Objective:To develop CNN-Dinov2, a deep learning-based automatic classification model for Gram-stained images, enabling rapid diagnosis of three prevalent Gram-negative bacilli in bloodstream infections: Escherichia coli ( E.coli), Klebsiella pneumoniae ( K.pneumoniae), and Pseudomonas aeruginosa ( P.aeruginosa). Methods:This evaluation study analyzed 1 425 Gram-stained microscopic images from patients with bloodstream infections at Houjie Hospital, in Dongguan City, collected between January 2023 and January 2024. The images, all positive for blood culture and identified as target strains, were categorized into Escherichia coli (419 images), Klebsiella pneumoniae (411 images), Pseudomonas aeruginosa (413 images), and other Gram-negative bacilli (182 images). They were randomly split into a training set (1 141 images), a validation set (141 images), and a test set (143 images) in an 8∶1∶1 ratio. A hybrid CNN-Dinov2 model was developed by integrating ResNet′s local feature extraction with Dinov2′s global pre-trained features, followed by a fully connected layer. The model was optimized by inputting the preprocessed images and adjusting parameters through loss calculation and backpropagation. AlexNet, Dinov2, and ResNet18 served as control models. The models′ classification performance was assessed using accuracy, precision, weighted F1 score, and recall rate, derived from the confusion matrix. The PR curve and AP value further evaluated each model′s classification capability across the four image categories. Results:The CNN-Dinov2 model achieved a training accuracy of 99.74%, a validation accuracy of 98.12%, and a validation loss of 0.070 6, demonstrating robust generalization without overfitting. Validation metrics revealed superior performance with an accuracy of 98.60%, precision of 98.65%, a weighted F1 score of 98.60%, and a recall rate of 98.60%, outperforming other models. The confusion matrix confirmed its strong classification capability, with the highest sum of diagonal values for identifying four types of bacteria. The macro average precision (AP) values under the precision-recall (PR) curves were all 1, indicating excellent discrimination across all categories. Overall, the CNN-Dinov2 model exhibited the best performance among the four models evaluated.Conclusion:This study successfully developed CNN-Dinov2, an automated classification model for Gram staining images. It offers valuable support for the rapid diagnosis of bloodstream infections caused by Escherichia coli, Klebsiella pneumoniae, and Pseudomonas aeruginosa, demonstrating practical utility.

Objective:To develop CNN-Dinov2, a deep learning-based automatic classification model for Gram-stained images, enabling rapid diagnosis of three prevalent Gram-negative bacilli in bloodstream infections: Escherichia coli ( E.coli), Klebsiella pneumoniae ( K.pneumoniae), and Pseudomonas aeruginosa ( P.aeruginosa). Methods:This evaluation study analyzed 1 425 Gram-stained microscopic images from patients with bloodstream infections at Houjie Hospital, in Dongguan City, collected between January 2023 and January 2024. The images, all positive for blood culture and identified as target strains, were categorized into Escherichia coli (419 images), Klebsiella pneumoniae (411 images), Pseudomonas aeruginosa (413 images), and other Gram-negative bacilli (182 images). They were randomly split into a training set (1 141 images), a validation set (141 images), and a test set (143 images) in an 8∶1∶1 ratio. A hybrid CNN-Dinov2 model was developed by integrating ResNet′s local feature extraction with Dinov2′s global pre-trained features, followed by a fully connected layer. The model was optimized by inputting the preprocessed images and adjusting parameters through loss calculation and backpropagation. AlexNet, Dinov2, and ResNet18 served as control models. The models′ classification performance was assessed using accuracy, precision, weighted F1 score, and recall rate, derived from the confusion matrix. The PR curve and AP value further evaluated each model′s classification capability across the four image categories. Results:The CNN-Dinov2 model achieved a training accuracy of 99.74%, a validation accuracy of 98.12%, and a validation loss of 0.070 6, demonstrating robust generalization without overfitting. Validation metrics revealed superior performance with an accuracy of 98.60%, precision of 98.65%, a weighted F1 score of 98.60%, and a recall rate of 98.60%, outperforming other models. The confusion matrix confirmed its strong classification capability, with the highest sum of diagonal values for identifying four types of bacteria. The macro average precision (AP) values under the precision-recall (PR) curves were all 1, indicating excellent discrimination across all categories. Overall, the CNN-Dinov2 model exhibited the best performance among the four models evaluated.Conclusion:This study successfully developed CNN-Dinov2, an automated classification model for Gram staining images. It offers valuable support for the rapid diagnosis of bloodstream infections caused by Escherichia coli, Klebsiella pneumoniae, and Pseudomonas aeruginosa, demonstrating practical utility.

