[Objective] To retrospectively analyze the detection results of alanine aminotransferase (ALT) unqualified repeat blood donors in Guangzhou, so as to provide evidence for further expanding the repeat blood donor pool, reducing the rate of blood discarding and improving the qualified rate of blood test. [Methods] Blood donors with unqualified ALT in Guangzhou Blood Center from January 2018 to April 2024 were selected as the research objects. The past blood donation and population characteristics were analyzed according to the number of blood donations and ALT unqualified times. [Results] Among repeat blood donors with previous ALT disqualification, 99.5% to 99.7% did not have reactive markers for transfusion-transmitted diseases (TTD), which was higher than the rate among first-time blood donors with unqualified ALT (95.8%) (P<0.05). The rate of single-item ALT disqualification in repeat blood donors was higher in males than in females (P<0.05); it also varied by age (18-25 years > 26-35 years > 36-45 years > over 45 years) (P<0.05); and by quarter (third and fourth quarters > first and second quarters) (P<0.05). The ALT unqualified rate was significantly higher whole blood donors than that of platelet donors and returning blood donors (P<0.05). The overall ALT level (51.0 U/L), individual ALT level (56.0 U/L) and individual ALT unqualified rate (66.7%) of repeat blood donors with multiple ALT disqualifications were higher than those of repeat blood donors with single-item ALT disqualifications (26.0 U/L, 38.5 U/L, and 33.3%, respectively) (P<0.05). Moreover, as the number of ALT disqualifications increased, the overall level of ALT in repeat blood donors also increased (P<0.05), and the average level of individual ALT and individual ALT unqualified ratio tended to increase. Repeat blood donors with frequent ALT disqualifications had higher ALT levels (69.0 U/L). [Conclusion] The ALT unqualified rates of repeat blood donors were mostly non-specific elevation without TTD. Repeat blood donors with multiple ALT disqualifications tend to have continuous high ALT. Moreover, and with the increase of ALT disqualifications times, the overall ALT levels the average individual ALT levels and individual ALT unqualified rates showed an increasing trend.

[Objective] To establish a new scoring system to predict the perioperative outcomes (operation time, intraoperative blood loss, and trifecta achievement) in patients undergoing robot-assisted partial nephrectomy (RAPN) by integrating the RENAL and Mayo adhesive probability (MAP) scores. [Methods] Clinical data of 178 patients with renal cell carcinoma who underwent RAPN performed by the same surgeon in our hospital during Jan.2015 and Jan.2022 were retrospectively analyzed.The RENAL and MAP scores of all patients were calculated.Linear regression and logistic regression were used to evaluate the associations between the components of the RENAL and MAP scores (a total of 6 variables) and perioperative outcomes.The factors with significant associations were then included into logistic regression analysis to identify independent predictors for constructing an assessment system for perioperative outcomes, and the receiver operating characteristic (ROC) curve was plotted to calculate the area under the curve (AUC) to predict its efficacy. [Results] Multivariate linear regression analysis showed that tumor size (β=6.14, 95%CI: 1.93—10.34, P=0.004), exophytic rate (β=10.60, 95%CI: 3.44—17.76, P=0.004), and perinephric fat thickness (β=16.48, 95%CI: 8.52—24.45, P<0.001) were significantly associated with operation time.Tumor size (β=10.55 95%CI: 5.60—15.49, P<0.001) was associated with both intraoperative blood loss and trifecta achievement (OR=1.73, 95%CI: 1.26—2.36, P=0.001). Multivariate logistic regression analysis of these 3 factors identified tumor size (OR=9.07, 95% CI: 1.18—69.45, P=0.03) and perinephric fat thickness (OR=2.28, 95%CI: 1.86—6.04, P=0.01) as independent predictors of perioperative outcomes.Based on these findings, the tumor size and perinephric fat thickness (RP) scoring was constructed, which demonstrated better predictive ability than RENAL score or MAP score alone (RP vs.RENAL vs.MAP: 0.766 vs.0.548 vs.0.684). [Conclusion] The RP score includes fewer variables than the RENAL and MAP scores but outperforms them.

