Objective To explore the construction of α-1,3-galactosyltransferase (GGTA1) gene-knockout (GTKO) Diannan miniature pigs and the kidney xenotransplantation from pigs to rhesus macaques, and to assess the effectiveness of GTKO pigs. Methods The GTKO Diannan miniature pigs were constructed using the CRISPR/Cas9 gene-editing system and somatic cell cloning technology. The phenotype of GTKO pigs was verified through polymerase chain reaction, Sanger sequencing and immunofluorescence staining. Flow cytometry was used to detect antigen-antibody (IgM) binding and complement-dependent cytotoxicity. Kidney xenotransplantation was performed from GTKO pigs to rhesus macaques. The humoral immunity, cellular immunity, coagulation and physiological indicators of the recipient monkeys were monitored. The function and pathological changes of the transplanted kidneys were analyzed using ultrasonography, hematoxylin-eosin staining, immunohistochemical staining and immunofluorescence staining. Results Single-guide RNA (sgRNA) targeting exon 4 of the GGTA1 gene in Diannan miniature pigs was designed. The pGL3-GGTA1-sgRNA1-GFP vector was transfected into fetal fibroblasts of Diannan miniature pigs. After puromycin selection, two cell clones, C59# and C89#, were identified as GGTA1 gene-knockout clones. These clones were expanded to form cell lines, which were used as donor cells for somatic cell nuclear transfer. The reconstructed embryos were transferred into the oviducts of trihybrid surrogate sows, resulting in 13 fetal pigs. Among them, fetuses F04 and F11 exhibited biallelic mutations in the GGTA1 gene, and F04 had a normal karyotype. Using this GTKO fetal pig for recloning and transferring the reconstructed embryos into the oviducts of trihybrid surrogate sows, seven surviving piglets were obtained, all of which did not express α-Gal epitope. The binding of IgM from the serum of rhesus monkey 20# to GTKO pig PBMC was reduced, and the survival rate of GTKO pig PBMC in the complement-dependent cytotoxicity assay was higher than that of wild-type pig. GTKO pig kidneys were harvested and perfused until completely white. After the left kidney of the recipient monkey was removed, the pig kidney was heterotopically transplanted. Following vascular anastomosis and blood flow restoration, the pig kidney rapidly turned pink without hyperacute rejection (HAR). Urine appeared in the ureter 6 minutes later, indicating successful kidney transplantation. The right kidney of the recipient was then removed. Seven days after transplantation, the transplanted kidney had good blood flow, the recipient monkey's serum creatinine level was stable, and serum potassium and cystatin C levels were effectively controlled, although they increased 10 days after transplantation. Seven days after transplantation, the levels of white blood cells, lymphocytes, monocytes and eosinophils in the recipient monkey increased, while platelet count and fibrinogen levels decreased. The activated partial thromboplastin time, thrombin time and prothrombin time remained relatively stable but later showed an upward trend. The recipient monkey survived for 10 days. At autopsy, the transplanted kidney was found to be congested, swollen and necrotic, with a small amount of IgG deposition in the renal tissue, and a large amount of IgM, complement C3c and C4d deposition, as well as CD68⁺ macrophage infiltration. Conclusions The kidneys of GTKO Diannan miniature pigs may maintain normal renal function for a certain period in rhesus macaques and effectively overcome HAR, confirming the effectiveness of GTKO pigs for xenotransplantation.

