of various aspects of F. merguiensis from the three locations provide important information and data support for germplasm resources and genetic breeding efforts.

Salinity is a common abiotic stress in the culture of penaeid shrimp. Through post-transcriptional regulation of gene transcripts, microRNAs (miRNAs) play an important role in the adaptation to a stressful environment. However, the involvement of miRNAs in the salinity stress response of shrimp remains unclear. In the present study, the sequence and expression profile of miRNAs in the hepatopancreas of low-salinity-treated Penaeus monodon were obtained by the high-throughput sequencing technique. A total of 679 miRNAs were identified, including 167 miRNAs that were significantly differentially expressed after low-salinity exposure (p < 0.05). Remarkably, most of these miRNAs were downregulated, suggesting that a series of genes were activated to participate in stress response. In addition, 43 miRNAs differentially expressed at all treatment were selected as putative key modulators. Enrichment analysis of genes targeted by these miRNAs indicated that a network that consists of the nervous system, the immune system, and the endocrine system played a crucial role in maintaining the homeostasis of P. monodon under low-salinity stress. These findings may help contribute to a better understanding of the mechanism that regulates salinity tolerance in shrimp and provide valuable genetic information for subsequent studies.

Abstract Objective This study aims to compare the feasibility and effectiveness of automatic deep learning network and radiomics models in differentiating low tumor stroma ratio (TSR) from high TSR in pancreatic ductal adenocarcinoma (PDAC). Methods A retrospective analysis was conducted on a total of 207 PDAC patients from three centers (training cohort: n = 160; test cohort: n = 47). TSR was assessed on hematoxylin and eosin-stained specimens by experienced pathologists and divided as low TSR and high TSR. Deep learning and radiomics models were developed including ShuffulNetV2, Xception, MobileNetV3, ResNet18, support vector machine (SVM), k-nearest neighbor (KNN), random forest (RF), and logistic regression (LR). Additionally, the clinical models were constructed through univariate and multivariate logistic regression. Kaplan–Meier survival analysis and log-rank tests were conducted to compare the overall survival time between different TSR groups. Results To differentiate low TSR from high TSR, the deep learning models based on ShuffulNetV2, Xception, MobileNetV3, and ResNet18 achieved AUCs of 0.846, 0.924, 0.930, and 0.941, respectively, outperforming the radiomics models based on SVM, KNN, RF, and LR with AUCs of 0.739, 0.717, 0.763, and 0.756, respectively. Resnet 18 achieved the best predictive performance. The clinical model based on T stage alone performed worse than deep learning models and radiomics models. The survival analysis based on 142 of the 207 patients demonstrated that patients with low TSR had longer overall survival. Conclusions Deep learning models demonstrate feasibility and superiority over radiomics in differentiating TSR in PDAC. The tumor stroma ratio in the PDAC microenvironment plays a significant role in determining prognosis. Critical relevance statement The objective was to compare the feasibility and effectiveness of automatic deep learning networks and radiomics models in identifying the tumor-stroma ratio in pancreatic ductal adenocarcinoma. Our findings demonstrate deep learning models exhibited superior performance compared to traditional radiomics models. Key points • Deep learning demonstrates better performance than radiomics in differentiating tumor-stroma ratio in pancreatic ductal adenocarcinoma. • The tumor-stroma ratio in the pancreatic ductal adenocarcinoma microenvironment plays a protective role in prognosis. • Preoperative prediction of tumor-stroma ratio contributes to clinical decision-making and guiding precise medicine. Graphical Abstract

With the rapid development of AIGC large models and intelligent computing applications, deterministic networking technology for wide-area RDMA has become a recent research hotspot.The development path, implementation solutions and technical challenges of RDMA-WAN were analyzed, by setting up a laboratory and an outfield testing environment, with a series of testing and verifications.The deterministic demand characteristics and boundary performance indicators of RDMA-WAN were proposed.Finally, the deterministic networking technologies for RDMA-WAN were summarized and prospected.

Feline panleukopenia virus (FPV), a globally pervasive and highly pathogenic pathogen, has garnered significant attention recently due to the cross-species transmission of its variants. Despite the vast body of research conducted on FPV, studies exploring its evolutionary history, dynamics, and the factors driving its evolution remain scarce. The pathogenicity of strains with the prevalent mutations (A91S and I101T) in the VP2 protein has also not been fully elucidated. This study conducted a comparative analysis of FPV VP2 sequences sourced from Xinjiang province in China, other provinces in China, and other countries. It was confirmed that the evolutionary rate of FPV approached that of RNA viruses, at approximately 1.13 × 10−4 substitutions/site/year. The study reconstructed molecular models of the VP2 protein with the A91S and I101T mutations and used viral strains carrying these mutations to perform the animal regression experiment. It was confirmed that isolates with the A91S and I101T mutations could cause typical leukopenia and acute enteritis symptoms, suggesting that the mutant strains still possess certain pathogenicity. This is the first study to report on the evolutionary dynamics of FPV in Xinjiang, China, and it emphasized the importance of continuously monitoring FPV evolutionary dynamics.