With the widespread use of antibiotics, drug-resistant bacterial infections have become a significant threat to human health. Finding new antibacterial strategies that can effectively control drug-resistant bacterial infections has become an urgent task. Unlike small molecule drugs that target bacterial proteins, antisense oligonucleotide (ASO) can target genes related to bacterial resistance, pathogenesis, growth, reproduction and biofilm formation. By regulating the expression of these genes, ASO can inhibit or kill bacteria, providing a novel approach for the development of antibacterial drugs. To overcome the challenge of delivering antisense oligonucleotide into bacterial cells, various drug delivery systems have been applied in this field, including cell-penetrating peptides, lipid nanoparticles and inorganic nanoparticles, which have injected new momentum into the development of antisense oligonucleotide in the antibacterial realm. This review summarizes the current development of small nucleic acid drugs, the antibacterial mechanisms, targets, sequences and delivery vectors of antisense oligonucleotide, providing a reference for the research and development of antisense oligonucleotide in the treatment of bacterial infections.

OBJECTIVE To predict and validate the potential mechanisms of Kaixinsan （KXS） in ameliorating neuroinflammation in Alzheimer’s disease （AD）. METHODS Network pharmacology was employed to identify core anti- inflammatory components and key inflammatory targets of KXS for AD. Gene ontology （GO） functional annotation， Kyoto Encyclopedia of Genes and Genomes （KEGG） pathway enrichment， and molecular docking were performed. Based on these findings， male SD rats were used to establish an AD model via chronic D-galactose induction. The effects of KXS on AD rats were evaluated， including quantitative behavioral score， learning and memory parameters （escape latency， platform crossings， platform quadrant distance and time）， organ indexes （heart， liver， spleen， thymus）， histopathological alterations in the hippocampus， and expressions of inflammation-related pathway proteins and their upstream/downstream regulators. RESULTS Core anti-inflammatory components of KXS for AD included gomisin B， panaxytriol， gomisin A， enhydrin， vulgarin and panaxydol， while key inflammatory targets involved nuclear factor-kappa B subunit 1（ NFKB1）， nuclear factor-κB p65（ NF-κB p65）， interleukin-1β（ IL- 1β）， IL-6， Toll-like receptor 4 （TLR4）， tumor necrosis factor， nucleotide-binding domain leucine-rich repeat and pyrin domain- containing receptor 3 （NLRP3） and caspase-1 （CASP1）. GO and KEGG pathway enrichment involved inflammatory response， phosphorylation and the NF-κB signaling pathway. Molecular docking confirmed strong binding affinities between core components and key targets. Animal experiments demonstrated that， compared to the model group， KXS significantly alleviated histopathological damage （e.g.， neuronal shrinkage， reduced Nissl bodies in hippocampal CA1， CA3， and DG regions）， increased organ indexes （except for liver index） and Nissl-stained positive cells， improved learning and memory performance， and reduced behavioral scores （at the 8 and 12 weeks of the experiment） and protein expression of NF- κB p65， phosphorylated NF- κB p65， TLR4， NLRP3， CASP1 and IL-1β. CONCLUSIONS KXS effectively mitigates neuroinflammation， reduces hippocampal neuronal injury， and enhances learning and memory ability in AD rats， potentially through suppressing the NF-κB signaling pathway and its upstream/ downstream regulators.

