Background: Hepatocellular carcinoma (HCC) is a leading cause of cancer mortality, characterized by poor prognosis due to its high proliferative and invasive potential. Tumor metabolic reprogramming, particularly involving glucose metabolism, is essential for tumor survival. This study investigates the role of the Hippo pathway effector transcriptional co-activator with PDZ-binding motif (TAZ) in regulating gluconeogenesis and promoting tumorigenesis in HCC. Methods: TAZ expression in HCC was analyzed using The Cancer Genome Atlas data and validated in clinical samples and cell lines. TAZ was overexpressed or silenced in HCC cell lines to evaluate its effects on cell proliferation, apoptosis, migration, and invasion. The expression and prognostic relevance of the gluconeogenesis-related genes phosphoenolpyruvate carboxykinase 1 (PCK1) and glucose-6-phosphatase (G6PC) were examined, along with their correlation with TAZ expression. Tumor growth was assessed in nude mice. Interactions between TAZ and the glucocorticoid receptor (GR) were investigated using co-immunoprecipitation, immunofluorescence, and chromatin immunoprecipitation assays. Results: TAZ was significantly upregulated in HCC tissues and cell lines. TAZ overexpression enhanced proliferation, reduced apoptosis, and promoted migration and invasion. In contrast, PCK1 and G6PC were downregulated in HCC and showed a negative correlation with TAZ expression. Conclusion TAZ modulates gluconeogenesis and accelerates tumor growth, whereas its knockdown attenuates tumor progression. TAZ interacts with GR, suppressing its transcriptional activity on gluconeogenic gene promoters.

Objective: To explore the correlation between the composition of bone marrow lymphocyte subsets and the clinical attributes observed in de novo AML patients, as well as their influence on prognosis. Methods: A detailed study was carried out on a cohort of 191 de novo acute myeloid leukemia patients who were admitted to our medical center between October 2022 and September 2024. In addition, a group of 24 patients with iron deficiency anemia individuals was carefully chosen as the control cohort. The proportions of lymphocyte subsets within the bone marrow of de novo AML patients were analyzed. Furthermore, an in-depth analysis was performed to investigate the association between the expression levels of these subsets in de novo AML patients and their clinical attributes, as well as their prognostic implications. Results: The proportion of CD19 and CD56 lymphocytes within the bone marrow of de novo AML patients significantly diminished compared to the control cohort (8.5% vs 13.2% P<0.05, and 15.5% vs 18.0%, P<0.05). Conversely, no significant discrepancies were observed in the CD3 , CD3 CD4 , and CD3 CD8 lymphocyte percentages between the AML patients and control group (71.7% vs 72.1%, 32.5% vs 33.7% and 32.8% vs 35.7%, P>0.05). When analyzing the relationships between lymphocyte subsets within the bone marrow of de novo patients and their respective clinical characteristics, patients aged 60 years and above exhibited diminished percentages of CD3 CD8 lymphocytes in the bone marrow compared to their younger counterparts (31.6% vs 34.1%, P<0.05), while the CD56 lymphocyte subsets demonstrated an increased prevalence (17.2% vs 14.4%, P<0.05). Furthermore, patients with leukocytosis (WBC≥100×10 /L) presented lower levels of CD3 and CD3 CD4 lymphocytes in the bone marrow compared with those without it (65.3% vs 72.9% P<0.05, and 28.9% vs 33.2%, P<0.05), respectively. The AML1-ETO fusion gene-positive cohort exhibited a higher prevalence of CD3 CD8 lymphocytes in the bone marrow than in the negative group (38.2% vs 32.3%, P<0.05), whereas the FLT3-ITD mutation-positive group presented a decreased prevalence of CD56 lymphocytes compared with the negative group (12.4% vs 16.8%, P<0.05). In addition, the NPM1 mutation-positive group demonstrated lower levels of CD3 CD8 lymphocytes in the bone marrow than in the negative group (29.1% vs 33.3%, P<0.05). Variables such as tumor protein p53(TP53) mutation positive, the absence of hematopoietic stem cell transplantation, and CD3 CD4 lymphocyte proportions below 25% were identified as independent adverse prognostic indicators for AML patients (P<0.05). Conclusion: The pathogenesis of AML is closely associated with an imbalance in bone marrow lymphocyte subsets. The FLT3-ITD mutation potentially contributes to the dysregulation of CD56 lymphocyte subset expression. The AML1-ETO fusion gene and NPM1 mutation are implicated in the abnormal expression of CD3 CD8 lymphocytes within the bone marrow. Moreover, the percentage of CD3 CD4 lymphocytes in the bone marrow serves as a prognostic factor for de novo AML patients.