Due to its high tolerance to salinity and temperature, as well as its strong adaptability, Metapenaeus ensis holds an important position in the Chinese aquaculture industry. However, studies on the evaluation of its germplasm resources remain insufficient. This research conducted an in-depth comparative evaluation of M. ensis from three representative farming regions in China: Sanya, Zhuhai, and Raoping. The nutritional analysis of muscle tissue showed no statistically significant differences in crude ash, moisture, and crude protein content among the populations (p > 0.05). However, significant differences were observed in crude fat and total sugar content (p < 0.05). The MeSY and MeRP populations had higher crude fat content than the MeZH population (p < 0.05), while the MeZH population exhibited the highest total sugar content. In terms of amino acid composition, the MeSY population had relatively higher total essential amino acid content and proportion, as well as higher total amino acid content, both of which were statistically significant (p < 0.05). A fatty acid composition analysis further highlighted the advantages of the MeRP population in several key fatty acids (p < 0.05). Physiological and biochemical analyses showed no significant differences among the three populations in total antioxidant capacity, superoxide dismutase activity, or catalase activity (p > 0.05). A genetic diversity analysis indicated that M. ensis has relatively low diversity, with the MeSY population showing higher SNP density and nucleotide diversity. A genetic differentiation analysis revealed significant genetic differentiation between the MeSY and MeZH populations, while differentiation between the MeZH and MeRP populations was relatively smaller. This comprehensive assessment of nutritional components, amino acids, fatty acids, antioxidant capacity, and genetic diversity highlights the advantages of germplasm resources from different regions. These findings provide valuable insights for future research on the genetic characteristics and breeding potential of M. ensis.

This study aimed to evaluate the quality and genetic diversity of farmed Litopenaeus vannamei across three distinct populations from Maoming City (MM), Zhanjiang City (ZJ), and Yangjiang City (YJ) in Guangdong Province. Muscle tissues from L. vannamei were analyzed for phenotypic traits, conventional nutrients, amino acids, and fatty acids, while genetic diversity was assessed using whole genome sequencing techniques. The analysis revealed that the crude protein content in shrimp across the three populations ranged from 20.87 to 21.95 g/100 g, crude fat content ranged from 0.90 to 1.50 g/100 g, essential amino acid content ranged from 5.55 to 5.86 g/100 g, total amino acid content ranged from 14.73 to 15.27 g/100 g, total fatty acid content ranged from 682.73 to 793.97 mg/100 g, total antioxidant capacity (T-AOC) ranged from 2.68 to 2.72 μmol/g, superoxide dismutase (SOD) activity ranged from 1021.97 to 1057.21 U/g, and catalase (CAT) activity ranged from 78.65 to 81.33 μmoL/min. No significant differences were observed in ash and crude fat levels among conventional nutrients, nor in the biochemical indexes T-AOC, CAT, and SOD. Genetic analysis showed that the single nucleotide polymorphism density (SNP/Kb) ranged from 15.323 to 17.461, nucleotide diversity (π) ranged from 2.98 × 10−5 to 15.84 × 10−5, polymorphism information content (PIC) ranged from 0.300 to 0.317, heterozygosity (Ho) ranged from 0.033 to 0.048, and inbreeding coefficients (FIS) ranged from 0.834 to 0.887. The genetic differentiation index (FST) values among the three populations ranged from 0.056 to 0.106. This study provides an evaluation of the germplasm resources and genetic diversity of farmed L. vannamei, offering insights for the efficient management and sustainable utilization of this species’ germplasm resources.