ObjectivePrimary liver cancer, predominantly hepatocellular carcinoma (HCC), is a significant global health issue, ranking as the sixth most diagnosed cancer and the third leading cause of cancer-related mortality. Accurate and early diagnosis of HCC is crucial for effective treatment, as HCC and non-HCC malignancies like intrahepatic cholangiocarcinoma (ICC) exhibit different prognoses and treatment responses. Traditional diagnostic methods, including liver biopsy and contrast-enhanced ultrasound (CEUS), face limitations in applicability and objectivity. The primary objective of this study was to develop an advanced, light-weighted classification network capable of distinguishing HCC from other non-HCC malignancies by leveraging the automatic analysis of brightness changes in CEUS images. The ultimate goal was to create a user-friendly and cost-efficient computer-aided diagnostic tool that could assist radiologists in making more accurate and efficient clinical decisions. MethodsThis retrospective study encompassed a total of 161 patients, comprising 131 diagnosed with HCC and 30 with non-HCC malignancies. To achieve accurate tumor detection, the YOLOX network was employed to identify the region of interest (ROI) on both B-mode ultrasound and CEUS images. A custom-developed algorithm was then utilized to extract brightness change curves from the tumor and adjacent liver parenchyma regions within the CEUS images. These curves provided critical data for the subsequent analysis and classification process. To analyze the extracted brightness change curves and classify the malignancies, we developed and compared several models. These included one-dimensional convolutional neural networks (1D-ResNet, 1D-ConvNeXt, and 1D-CNN), as well as traditional machine-learning methods such as support vector machine (SVM), ensemble learning (EL), k-nearest neighbor (KNN), and decision tree (DT). The diagnostic performance of each method in distinguishing HCC from non-HCC malignancies was rigorously evaluated using four key metrics: area under the receiver operating characteristic (AUC), accuracy (ACC), sensitivity (SE), and specificity (SP). ResultsThe evaluation of the machine-learning methods revealed AUC values of 0.70 for SVM, 0.56 for ensemble learning, 0.63 for KNN, and 0.72 for the decision tree. These results indicated moderate to fair performance in classifying the malignancies based on the brightness change curves. In contrast, the deep learning models demonstrated significantly higher AUCs, with 1D-ResNet achieving an AUC of 0.72, 1D-ConvNeXt reaching 0.82, and 1D-CNN obtaining the highest AUC of 0.84. Moreover, under the five-fold cross-validation scheme, the 1D-CNN model outperformed other models in both accuracy and specificity. Specifically, it achieved accuracy improvements of 3.8% to 10.0% and specificity enhancements of 6.6% to 43.3% over competing approaches. The superior performance of the 1D-CNN model highlighted its potential as a powerful tool for accurate classification. ConclusionThe 1D-CNN model proved to be the most effective in differentiating HCC from non-HCC malignancies, surpassing both traditional machine-learning methods and other deep learning models. This study successfully developed a user-friendly and cost-efficient computer-aided diagnostic solution that would significantly enhances radiologists’ diagnostic capabilities. By improving the accuracy and efficiency of clinical decision-making, this tool has the potential to positively impact patient care and outcomes. Future work may focus on further refining the model and exploring its integration with multimodal ultrasound data to maximize its accuracy and applicability.

ObjectiveTo explore the effect of modified Lianpoyin formula （LPYJWF） in the treatment of Helicobacter pylori （Hp）-associated gastric mucosal damage based on mitochondrial autophagy and NLRP3 inflammasome signaling pathway. MethodsA total of 60 eight-week-old Balb/c male mice were assigned via the random number table method into control， model， high-dose LPYJWF （LPYJWF-H， 27.3 g·kg^-1·d^-1）， medium-dose LPYJWF （LPYJWF-M， 13.65 g·kg^-1·d^-1）， low-dose LPYJWF （LPYJWF-L， 6.83 g·kg^-1·d^-1）， and quadruple therapy groups. Except the control group， other groups were modeled for Hp infection. Mice were administrated with LPYJWF at corresponding doses by gavage. Quadruple therapy group was given omeprazole （6.06 mg·kg^-1·d^-1） + amoxicillin （303 mg·kg^-1·d^-1） + clarithromycin （151.67 mg·kg^-1·d^-1） + colloidal pectin capsules （30.3 mg·kg^-1·d^-1） by gavage. The control group was given an equal volume of 0.9% NaCl for 