To investigate the genomic features and perform cluster analysis of Carbapenem-resistant Klebsiella pneumoniae (CRKP) to provide an experimental basis for guiding the prevention and treatment of CRKP infections.A retrospective case-cohort study was conducted on 19 non-redundant CRKP strains isolated from the Tenth Affiliated Hospital of Southern Medical University between January and June 2023. Whole genome sequencing (WGS) and multilocus sequence typing (MLST) were performed to compare genomic features and analyze the resistance genes and homology of the strains.The results showed that the 19 CRKP strains were isolated from 8 different clinical departments, mainly from respiratory specimens. The whole genome sequencing revealed that the genomic lengths of CRKP ranged from 4.90 to 5.85 Mbp, with contigs N50 values>20 kb for each genome. The median overall GC content was 57.0% (50.4%-57.1%). Comparative genomic analysis identified three regions with high genomic variability. WGS detected 32 resistance genes across 11 categories. All 19 strains carried carbapenem resistance genes ( blaKPC-2 and blaOXA-48), blaTEM-1B extended-spectrum β-lactamase resistance genes, qnrS1 quinolone resistance gene, and fosA fosfomycin resistance gene, with each strain carrying only one carbapenemase gene. The detection rate of blaKPC-2 was 94.7% (18/19). MLST identified three sequence types: ST11, ST437 and ST147, with ST11 being predominant (89.5%, 17/19). Clustering analysis based on acquired resistance genes revealed three clonal transmission patterns among strains 72 and 90, and strains 88, 84, 66 and 79.In conclusion, CRKP strains carry multiple resistance genes, and clustering analysis indicating that nosocomial clonal transmission is closely related to acquired resistance genes. The ST11- blaKPC-2 type strain is the predominant clone. Strengthened surveillance and effective control strategies are necessary to reduce nosocomial transmission of CRKP.

To investigate the genomic features and perform cluster analysis of Carbapenem-resistant Klebsiella pneumoniae (CRKP) to provide an experimental basis for guiding the prevention and treatment of CRKP infections.A retrospective case-cohort study was conducted on 19 non-redundant CRKP strains isolated from the Tenth Affiliated Hospital of Southern Medical University between January and June 2023. Whole genome sequencing (WGS) and multilocus sequence typing (MLST) were performed to compare genomic features and analyze the resistance genes and homology of the strains.The results showed that the 19 CRKP strains were isolated from 8 different clinical departments, mainly from respiratory specimens. The whole genome sequencing revealed that the genomic lengths of CRKP ranged from 4.90 to 5.85 Mbp, with contigs N50 values>20 kb for each genome. The median overall GC content was 57.0% (50.4%-57.1%). Comparative genomic analysis identified three regions with high genomic variability. WGS detected 32 resistance genes across 11 categories. All 19 strains carried carbapenem resistance genes ( blaKPC-2 and blaOXA-48), blaTEM-1B extended-spectrum β-lactamase resistance genes, qnrS1 quinolone resistance gene, and fosA fosfomycin resistance gene, with each strain carrying only one carbapenemase gene. The detection rate of blaKPC-2 was 94.7% (18/19). MLST identified three sequence types: ST11, ST437 and ST147, with ST11 being predominant (89.5%, 17/19). Clustering analysis based on acquired resistance genes revealed three clonal transmission patterns among strains 72 and 90, and strains 88, 84, 66 and 79.In conclusion, CRKP strains carry multiple resistance genes, and clustering analysis indicating that nosocomial clonal transmission is closely related to acquired resistance genes. The ST11- blaKPC-2 type strain is the predominant clone. Strengthened surveillance and effective control strategies are necessary to reduce nosocomial transmission of CRKP.