ObjectiveTransfer RNA-derived fragments (tRFs) are a recently characterized and rapidly expanding class of small non-coding RNAs, typically ranging from 13 to 50 nucleotides in length. They are derived from mature or precursor tRNA molecules through specific cleavage events and have been implicated in a wide range of cellular processes. Increasing evidence indicates that tRFs play important regulatory roles in gene expression, primarily by interacting with target messenger RNAs (mRNAs) to induce transcript degradation, in a manner partially analogous to microRNAs (miRNAs). However, despite their emerging biological relevance and potential roles in disease mechanisms, there remains a significant lack of computational tools capable of systematically predicting the interaction landscape between tRFs and their target mRNAs. Existing databases often rely on limited interaction features and lack the flexibility to accommodate novel or user-defined tRF sequences. The primary goal of this study was to develop a machine learning based prediction algorithm that enables high-throughput, accurate identification of tRF:mRNA binding events, thereby facilitating the functional analysis of tRF regulatory networks. MethodsWe began by assembling a manually curated dataset of 38 687 experimentally verified tRF:mRNA interaction pairs and extracting seven biologically informed features for each pair: (1) AU content of the binding site, (2) site pairing status, (3) binding region location, (4) number of binding sites per mRNA, (5) length of the longest consecutive complementary stretch, (6) total binding region length, and (7) seed sequence complementarity. Using this dataset and feature set, we trained 4 distinct machine learning classifiers—logistic regression, random forest, decision tree, and a multilayer perceptron (MLP)—to compare their ability to discriminate true interactions from non-interactions. Each model’s performance was evaluated using overall accuracy, receiver operating characteristic (ROC) curves, and the corresponding area under the ROC curve (AUC). The MLP consistently achieved the highest AUC among the four, and was therefore selected as the backbone of our prediction framework, which we named tRF Prospect. For biological validation, we retrieved 3 high-throughput RNA-seq datasets from the gene expression omnibus (GEO) in which individual tRFs were overexpressed: AS-tDR-007333 (GSE184690), tRF-3004b (GSE197091), and tRF-20-S998LO9D (GSE208381). Differential expression analysis of each dataset identified genes downregulated upon tRF overexpression, which we designated as putative targets. We then compared the predictions generated by tRF Prospect against those from three established tools—tRFTar, tRForest, and tRFTarget—by quantifying the number of predicted targets for each tRF and assessing concordance with the experimentally derived gene sets. ResultsThe proposed algorithm achieved high predictive accuracy, with an AUC of 0.934. Functional validation was conducted using transcriptome-wide RNA-seq datasets from cells overexpressing specific tRFs, confirming the model’s ability to accurately predict biologically relevant downregulation of mRNA targets. When benchmarked against established tools such as tRFTar, tRForest, and tRFTarget, tRF Prospect consistently demonstrated superior performance, both in terms of predictive precision and sensitivity, as well as in identifying a higher number of true-positive interactions. Moreover, unlike static databases that are limited to precomputed results, tRF Prospect supports real-time prediction for any user-defined tRF sequence, enhancing its applicability in exploratory and hypothesis-driven research. ConclusionThis study introduces tRF Prospect as a powerful and flexible computational tool for investigating tRF:mRNA interactions. By leveraging the predictive strength of deep learning and incorporating a broad spectrum of interaction-relevant features, it addresses key limitations of existing platforms. Specifically, tRF Prospect: (1) expands the range of detectable tRF and target types; (2) improves prediction accuracy through multilayer perceptron model; and (3) allows for dynamic, user-driven analysis beyond database constraints. Although the current version emphasizes miRNA-like repression mechanisms and faces challenges in accurately capturing 5'UTR-associated binding events, it nonetheless provides a critical foundation for future studies aiming to unravel the complex roles of tRFs in gene regulation, cellular function, and disease pathogenesis.

Hearing loss is the most prevalent disabling disease. Cochlear implantation（CI） serves as the primary intervention for severe to profound hearing loss. This consensus systematically explores the value of genetic diagnosis in the pre-operative assessment and efficacy prognosis for CI. Drawing upon domestic and international research and clinical experience, it proposes an evidence-based medicine three-tiered prognostic classification system（Favorable, Marginal, Poor）. The consensus focuses on common hereditary non-syndromic hearing loss（such as that caused by mutations in genes like GJB2, SLC26A4, OTOF, LOXHD1） and syndromic hereditary hearing loss（such as Jervell & Lange-Nielsen syndrome and Waardenburg syndrome）, which are closely associated with congenital hearing loss, analyzing the impact of their pathological mechanisms on CI outcomes. The consensus provides recommendations based on multiple round of expert discussion and voting. It emphasizes that genetic diagnosis can optimize patient selection, predict prognosis, guide post-operative rehabilitation, offer stratified management strategies for patients with different genotypes, and advance the application of precision medicine in the field of CI.
Humans ; Cochlear Implantation ; Prognosis ; Hearing Loss/surgery* ; Consensus ; Connexin 26 ; Mutation ; Sulfate Transporters ; Connexins/genetics*

Alzheimer's disease (AD) is the major form of dementia in the elderly and is closely related to the toxic effects of microglia sustained activation. In AD, sustained microglial activation triggers impaired synaptic pruning, neuroinflammation, neurotoxicity, and cognitive deficits. Accumulating evidence has demonstrated that aberrant expression of deubiquitinating enzymes is associated with regulating microglia function. Here, we use RNA sequencing to identify a deubiquitinase YOD1 as a regulator of microglial function and AD pathology. Further study showed that YOD1 knockout significantly improved the migration, phagocytosis, and inflammatory response of microglia, thereby improving the cognitive impairment of AD model mice. Through LC-MS/MS analysis combined with Co-IP, we found that Myosin heavy chain 9 (MYH9), a key regulator maintaining microglia homeostasis, is an interacting protein of YOD1. Mechanistically, YOD1 binds to MYH9 and maintains its stability by removing the K48 ubiquitin chain from MYH9, thereby mediating the microglia polarization signaling pathway to mediate microglia homeostasis. Taken together, our study reveals a specific role of microglial YOD1 in mediating microglia homeostasis and AD pathology, which provides a potential strategy for targeting microglia to treat AD.