14 days. Hematoxylin-eosin （HE） staining was used to observe the pathological changes of gastric mucosa， and Warthin-Starry （W-S） silver staining was used to detect Hp colonization. Transmission electron microscopy was employed to observe the mitochondrial ultrastructure of the gastric tissue， and immunofluorescence co-localization assay was adopted to detect the expression of mitochondrial transcription factor A （TFAM） and translocase of the outer mitochondrial membrane member 20 （TOMM20）. The water-soluble tetrazolium salt method and thiobarbituric acid method were used to determine the levels of superoxide dismutase （SOD） and malondialdehyde （MDA）， respectively， in the gastric tissue. Western blot was employed to measure the protein levels of PTEN-induced kinase 1 （PINK1）， Parkin， p62， microtubule-associated protein 1 light chain 3 （LC3）， NOD-like receptor protein 3 （NLRP3）， apoptosis-associated speck-like protein containing a CARD （ASC）， interleukin-1β （IL-1β）， and interleukin-18 （IL-18）. Real-time quantitative PCR was employed to assess the mRNA levels of PINK1， Parkin， p62， and LC3. ResultsCompared with the control group， the model group presented obvious gastric mucosal damage， colonization of a large number of Hp， severe mitochondrial damage， vacuolated structures due to excessive autophagy， reduced TOMM20 and TFAM co-expression in the gastric mucosal tissue， and reduced SOD and increased MDA （P<0.01）. In addition， the gastric tissue in the model group showed up-regulated protein and mRNA levels of PINK1， Parkin， and LC3 and down-regulated protein and mRNA levels of p62 （P<0.01， as well as increased expression of inflammasome-associated proteins NLRP3， ASC， IL-1β， and IL-18 （P<0.01）. Compared with the model group， the LPYJWF and quadruple therapy groups showed alleviated pathological damage of gastric mucosa， reduced Hp colonization， mitigated mitochondrial damage， and increased co-expression of TOMM20 and TFAM. The SOD level was elevated in the LPYJWF-L group （P<0.01）， and the MDA levels became lowered in the LPYJWF and quadruple therapy groups （P<0.05， P<0.01）. Furthermore， the LPYJWF and quadruple therapy groups showed down-regulated mRNA levels of PINK1， Parkin， and LC3 and protein levels of PINK1 and Parkin， and up-regulated mRNA level of p62 （P<0.01）. The LPYJWF-M， LPYJWF-H， and quadruple therapy groups showcased down-regulated LC3 Ⅱ/LC3 Ⅰ level （P<0.05， P<0.01） and up-regulated protein level of p62 （P<0.01）. The expression of inflammasome-associated proteins NLRP3， ASC， IL-1β， and IL-18 were reduced in the LPYJWF and quadruple therapy groups （P<0.05， P<0.01）. ConclusionLPYJWF ameliorates gastric mucosal damage and exerts mucosa-protective effects in Hp-infected mice， which may be related to the inhibition of excessive mitochondrial autophagy， thereby inhibiting the activation of the NLRP3 inflammasome pathway.

ObjectiveTo explore the effect of modified Lianpoyin formula （LPYJWF） in the treatment of Helicobacter pylori （Hp）-associated gastric mucosal damage based on mitochondrial autophagy and NLRP3 inflammasome signaling pathway. MethodsA total of 60 eight-week-old Balb/c male mice were assigned via the random number table method into control， model， high-dose LPYJWF （LPYJWF-H， 27.3 g·kg^-1·d^-1）， medium-dose LPYJWF （LPYJWF-M， 13.65 g·kg^-1·d^-1）， low-dose LPYJWF （LPYJWF-L， 6.83 g·kg^-1·d^-1）， and quadruple therapy groups. Except the control group， other groups were modeled for Hp infection. Mice were administrated with LPYJWF at corresponding doses by gavage. Quadruple therapy group was given omeprazole （6.06 mg·kg^-1·d^-1） + amoxicillin （303 mg·kg^-1·d^-1） + clarithromycin （151.67 mg·kg^-1·d^-1） + colloidal pectin capsules （30.3 mg·kg^-1·d^-1） by gavage. The control group was given an equal volume of 0.9% NaCl for 14 days. Hematoxylin-eosin （HE） staining was used to observe the pathological changes of gastric mucosa， and Warthin-Starry （W-S） silver staining was used to detect Hp colonization. Transmission electron microscopy was employed to observe the mitochondrial ultrastructure of the gastric tissue， and immunofluorescence co-localization assay was adopted to detect the expression of mitochondrial transcription factor A （TFAM） and translocase of the outer mitochondrial membrane member 20 （TOMM20）. The water-soluble tetrazolium salt method and thiobarbituric acid method were used to determine the levels of superoxide dismutase （SOD） and malondialdehyde （MDA）， respectively， in the gastric tissue. Western blot was employed to measure the protein levels of PTEN-induced kinase 1 （PINK1）， Parkin， p62， microtubule-associated protein 