Objective: To explore the correlation between the composition of bone marrow lymphocyte subsets and the clinical attributes observed in de novo AML patients, as well as their influence on prognosis. Methods: A detailed study was carried out on a cohort of 191 de novo acute myeloid leukemia patients who were admitted to our medical center between October 2022 and September 2024. In addition, a group of 24 patients with iron deficiency anemia individuals was carefully chosen as the control cohort. The proportions of lymphocyte subsets within the bone marrow of de novo AML patients were analyzed. Furthermore, an in-depth analysis was performed to investigate the association between the expression levels of these subsets in de novo AML patients and their clinical attributes, as well as their prognostic implications. Results: The proportion of CD19 and CD56 lymphocytes within the bone marrow of de novo AML patients significantly diminished compared to the control cohort (8.5% vs 13.2% P<0.05, and 15.5% vs 18.0%, P<0.05). Conversely, no significant discrepancies were observed in the CD3 , CD3 CD4 , and CD3 CD8 lymphocyte percentages between the AML patients and control group (71.7% vs 72.1%, 32.5% vs 33.7% and 32.8% vs 35.7%, P>0.05). When analyzing the relationships between lymphocyte subsets within the bone marrow of de novo patients and their respective clinical characteristics, patients aged 60 years and above exhibited diminished percentages of CD3 CD8 lymphocytes in the bone marrow compared to their younger counterparts (31.6% vs 34.1%, P<0.05), while the CD56 lymphocyte subsets demonstrated an increased prevalence (17.2% vs 14.4%, P<0.05). Furthermore, patients with leukocytosis (WBC≥100×10 /L) presented lower levels of CD3 and CD3 CD4 lymphocytes in the bone marrow compared with those without it (65.3% vs 72.9% P<0.05, and 28.9% vs 33.2%, P<0.05), respectively. The AML1-ETO fusion gene-positive cohort exhibited a higher prevalence of CD3 CD8 lymphocytes in the bone marrow than in the negative group (38.2% vs 32.3%, P<0.05), whereas the FLT3-ITD mutation-positive group presented a decreased prevalence of CD56 lymphocytes compared with the negative group (12.4% vs 16.8%, P<0.05). In addition, the NPM1 mutation-positive group demonstrated lower levels of CD3 CD8 lymphocytes in the bone marrow than in the negative group (29.1% vs 33.3%, P<0.05). Variables such as tumor protein p53(TP53) mutation positive, the absence of hematopoietic stem cell transplantation, and CD3 CD4 lymphocyte proportions below 25% were identified as independent adverse prognostic indicators for AML patients (P<0.05). Conclusion: The pathogenesis of AML is closely associated with an imbalance in bone marrow lymphocyte subsets. The FLT3-ITD mutation potentially contributes to the dysregulation of CD56 lymphocyte subset expression. The AML1-ETO fusion gene and NPM1 mutation are implicated in the abnormal expression of CD3 CD8 lymphocytes within the bone marrow. Moreover, the percentage of CD3 CD4 lymphocytes in the bone marrow serves as a prognostic factor for de novo AML patients.

PANoptosis, a newly defined form of cell death, is characterized by the simultaneous occurrence and crosstalk of apoptosis, pyroptosis, and necroptosis. It has emerged as a promising therapeutic target for various diseases. Ovarian dysfunction is marked by oligomenorrhea or amenorrhea, elevated gonadotropin levels, and diminished estrogen levels, often accompanying subfertility or infertility. Additionally, it can manifest with perimenopausal syndrome and increase the risks of osteoporosis, cardiovascular disease, and cognitive impairments, seriously impacting patients' quality of life. While current studies have reported that ovarian hypofunction is associated with apoptosis, pyroptosis, or necroptosis, a systematic investigation into the relationship between PANoptosis and ovarian hypofunction is still absent. This review aims to elucidate the potential link between these two phenomena, providing new insights into the mechanisms underlying ovarian hypofunction and potential treatment strategies.
Humans ; Female ; Ovary/metabolism* ; Apoptosis ; Animals ; Necroptosis ; Ovarian Diseases/physiopathology*