Salinity is a crucial environmental factor influencing the survival, growth, development, and reproduction of aquatic animals. However, the underlying molecular mechanisms of the shrimp’s response to salinity stress are not yet fully understood. Therefore, we used the Illumina NovaSeq 6000 platform to perform transcriptome sequencing of the hepatopancreas of Litopenaeus vannamei under high-salinity (30 PSU), medium-salinity (10 PSU), and low-salinity (0.5 PSU) conditions. We obtained 63.23 Gb of high-quality data and identified 3589 differentially expressed genes (DEGs), including 1638 upregulated and 1951 downregulated genes. Notably, a comparison between the control group (30 PSU) and the low-salinity group (0.5 PSU) revealed that the BBOX1 and CHE1 genes were significantly upregulated, while the ACOX1, MPV, CYP2L1, GCH, MVK, TREt1, and XDH genes were significantly downregulated. These genes are primarily involved in key metabolic pathways, such as fatty acid oxidation, cholesterol metabolism, and hormone synthesis and metabolism. The key genes involved in fatty acid β-oxidation, such as ACOX1, ACAD, HADH, HSD17B4, PECR, CROT, PIPOX, and CG5009, all showed a downward trend, suggesting that L. vannamei may respond to salt stress by regulating fatty acid oxidative metabolism, optimizing energy utilization, and maintaining cell homeostasis under low-salinity conditions. Functional annotation of gene ontology (GO) and KEGG pathway enrichment analysis highlighted the roles of these significant DEGs in the adaptation of L. vannamei to environments of varying salinity, underscoring the importance of metabolic pathways in their adaptive physiological responses. This study provides a crucial molecular biological basis for understanding the molecular mechanisms and physiological protection strategies of L. vannamei under salinity stress.

Penaeus monodon is one of the world’s most important aquaculture species, with its host-associated microbial community playing a crucial role in its growth, metabolism, immune response, and adaptation. In our study, we utilized Illumina high-throughput sequencing to investigate the composition, structure, and function of the intestinal microbial communities of P. monodon from two different regions in Guangdong. Our results identified 176 phyla across both populations, with Proteobacteria and Firmicutes being predominant. Furthermore, we identified 3095 genera, with Photobacterium, Vibrio, and Aliiroseovarius being the most dominant. Functional gene analysis based on KEGG data indicated that the carbohydrate metabolism and amino acid metabolism were significant at the secondary metabolic pathway level. The eggNOG functional annotation revealed that the genes involved in replication, recombination, and repair are of paramount importance. The CAZy annotation results indicated that Glycoside Hydrolases (GH) have the highest abundance. The Pfam annotation analysis showed that the two most prevalent domains are P-loop NTPase and NADP Rossmann. Our investigation provides a reference for species-level and functional-level analyses of the intestinal microbiota of P. monodon, contributing valuable insights into its microbial ecology.

Abstract Purpose This study aimed to construct a mortality model for the risk stratification of intensive care unit (ICU) patients with sepsis by applying a machine learning algorithm. Methods Adult patients who were diagnosed with sepsis during admission to ICU were extracted from MIMIC-III, MIMIC-IV, eICU, and Zigong databases. MIMIC-III was used for model development and internal validation. The other three databases were used for external validation. Our proposed model was developed based on the Extreme Gradient Boosting (XGBoost) algorithm. The generalizability, discrimination, and validation of our model were evaluated. The Shapley Additive Explanation values were used to interpret our model and analyze the contribution of individual features. Results A total of 16,741, 15,532, 22,617, and 1,198 sepsis patients were extracted from the MIMIC-III, MIMIC-IV, eICU, and Zigong databases, respectively. The proposed model had an area under the receiver operating characteristic curve (AUROC) of 0.84 in the internal validation, which outperformed all the traditional scoring systems. In the external validations, the AUROC was 0.87 in the MIMIC-IV database, better than all the traditional scoring systems; the AUROC was 0.83 in the eICU database, higher than the Simplified Acute Physiology Score III and Sequential Organ Failure Assessment (SOFA),equal to 0.83 of the Acute Physiology and Chronic Health Evaluation IV (APACHE-IV), and the AUROC was 0.68 in the Zigong database, higher than those from the systemic inflammatory response syndrome and SOFA. Furthermore, the proposed model showed the best discriminatory and calibrated capabilities and had the best net benefit in each validation. Conclusions The proposed algorithm based on XGBoost and SHAP-value feature selection had high performance in predicting the mortality of sepsis patients within 24 h of ICU admission.

Abstract Modern omics technologies can generate massive amounts of biomedical data, providing unprecedented opportunities for individualized precision medicine. However, traditional statistical methods cannot effectively process and utilize such big data. To meet this new challenge, machine learning algorithms have been developed and applied rapidly in recent years, which are capable of reducing dimensionality, extracting features, organizing data and forming automatable data-driven clinical decision systems. Data-driven clinical decision-making have promising applications in precision medicine and has been studied in digestive diseases, including early diagnosis and screening, molecular typing, staging and stratification of digestive malignancies, as well as precise diagnosis of Crohn's disease, auxiliary diagnosis of imaging and endoscopy, differential diagnosis of cystic lesions, etiology discrimination of acute abdominal pain, stratification of upper gastrointestinal bleeding (UGIB), and real-time diagnosis of esophageal motility function, showing good application prospects. Herein, we reviewed the recent progress of data-driven clinical decision making in precision diagnosis of digestive diseases and discussed the limitations of data-driven decision making after a brief introduction of methods for data-driven decision making.