Colorectal tumors often create an immunosuppressive microenvironment that prevents them from responding to immunotherapy.Cannabidiol(CBD)is a non-psychoactive natural active ingredient from the cannabis plant that has various pharmacological effects,including neuroprotective,antiemetic,anti-inflammatory,and antineoplastic activities.This study aimed to elucidate the specific anticancer mechanism of CBD by single-cell RNA sequencing(scRNA-seq)and single-cell ATAC sequencing(scATAC-seq)technologies.Here,we report that CBD inhibits colorectal cancer progression by modulating the suppressive tumor microenvironment(TME).Our single-cell transcriptome and ATAC sequencing results showed that CBD suppressed M2-like macrophages and promoted M1-like macrophages in tumors both in strength and quantity.Furthermore,CBD significantly enhanced the interaction between M1-like macrophages and tumor cells and restored the intrinsic anti-tumor properties of macrophages,thereby preventing tumor progression.Mechanistically,CBD altered the metabolic pattern of macro-phages and related anti-tumor signaling pathways.We found that CBD inhibited the alternative acti-vation of macrophages and shifted the metabolic process from oxidative phosphorylation and fatty acid oxidation to glycolysis by inhibiting the phosphatidylinositol 3-kinase-protein kinase B signaling pathway and related downstream target genes.Furthermore,CBD-mediated macrophage plasticity enhanced the response to anti-programmed cell death protein-1(PD-1)immunotherapy in xenografted mice.Taken together,we provide new insights into the anti-tumor effects of CBD.

BACKGROUND: Sarcopenia is an age-related progressive skeletal muscle disorder involving the loss of muscle mass or strength and physiological function. Efficient and precise AI algorithms may play a significant role in the diagnosis of sarcopenia. In this study, we aimed to develop a machine learning model for sarcopenia diagnosis using clinical characteristics and laboratory indicators of aging cohorts. METHODS: We developed models of sarcopenia using the baseline data from the West China Health and Aging Trend (WCHAT) study. For external validation, we used the Xiamen Aging Trend (XMAT) cohort. We compared the support vector machine (SVM), random forest (RF), eXtreme Gradient Boosting (XGB), and Wide and Deep (W&D) models. The area under the receiver operating curve (AUC) and accuracy (ACC) were used to evaluate the diagnostic efficiency of the models. RESULTS: The WCHAT cohort, which included a total of 4057 participants for the training and testing datasets, and the XMAT cohort, which consisted of 553 participants for the external validation dataset, were enrolled in this study. Among the four models, W&D had the best performance (AUC = 0.916 ± 0.006, ACC = 0.882 ± 0.006), followed by SVM (AUC =0.907 ± 0.004, ACC = 0.877 ± 0.006), XGB (AUC = 0.877 ± 0.005, ACC = 0.868 ± 0.005), and RF (AUC = 0.843 ± 0.031, ACC = 0.836 ± 0.024) in the training dataset. Meanwhile, in the testing dataset, the diagnostic efficiency of the models from large to small was W&D (AUC = 0.881, ACC = 0.862), XGB (AUC = 0.858, ACC = 0.861), RF (AUC = 0.843, ACC = 0.836), and SVM (AUC = 0.829, ACC = 0.857). In the external validation dataset, the performance of W&D (AUC = 0.970, ACC = 0.911) was the best among the four models, followed by RF (AUC = 0.830, ACC = 0.769), SVM (AUC = 0.766, ACC = 0.738), and XGB (AUC = 0.722, ACC = 0.749). CONCLUSIONS: The W&D model not only had excellent diagnostic performance for sarcopenia but also showed good economic efficiency and timeliness. It could be widely used in primary health care institutions or developing areas with an aging population. TRIAL REGISTRATION Chictr.org, ChiCTR 1800018895.
Humans ; Aged ; Sarcopenia/diagnosis* ; Deep Learning ; Aging ; Algorithms ; Biomarkers