Objective To explore the role of Schlafen4(SLFN4)in acute pneumonia induced by hypervirulent Klebsiella pneumoniae(hvKp)via intratracheal aerosolization.Methods Differential expression gene Slfn4 was identified after infection with hvKp based on RNA sequencing(RNA-seq)and single-cell RNA sequencing(scRNA-seq)data before Slfn4-/-mice were obtained via CRISPR/Cas gene editing technology.Slfn4-/-mice and wild mice were challenged via intratracheal aerosolization.Mortality and weight changes were recorded for 14 d,while pathological changes and expression levels of interleukin-6(IL-6),IL-17A,IL-1β,and tumor necrosis factor-α(TNF-α)were detected at 48 h post-infection.Results SLFN4 expression was significantly increased in wild mice after infection with hvKp.Survival was significantly increased,and weight loss was mitigated before gradual recovery in Slfn4-/-mice after infection.The knockout of SLFN4 attenuated alveolar wall thickening,diminished neutrophil infiltration,and suppressed pro-inflammatory cytokine production(IL-6,IL-17A,IL-1β,TNF-α)in the lung at 48 h post-infection.Conclusion The deletion of SLFN4 may suppress the expression of specific pro-inflammatory cytokines and attenuate neutrophil over-recruitment in the lung,thereby alleviating pneumonia in mice after hvKp infection.

Objective : To identify autophagy-related genes involved in pulmonary infection caused by the highly drug-resistant and virulent methicillin-resistant Staphylococcus aureus strain USA300 ( USA300) ，and to explore the underlying molecular mechanisms ， thereby providing potential targets for immunotherapy． Methods: The GSE220943 dataset of a USA300-induced pulmonary infection mouse model was obtained from the GEO database． Differentially expressed genes ( DEGs ) were identified using the DESeq2 package． Autophagy-related genes ( ARGs) were retrieved from the MSigDB and Autophagy databases．Weighted gene co-expression network analysis ( WGCNA) was performed to construct gene co-expression modules．Genes overlapping among DEGs，ARGs，and WGCNA modules were identified as autophagy-related DEGs．Gene Ontology ( GO) enrichment analysis was con- ducted using the clusterProfiler R package，while Kyoto Encyclopedia of Genes and Genomes ( KEGG) pathway en- richment analysis was performed via the Metascape platform．Immune cell infiltration was analyzed using the Immu- CellAI-mouse website．A protein - protein interaction ( PPI) network was constructed using the STRING database， and hub genes were identified through topological analysis in Cytoscape． Receiver operating characteristic curve ( ROC) curves were plotted via the website https: / /www．bioinformatics．com．cn． Finally，key gene expression was validated in mouse lung tissues by real-time quantitative reverse transcription PCR ( RT-qPCR) ． Results: A total of 6 135，4 075，3 680，and 2 342 differentially expressed genes ( DEGs) were identified at 12，24，48，and 96 hours post-infection，respectively．By integrating DEGs，autophagy-related genes ( ARGs) ，and WGCNA mod- ules，19 autophagy-related DEGs were identified． GO and KEGG enrichment analyses indicated that these genes were mainly involved in CD4 + T cell activation and regulation，innate immune responses，and autophagosome mem- brane formation．Immune infiltration analysis revealed that innate immune cells such as neutrophils and dendritic cells predominated during the early phase of infection，while γδ T cells and M2 macrophages became more promi- nent in the later stages．PPI network analysis identified 12 hub autophagy-related genes，among which three upreg- ulated key genes ( Eif2ak2，Ikbke，and Nfkbiz) were further confirmed．The area under the ROC curve for all three genes was 1. 000．RT-qPCR validation demonstrated significantly elevated expression of these three genes in lung tissues at 24 hours post-infection ( all P＜0. 05) ． Conclusion Eif2ak2，Ikbke，and Nfkbiz may be involved in the pulmonary infection caused by USA300 by promoting autophagy and hold promise as potential targets for immuno- therapy．