Traditional development of small protein scaffolds has relied on display technologies and mutation-based engineering, which limit sequence and functional diversity, thereby constraining their therapeutic and application potential. Protein design tools have significantly advanced the creation of novel protein sequences, structures, and functions. However, further improvements in design strategies are still needed to more efficiently optimize the functional performance of protein-based drugs and enhance their druggability. Here, we extended an evolution-based design protocol to create a novel minibinder, BindHer, against the human epidermal growth factor receptor 2 (HER2). It not only exhibits super stability and binding selectivity but also demonstrates remarkable properties in tissue specificity. Radiolabeling experiments with ^99mTc, ⁶⁸Ga, and ¹⁸F revealed that BindHer efficiently targets tumors in HER2-positive breast cancer mouse models, with minimal nonspecific liver absorption, outperforming scaffolds designed through traditional engineering. These findings highlight a new rational approach to automated protein design, offering significant potential for large-scale applications in therapeutic mini-protein development.

ObjectiveTo analyze the evolutionary characteristics and genetic variations of the HA (hemagglutinin) and NA (neuraminidase) genes of influenza A(H1N1) viruses in Shanghai during 2024, to investigate their transmission patterns, and to evaluate their potential impact on vaccine effectiveness. MethodsFrom January to October 2024, throat swab specimens were collected from influenza like illness (ILI) patients at 4 hospitals in Shanghai. Real-time fluorescence ploymerase chain reaction (RT-PCR) was used for virus detection and isolation of H1N1 influenza viruses. Forty influenza A(H1N1) virus strains were sequenced using Illumina NovaSeq 6000 platform, followed by phylogenetic analyses, genetic distance analysis, and amino acid variation analyses of HA and NA genes. ResultsPhylogenetic tree of the HA and NA genes revealed that the 40 influenza A(H1N1) virus strains circulating in Shanghai in 2024 exhibited no significant geographic clustering, with a broad origin of strains and complex transmission chains. Genetic distance analyses demonstrated that the average intra-group genetic distances of HA and NA genes among the Shanghai strains were 0.005 1±0.000 6 and 0.004 6±0.000 6, respectively, which were comparable to or higher than those observed in global surveillance strains. Both HA and NA genes displayed frequent mutations. Compared to the 2023‒2024 and 2024‒2025 Northern Hemisphere A(H1N1) vaccine strains (WHO-recommended), the HA proteins of 40 Shanghai strains exhibited amino acid substitutions at positions 120, 137, 142, 169, 216, 223, 260, 277, 356 and 451, with critical mutations at positions 137 and 142 located within the Ca2 antigenic determinant. Furthermore, mutations in the NA protein were observed at positions 13, 50, 200, 257, 264, 339 and 382. ConclusionThe genetic background of the 2024 Shanghai influenza A(H1N1) virus strains is complex and diverse, and antigenic variation may affect vaccine effectiveness. Therefore, it is recommended to enhance genomic surveillance of influenza viruses, evaluate vaccine suitability, and implement more targeted prevention and control strategies against imported influenza viruses.

Objective:To determine the content of the released nickel ion through the 7 extraction media to extract the Ni-Ti wires and to plot the curve of the released nickel ion so as to identify a leaching medium that can be substituted for blood for in vitro Ni release evaluation. Methods:The release of Ni through microwave digestion/inductively coupled plasma mass spectrometry (ICP-MS) in the goat serum was determined. Because of the high content of Ni release, it could be determined by diluting the extraction medium, and other extraction media could be determined directly. Ni release standard curves were plotted by the release amount and different time point variables. Though the different extraction media Ni release curves confirm the specificity of extraction media instead of blood.Results:By analyzing the Ni release curves of seven leaching media, it was found that none of these seven extraction media was suitable for the evaluation of Ni release in in vitro leaching media. Considering the safety of the leaching medium and the simplicity of preparation, hydrochloric acid solution was chosen as the leaching medium, but the concentration needed to be diluted accordingly. Finally, a hydrochloric acid solution was created as an alternative to blood for the in vitro study of Ni release from Ni-Ti alloy cardiovascular products, with a volume fraction of 0.005%. Conclusions:The in vitro leaching medium that can replace blood was found to be hydrochloric acid for the time being, but its concentration was too high, resulting in too much Ni release as well, which deviated from the actual situation. Therefore, the hydrochloric acid solution was diluted step by step, and the Ni release curve was examined until it was close to the clinical release level, and the actual concentration was determined, thus laying a solid foundation for the subsequent evaluation of the safety and risk.