Objective:Based on the principle that the aggregation-induced emission (AIE) fluorescent probe 6PD-DPAN could bind and aggregate with bacteria, and the fluorescence intensity could reflect the quantity of bacteria, a new method for rapid, convenient, and accurate bacterial drug sensitivity testing was established, which provided a basis for rapid and accurate clinical drug use.Methods:This was a methodological evaluation study. A total of 107 clinical isolates were collected from Houjie Hospital of Dongguan City from January to December 2022, among which 46 isolates were used for the establishment of the new method, and 61 isolates were used for methodological validation. The minimum inhibitory concentration (MIC) determined by broth microdilution method was used as the gold standard, and three antibacterial drugs, gentamicin, levofloxacin, and cefotaxime, were used as experimental drugs. The AIE plate was incubated for 4 hours, and the fluorescence intensity was measured every half an hour to draw a fluorescence change curve. The MIC results were compared with the CLSI breakpoints to determine the bacteria as sensitive, intermediate, or resistant. To simplify the detection process, the ratio of fluorescence intensity at 4 hours(R) was calculated, and the ROC curve was used to analyze the efficacy of R in determining bacterial growth and establish its cutoff value. The new method was used to determine the MIC of 61 clinical isolates, with broth microdilution method as the gold standard. The basic consistency, categorical consistency, very major errors, and major errors of the new method were analyzed, and the consistency between the two methods was determined by the Kappa test.Results:ROC curve analysis of the R after 4 hours of culture: The cut-off value was 3.0, with both sensitivity and specificity for determining bacterial growth being 100%. The median (interquartile) R for bacterial growth inhibition was 11.1 (8.6, 14.4); the median R-value for bacterial growth was 1.1 (1.0, 1.2). Compared to the gold standard, the newly established method showed 100% (61/61) essential agreement in detecting MICs of 61 clinical isolates, with a categorical agreement of 96.7% (59/61). There were no very major or major errors, and the Kappa value was 0.94, indicating good consistency between the newly established method and the microbroth dilution method.Conclusions:This study successfully established a new method for bacterial drug sensitivity testing based on AIE technology, which could obtain satisfactory results within 5 hours, providing a basis for early precision drug treatment in clinical practice.

Objective:To explore the effect of minimally invasive hematoma puncture and drainage in the treatment of elderly patients with cerebral hemorrhage by using 3D slicer and Sina software to conduct 3D reconstruction and preoperative localization of intracerebral hematoma.Methods:A total of 74 elderly patients with a first-onset intracerebral hematoma aged ≥75 years, having surgical indications and stable vital signs were grouped into 3D slicer plus Sina software localization group(as group A, n=40)or CT localization group(as group B, n=34). Based on the localization, hematoma puncture and drainage were performed after local anesthesia.Preoperative preparation time, hematoma location, puncture success rate, postoperative hematoma clearance rate, postoperative re-bleeding rate and GCS score were statistically analyzed.Glasgow coma scale(GCS)scores were used in predicting the mortality.Results:The preoperative preparation time was significantly shorter in group A than in group B[(5.5±3.4)min vs.(8.5±2.7)min, t=3.337, P=0.001]. The success rate of hematoma puncture and drainage(90.0% and 70.6%, χ2=4.51, P=0.034)and postoperative hematoma clearance rate[(87.5±3.4)% and(80.3±2.7)%, t=10.10, P=0.000]were higher in group A than in group B. There were no significant differences in operative time, the accuracy of hematoma localization, re-bleeding rate and GCS score between the two groups( P>0.05). Conclusions:3D slicer plus Sina software can precisely locate the intracerebral hematoma, and minimally invasive hematoma puncture and drainage of intracerebral hematoma under local anesthesia were safe and effective in the treatment of elderly patients with intracerebral hemorrhage.

Objective: To establish a method for simultaneous and rapid detecting of the polymorphisms in Cytochrome P450 2C9 (CYP2C9), CYP2C19, CYP4F2, Vitamin K epoxide reductase (VKORC1) and ATP-binding cassette subfamily B member1 (ABCB1) gene, which were associated with warfarin and clopidogrel, based on liquid phase chip technology. Methods: Method establishment. The eight gene sequences near targeted sites related to warfarin and clopidogrel were found in Genbank, and the specific primers and probes were designed. Through multiple PCR amplification, followed by allele specific primer extension (ASPE), and MagPlex-Tag microspheres hybridization, the suspension array Luminex 200 system step-by-step, the genotypes were determined by fluorescence signal. The reaction system was optimized and its methodological evaluation was performed. 260 patients with antithrombotic therapy from Dongguan houjie hospital were recruited in this study form June 2017 to December 2018. The eight genotypes of the 260 patients were detected by the established method, and the results were compared with the sequencing results. Results: The results of 260 samples showed that allelic median fluorescence intensity (MFI) ratios of homozygotes (mutant/wild-type) were all greater than 0.9 or less than 0.1, and all the allelic MFI ratios of heterozygotes were between 0.3 and 0.6. The within run and between run coefficients of variance for allelic MFI ratios were lower than 6.4% and 10.9%, respectively. The minimum DNA template requirements was 0.75ng. The genotypes of 260 patients determined by the established method were completely concordant with the sequencing results. Conclusion A method was established successfully for rapid detecting the genotypes which associated with warfarin and clopidogrel based on liquid phase chip technology.