Objective:To investigate the diagnostic value of urine pH, serum uric acid and related clinical indicators in the diagnosis of urinary infection stones, and to construct a prediction nomogram.Methods:The clinical data of 432 patients with urinary calculi admitted to Xuzhou Central Hospital from August 2018 to November 2023 were retrospectively analyzed. The study included 289 males and 143 females, with an average age of (52.72±13.46) years old. Among the patients, there were 98 cases of hypertension, 67 cases of diabetes, and 100 cases of recurrent calculi. Kidney stones were present in 152 cases, ureteral stones in 242 cases, and bladder stones in 38 cases. Urine bacterial culture yielded positive results in 97 cases. According to the results of postoperative stone composition analysis, the two groups were categorized as infection and no-infection stone groups, and the differences in general data between the two groups were compared. Univariate and multivariate logistic regression analysis were conducted to assess the diagnostic value of urine pH, serum uric acid, and related clinical indicators for urinary infection stones. Receiver operating characteristic (ROC) curve and area under curve (AUC) were utilized to evaluate the clinical significance of urine pH, serum uric acid, and combined indexes in preoperatively diagnosing urinary infection stones, as well as constructing a nomogram prediction model.Results:There were 127 cases of infection stones and 305 cases of no-infection stones. The infection stone group exhibited a higher urine pH value [7.0(6.5, 7.5) vs. 6.0(5.5, 6.5), P<0.001], lower serum uric acid levels [(301.38±70.12) vs. (358.88±88.99) μmol/L, P<0.001], a higher proportion of females [55.1%(70/127) vs. 23.9%(73/305), P<0.001], younger age [(48.36±14.83)vs. (53.12±12.61)years old, P<0.001], a higher proportion of recurrence stones [34.6 %(44/127) vs. 18.4%(56/305), P<0.001], and a higher rate of positive urine bacteria culture[29.9%(38/127)vs. 19.3%(59/305), P=0.016]and nitrite test results [18.9%(24/127)vs. 6.3%(19/305), P <0.001]. Univariate logistic regression analysis revealed that urine pH value, serum uric acid levels, gender, age, recurrent stones, urine bacterial culture, and urine nitrite were associated with urinary infection stones ( P< 0.05). Multivariate logistic regression analysis revealed that urine pH value ( OR= 4.836, 95% CI 3.342-6.997), female gender( OR=2.320, 95% CI 1.286-4.186), recurrent stones ( OR=2.225, 95% CI 1.208-4.101), positive urine bacterial culture ( OR=2.061, 95% CI 1.094-3.883), serum uric acid ( OR=0.992, 95% CI 0.949-0.989), age ( OR=0.969, 95% CI 0.949-0.990) were independent risk factors for urinary infection stones ( P<0.05). The combined diagnostic value of six indicators was the highest, with an AUC of 0.874 (95% CI 0.837-0.911). Following this, urine pH exhibited an AUC of 0.818 (95% CI 0.778-0.858), while serum uric acid had an AUC of 0.704 (95% CI 0.652-0.756). The nomogram model was successfully constructed based on the six indicators. The mean AUC of the ROC curve after 1 000 resamples of the Bootstrap method was 0.864 (95% CI 0.828-0.900), and the calibration curve showed that the predicted curve fit the ideal curve well, with a mean absolute error of 0.005 and a Hosmer-Lemeshow test of P>0.05. Clinical decision curve analysis (DCA) showed that the model had a higher net clinical benefit when the model had a threshold probability value≥0.01. Conclusions:Urine pH and serum uric acid are closely related to urinary infection stones. A nomogram model combining these factors with gender, age, recurrent stones, and urine culture results can effectively predict the probability of infection-related stones, providing significant clinical value.

Objective:To investigate the diagnostic value of urine pH, serum uric acid and related clinical indicators in the diagnosis of urinary infection stones, and to construct a prediction nomogram.Methods:The clinical data of 432 patients with urinary calculi admitted to Xuzhou Central Hospital from August 2018 to November 2023 were retrospectively analyzed. The study included 289 males and 143 females, with an average age of (52.72±13.46) years old. Among the patients, there were 98 cases of hypertension, 67 cases of diabetes, and 100 cases of recurrent calculi. Kidney stones were present in 152 cases, ureteral stones in 242 cases, and bladder stones in 38 cases. Urine bacterial culture yielded positive results in 97 cases. According to the results of postoperative stone composition analysis, the two groups were categorized as infection and no-infection stone groups, and the differences in general data between the two groups were compared. Univariate and multivariate logistic regression analysis were conducted to assess the diagnostic value of urine pH, serum uric acid, and related clinical indicators for urinary infection stones. Receiver operating characteristic (ROC) curve and area under curve (AUC) were utilized to evaluate the clinical significance of urine pH, serum uric acid, and combined indexes in preoperatively diagnosing urinary infection stones, as well as constructing a nomogram prediction model.Results:There were 127 cases of infection stones and 305 cases of no-infection stones. The infection stone group exhibited a higher urine pH value [7.0(6.5, 7.5) vs. 6.0(5.5, 6.5), P<0.001], lower serum uric acid levels [(301.38±70.12) vs. (358.88±88.99) μmol/L, P<0.001], a higher proportion of females [55.1%(70/127) vs. 23.9%(73/305), P<0.001], younger age [(48.36±14.83)vs. (53.12±12.61)years old, P<0.001], a higher proportion of recurrence stones [34.6 %(44/127) vs. 18.4%(56/305), P<0.001], and a higher rate of positive urine bacteria culture[29.9%(38/127)vs. 19.3%(59/305), P=0.016]and nitrite test results [18.9%(24/127)vs. 6.3%(19/305), P <0.001]. Univariate logistic regression analysis revealed that urine pH value, serum uric acid levels, gender, age, recurrent stones, urine bacterial culture, and urine nitrite were associated with urinary infection stones ( P< 0.05). Multivariate logistic regression analysis revealed that urine pH value ( OR= 4.836, 95% CI 3.342-6.997), female gender( OR=2.320, 95% CI 1.286-4.186), recurrent stones ( OR=2.225, 95% CI 1.208-4.101), positive urine bacterial culture ( OR=2.061, 95% CI 1.094-3.883), serum uric acid ( OR=0.992, 95% CI 0.949-0.989), age ( OR=0.969, 95% CI 0.949-0.990) were independent risk factors for urinary infection stones ( P<0.05). The combined diagnostic value of six indicators was the highest, with an AUC of 0.874 (95% CI 0.837-0.911). Following this, urine pH exhibited an AUC of 0.818 (95% CI 0.778-0.858), while serum uric acid had an AUC of 0.704 (95% CI 0.652-0.756). The nomogram model was successfully constructed based on the six indicators. The mean AUC of the ROC curve after 1 000 resamples of the Bootstrap method was 0.864 (95% CI 0.828-0.900), and the calibration curve showed that the predicted curve fit the ideal curve well, with a mean absolute error of 0.005 and a Hosmer-Lemeshow test of P>0.05. Clinical decision curve analysis (DCA) showed that the model had a higher net clinical benefit when the model had a threshold probability value≥0.01. Conclusions:Urine pH and serum uric acid are closely related to urinary infection stones. A nomogram model combining these factors with gender, age, recurrent stones, and urine culture results can effectively predict the probability of infection-related stones, providing significant clinical value.