Objective:To establish a set of artificial intelligence (AI) verification rules for blood routine analysis.Methods:Blood routine analysis data of 18 474 hospitalized patients from the First Hospital of Jilin University during August 1st to 31st, 2019, were collected as training group for establishment of the AI verification rules,and the corresponding patient age, microscopic examination results, and clinical diagnosis information were collected. 92 laboratory parameters, including blood analysis report parameters, research parameters and alarm information, were used as candidate conditions for AI audit rules; manual verification combining microscopy was considered as standard, marked whether it was passed or blocked. Using decision tree algorithm, AI audit rules are initially established through high-intensity, multi-round and five-fold cross-validation and AI verification rules were optimized by setting important mandatory cases. The performance of AI verification rules was evaluated by comparing the false negative rate, precision rate, recall rate, F1 score, and pass rate with that of the current autoverification rules using Chi-square test. Another cohort of blood routine analysis data of 12 475 hospitalized patients in the First Hospital of Jilin University during November 1sr to 31st, 2023, were collected as validation group for validation of AI verification rules, which underwent simulated verification via the preliminary AI rules, thus performance of AI rules were analyzed by the above indicators. Results:AI verification rules consist of 15 rules and 17 parameters and do distinguish numeric and morphological abnormalities. Compared with auto-verification rules, the true positive rate, the false positive rate, the true negative rate, the false negative rate, the pass rate, the accuracy, the precision rate, the recall rate and F1 score of AI rules in training group were 22.7%, 1.6%, 74.5%, 1.3%, 75.7%, 97.2%, 93.5%, 94.7%, 94.1, respectively.All of them were better than auto-verification rules, and the difference was statistically significant ( P<0.001), and with no important case missed. In validation group, the true positive rate, the false positive rate, the true negative rate, the false negative rate, the pass rate, the accuracy, the precision rate, the recall rate and F1 score were 19.2%, 8.2%, 70.1%, 2.5%, 72.6%, 89.2%, 70.0%, 88.3%, 78.1, respectively, Compared with the auto-verification rules, The false negative rate was lower, the false positive rate and the recall rate were slightly higher, and the difference was statistically significant ( P<0.001). Conclusion:A set of the AI verification rules are established and verified by using decision tree algorithm of machine learning, which can identify, intercept and prompt abnormal results stably, and is moresimple, highly efficient and more accurate in the report of blood analysis test results compared with auto-vefication.

Objective:To investigate the correlation between miR-137 gene polymorphism and genetic susceptibility to gestational diabetes mellitus.Methods:A total of 500 pregnant women with gestational diabetes who were admitted to Shunde Women and Childrens Hospital of Guangdong Medical University from January 2023 to September 2023 were selected as the observation group, and 500 healthy pregnant women with normal glucose metabolism and no pregnancy complications were selected as the control group. Polymerase chain reaction (PCR) was used to detect rs1625579 polymorphisms of miR-137 gene between the two groups, and the clinical data of the two groups were compared to analyze the influencing factors of the occurrence of gestational diabetes mellitus.Results:The frequencies of GT+ GG genotype and allele G at rs1625579 site of miR-137 gene in observation group were 13.20% and 7.00%, respectively, which were significantly higher than those in control group (all P<0.05). Fasting blood glucose (FPG), fasting insulin (FINS) and insulin resistance index (HOMA-IR) of miR-137 genotype GT+ GG pregnant women in the observation group were (7.92±0.81)mmol/L, (19.92±3.10)mmol/L and 6.60±1.02, respectively. It was significantly higher than genotypic TT pregnant women (all P<0.05), and islet β cell function index (HOMA-β) was significantly lower than genotypic TT pregnant women (188.84±43.34) ( P<0.05). Pre-pregnancy body mass index (BMI) and average weekly weight gain during pregnancy in the observation group were (23.81±1.92)kg/m 2 and (445.50±35.65)g, respectively, which were significantly higher than those in the control group (all P<0.05). The proportion of family history of diabetes in the observation group was 8.60%, which was significantly higher than that in the control group ( P<0.05). Logistic regression analysis showed that preconception BMI and average weekly weight gain during pregnancy were the influential factors for the occurrence of gestational diabetes (all P<0.05). Conclusions:The occurrence of gestational diabetes mellitus has no significant correlation with miR-137 gene polymorphism, but is related to pre-pregnancy BMI and average weekly weight gain during pregnancy. Compared with other miR-137 genotypes, GT+ GG patients were more likely to develop abnormal blood glucose.