1 light chain 3 （LC3）， NOD-like receptor protein 3 （NLRP3）， apoptosis-associated speck-like protein containing a CARD （ASC）， interleukin-1β （IL-1β）， and interleukin-18 （IL-18）. Real-time quantitative PCR was employed to assess the mRNA levels of PINK1， Parkin， p62， and LC3. ResultsCompared with the control group， the model group presented obvious gastric mucosal damage， colonization of a large number of Hp， severe mitochondrial damage， vacuolated structures due to excessive autophagy， reduced TOMM20 and TFAM co-expression in the gastric mucosal tissue， and reduced SOD and increased MDA （P<0.01）. In addition， the gastric tissue in the model group showed up-regulated protein and mRNA levels of PINK1， Parkin， and LC3 and down-regulated protein and mRNA levels of p62 （P<0.01， as well as increased expression of inflammasome-associated proteins NLRP3， ASC， IL-1β， and IL-18 （P<0.01）. Compared with the model group， the LPYJWF and quadruple therapy groups showed alleviated pathological damage of gastric mucosa， reduced Hp colonization， mitigated mitochondrial damage， and increased co-expression of TOMM20 and TFAM. The SOD level was elevated in the LPYJWF-L group （P<0.01）， and the MDA levels became lowered in the LPYJWF and quadruple therapy groups （P<0.05， P<0.01）. Furthermore， the LPYJWF and quadruple therapy groups showed down-regulated mRNA levels of PINK1， Parkin， and LC3 and protein levels of PINK1 and Parkin， and up-regulated mRNA level of p62 （P<0.01）. The LPYJWF-M， LPYJWF-H， and quadruple therapy groups showcased down-regulated LC3 Ⅱ/LC3 Ⅰ level （P<0.05， P<0.01） and up-regulated protein level of p62 （P<0.01）. The expression of inflammasome-associated proteins NLRP3， ASC， IL-1β， and IL-18 were reduced in the LPYJWF and quadruple therapy groups （P<0.05， P<0.01）. ConclusionLPYJWF ameliorates gastric mucosal damage and exerts mucosa-protective effects in Hp-infected mice， which may be related to the inhibition of excessive mitochondrial autophagy， thereby inhibiting the activation of the NLRP3 inflammasome pathway.

Background::Cardiovascular disease (CVD) has emerged as the leading cause of death from prostate cancer (PCa) in recent decades, bringing a great disease burden worldwide. Men with preexisting CVD have an increased risk for major adverse cardiovascular events when treated with androgen deprivation therapy (ADT). The present study aimed to explore the prevalence and risk evaluation of CVD among people with newly diagnosed PCa in China.Methods::Clinical data of newly diagnosed PCa patients were retrospectively collected from 34 centers in China from 2010 to 2022 through convenience sampling. CVD was defined as myocardial infarction, arrhythmia, heart failure, stroke, ischemic heart disease, and others. CVD risk was estimated by calculating Framingham risk scores (FRS). Patients were accordingly divided into low-, medium-, and high-risk groups. χ2 or Fisher’s exact test was used for comparison of categorical variables. Results::A total of 4253 patients were enrolled in the present study. A total of 27.0% (1147/4253) of patients had comorbid PCa and CVD, and 7.2% (307/4253) had two or more CVDs. The enrolled population was distributed in six regions of China, and approximately 71.0% (3019/4253) of patients lived in urban areas. With imaging and pathological evaluation, most PCa patients were diagnosed at an advanced stage, with 20.5% (871/4253) locally progressing and 20.5% (871/4253) showing metastasis. Most of them initiated prostatectomy (46.6%, 1983/4253) or regimens involving ADT therapy (45.7%, 1944/4253) for prostate cancer. In the present PCa cohort, 43.1% (1832/4253) of patients had hypertension, and half of them had poorly controlled blood pressure. With FRS stratification, as expected, a higher risk of CVD was related to aging and metabolic disturbance. However, we also found that patients with treatment involving ADT presented an originally higher risk of CVD than those without ADT. This was in accordance with clinical practice, i.e., aged patients or patients at advanced oncological stages were inclined to accept systematic integrative therapy instead of surgery. Among patients who underwent medical castration, only 4.0% (45/1118) received gonadotropin releasing hormone antagonists, in stark contrast to the grim situation of CVD prevalence and risk.Conclusions::PCa patients in China are diagnosed at an advanced stage. A heavy CVD burden was present at the initiation of treatment. Patients who accepted ADT-related therapy showed an original higher risk of CVD, but the awareness of cardiovascular protection was far from sufficient.