Objective: To observe the efficacy and safety related measures by blocking mother-to-child transmission of hepatitis B virus with high viral load and HBeAg positivity during pregnancy in Guizhou province. Methods: Outpatient and inpatient cases of the Department of Infectious Diseases and Obstetrics of Guizhou Medical University Affiliated Hospitals from May 2016 to July 2017 were retrospectively divided into intervention group, non-intervention group and non- hepatitis B pregnant women group; with 75 cases in each group. HBsAg and HBeAg were positive in the intervention group. Pregnant women with HBV DNA ≥10⁶ IU/ml were treated with anti-HBV therapy for 24 to 28 weeks of gestation until delivery. According to oral drugs, they were divided into tenofovir (TDF) group or telbivudine (LDT) group, non-intervention group (HBsAg and HBeAg positive), HBV DNA positive pregnant women, pregnant women with no anti-HBV drugs, non-hepatitis B pregnant women (normal pregnant women without HBV infection). Infants and young children born to the three groups of women were immunized with the national viral hepatitis B action plan. The gestational weeks and Apgar scores at birth, delivery mode, feeding mode, sex and 7-months-old age were observed and counted. Serum hepatitis B markers (HBVM) and HBV DNA were quantitatively detected. HBVM was detected by time-resolved fluorescence immunoassay (TRFIA), and HBV DNA was detected by real-time PCR (FQ-PCR). The changes of liver parameters, HBsAg, HBeAg, HBV DNA, adverse drug reactions and treatment response of pregnant intervention group before medication (12-24 weeks of gestation), 4 weeks of medication (28-32 weeks of gestation), 36-40 weeks of gestation (36-40 weeks of gestation) were statistically calculated. A t-test was used to compare the data between the measurements. Data measurements within the groups were analyzed using rank -sum test. Results: In the intervention group, therapeutic medications showed no differences in demographic and clinical characteristics between TDF group and LDT group, including liver parameters, HBsAg, HBeAg and log10HBV DNA level. Compared with pre-treatment (TDF group: 4.84 ± 2.01; LDT group: 5.08 ± 1.99), TDF and LDT were significantly lower at the end of pregnancy (TDF group: 3.06 ± 0.66; LDT group: 3.51 ± 1.20). P < 0.05); and the treatment response rate was 100%. There were no serious adverse events in the intervention group. Infants and young children (7-months-old) in the intervention group had negative HBsAg, HBeAg and HBV DNA. The mother-to-child transmission rate of HBV was zero, with blocking rate of 100%. In addition, both infants and young children had different degrees of hepatitis B protective antibodies (anti-HBs, M: 144.33), and their antibody titers were higher than that of non-intervention group (anti-HBs, M: 65.91) and non-hepatitis B pregnant women (anti-HBs, M: 58.43). The difference was statistically significant (P < 0.05), and there was no significant correlation between the use of antiviral and the way of delivery and feeding. Outcomes of mother-to-child transmission of HBV infection in infants and young children (7-months-old) delivered by three groups of pregnant women in the non-intervention groups had 20.0% (15/75)/ 17.3% (13/75) HBsAg/HBeAg positivity rate, and 17.3% (13/75) HBV DNA positivity rate. Overall, mother-to-child transmission rate of HBV infection was 20% (15/75). Furthermore, the relationship between mother's HBV DNA load and infant HBV infection in the non-intervention group showed mother's HBV DNA ≥10⁶ IU/ml. Conclusion In the non-intervention group, mother-to-child transmission of HBV occurred, and infected mothers HBV DNA was ≥106 IU/ml before delivery. This suggests that HBeAg positive and high load HBV DNA replication were independent risk factors for mother-to-child transmission of hepatitis B. Therefore, prenatal drug intervention and postpartum standard immune blockade are necessary for high-risk pregnant women with hepatitis B to achieve zero mother-to-child transmission of hepatitis B in real- clinical practice.