OBJECTIVE To optimize the freeze-drying process for sheep placenta slices. METHODS An orthogonal test design was used with pre-freezing time， drying time and drying temperature as indicators to screen for the optimal freeze-drying process for sheep placenta slices. The peptide content， ethanol-soluble extract content， and freeze-drying rate of sheep placenta were used as indicators，the analytic hierarchy process-criteria importance through intercriteria correlation （AHP-CRITIC） method was employed to determine the weight of each indicator and calculate the comprehensive score， which was verified using the technique for order of preference by similarity to ideal solution （TOPSIS） model. RESULTS The optimal preparation process was found to be the pre-freezing time of 2 hours， the drying time of 16 hours， and the drying temperature of 30 °C. The average values of peptide content， ethanol-soluble extract content， and freeze-drying rate for three batches of samples were 5.883 mg/mL， 27.1%， and 95.77%， respectively； the comprehensive scores of three batches were 96.42， 99.18 and 99.58， with RSD of 1.75%. CONCLUSIONS This study successfully optimized the freeze-drying process for sheep placenta slices， which can provide a reference for the quality standard setting and industrial production of this type of slice.

Objective To establish an artificial intelligence (AI)-assisted platform for detection of parasite eggs, and to evaluate its detection efficiency and accuracy, so as to provide technical supports for elimination of parasitic diseases. Methods A total of 1 003 slides of Enterobius vermicularis, horkworm, Trichuris trichiura, Clonorchis sinensis, Taenia, Ascaris lumbricoides, Schistosoma japonicum, Paragonimus westermani and Fasciolopsis buski eggs were collected, and converted into digital images with an automatated scanning microscope to create a dataset. Based on the Object Detection platform on the Baidu Easy DL model, an AI-assisted platform for detection of parasite eggs was created through procedures of uploading, labeling, training, evaluation and optimization. Then, 70% of the datasets were randomly selected for model training, and the precision, recall and average accuracy were calculated to evaluate the effectiveness of platform for recognition of parasite eggs. In addition, the platform was deployed on the computer and smart phone terminals for use. Results An AI-assisted platform for detection of parasite eggs was successfully created. If the platform was deployed using the public cloud application programming interface (API), the average accuracy, precision and recall of the platform were 93.42%, 92.55% and 89.32% for recognition of parasite eggs. If the platform was deployed using the offline software development kit (SDK), the average accuracy, precision and recall of the platform were 92.97%, 94.78% and 87.63% for recognition of parasite eggs. In addition, the precision of the platform was 97.00% and 96.23% for identification of Taenia and C. sinensis eggs, respectively. Conclusions The AI-assisted platform for detection of parasite eggs has been successfully created, which is high in the accuracy for recognition of parasite eggs and convenient in use. This platform may provide a powerful technical support for parasitic disease diagnosis.