Background::In vitro fertilization (IVF) has emerged as a transformative solution for infertility. However, achieving favorable live-birth outcomes remains challenging. Current clinical IVF practices in IVF involve the collection of heterogeneous embryo data through diverse methods, including static images and temporal videos. However, traditional embryo selection methods, primarily reliant on visual inspection of morphology, exhibit variability and are contingent on the experience of practitioners. Therefore, an automated system that can evaluate heterogeneous embryo data to predict the final outcomes of live births is highly desirable. Methods::We employed artificial intelligence (AI) for embryo morphological grading, blastocyst embryo selection, aneuploidy prediction, and final live-birth outcome prediction. We developed and validated the AI models using multitask learning for embryo morphological assessment, including pronucleus type on day 1 and the number of blastomeres, asymmetry, and fragmentation of blastomeres on day 3, using 19,201 embryo photographs from 8271 patients. A neural network was trained on embryo and clinical metadata to identify good-quality embryos for implantation on day 3 or day 5, and predict live-birth outcomes. Additionally, a 3D convolutional neural network was trained on 418 time-lapse videos of preimplantation genetic testing (PGT)-based ploidy outcomes for the prediction of aneuploidy and consequent live-birth outcomes.Results::These two approaches enabled us to automatically assess the implantation potential. By combining embryo and maternal metrics in an ensemble AI model, we evaluated live-birth outcomes in a prospective cohort that achieved higher accuracy than experienced embryologists (46.1% vs. 30.7% on day 3, 55.0% vs. 40.7% on day 5). Our results demonstrate the potential for AI-based selection of embryos based on characteristics beyond the observational abilities of human clinicians (area under the curve: 0.769, 95% confidence interval: 0.709–0.820). These findings could potentially provide a noninvasive, high-throughput, and low-cost screening tool to facilitate embryo selection and achieve better outcomes. Conclusions::Our study underscores the AI model’s ability to provide interpretable evidence for clinicians in assisted reproduction, highlighting its potential as a noninvasive, efficient, and cost-effective tool for improved embryo selection and enhanced IVF outcomes. The convergence of cutting-edge technology and reproductive medicine has opened new avenues for addressing infertility challenges and optimizing IVF success rates.

Background::Breast cancer is ranked among the most prevalent malignancies in the Chinese female population. However, comprehensive reports detailing the latest epidemiological data and attributable disease burden have not been extensively documented.Methods::In 2018, high-quality cancer surveillance data were recorded in 700 population-based cancer registries in China. We extracted data on female breast cancers (International Classification of Diseases, Tenth Revision [ICD-10]: C50) and estimated the incidence and mortality in 2022 according to the baseline data and corresponding trends from 2010 to 2018. Pathological types were classified according to the ICD for Oncology, 3rd Edition codes. Disability-adjusted life years (DALYs) were calculated as the sum of the years of life lost (YLLs) and years lived with disability (YLDs).Results::In 2022, approximately 357,200 new female breast cancer cases and 75,000 deaths occurred in China, accounting for 15.59% and 7.94% of total new cancer cases and deaths, respectively. The age-standardized incidence rate (ASIR) was 33.04 per 100,000. When analyzed by pathological type, the ASIRs for papillary neoplasms, invasive breast carcinoma, rare and salivary gland-type tumors, and other types were 1.13, 29.79, 0.24, and 1.88 per 100,000, respectively. The age-standardized mortality rate (ASMR) was 6.10 per 100,000. A total of 2,628,000 DALYs were found to be attributable to female breast cancer in China, comprising 2,278,300 YLLs and 349,700 YLDs. The ASIR, ASMR, and age-standardized rate (ASR) for DALYs in urban areas were consistently higher than those in rural areas. We observed a four-fold increase in the ASIR and ASR for DALYs and an eight-fold increase in the ASMR among females over 55 years compared with those aged under 55 years.Conclusion::These data provide invaluable insights into the latest epidemiology of female breast cancer in China and highlight the urgency for disease prevention and control strategy formulation.