Advancements in artificial intelligence for pelvic floor ultrasound analysis.

Pelvic floor ultrasound provides a clear depiction of pelvic floor structures and their spatial anatomical relationships, enabling enhanced observation of pelvic organ function and position. The integration of artificial intelligence (AI) into medical imaging has revolutionized the automatic analysis of imaging data, offering efficient and accurate preprocessing and analysis. This technological advance addresses challenges associated with traditional pelvic floor ultrasound, such as reliance on operator's experience, time-intensive manual measurements, and significant potential for human error. Current AI applications in pelvic floor ultrasound encompass automatic measurement of the angle of progress (AOP), automatic segmentation of the levator hiatus (LH), and automatic identification of the levator ani muscle (LAM). AI excels in mimicking human analysis, distilling patterns from reorganized data. This paper, grounded in a comprehensive literature review, outlines the principal aspects of pelvic floor ultrasound and its augmentation through AI, highlighting the application value and progress of AI in this field.

Comprehensive assessment of health and ecological risk of cadmium in agricultural soils across China: A tiered framework.

Soil cadmium (Cd) contamination has been increasingly serious in agricultural land across China, posing unexpected risks to human health concerning crop safety and terrestrial ecosystems. This study collected Cd concentration data from 3388 soil sites in agricultural regions. To assess the Cd risk to crop safety, a comprehensive sampling investigation was performed to develop reliable Soil Plant Transfer (SPT) model. Eco-toxicity tests with representative soils and organism was conducted to construct the Species Sensitivity Distribution (SSD) for ecological risk assessment. Then, a tiered framework was applied based on Accumulation index, deterministic method (Hazard quotient), and probabilistic assessment (Monte Carlo and Joint Probability Curve). The results revealed the widespread Cd enrichment in agricultural soils, mainly concentrated in Central, Southern, and Southwest China. Risk assessments demonstrated the greater risks related to crop safety, while the ecological risks posed by soil Cd were manageable. Notably, agricultural soils in southern regions of China exhibited more severe risks to both crop safety and soil ecosystem, compared to other agricultural regions. Furthermore, tiered methodology proposed here, can be adapted to other trace elements with potential risks to crop safety and terrestrial ecosystem.

Identifying multiple soil pollutions of potentially contaminated sites based on multi-gate mixture-of-experts network.

With the rapid increase in the amount and sources of big data, using big data and machine learning methods to identify site soil pollution has become a research hotspot. However, previous studies that used basic information of sites as pollution identification indexes mainly have problems of low accuracy and efficiency when conducting complex model predictions for multiple soil pollution types. In this study, we collected the environmental data of 199 sites in 6 typical industries involving heavy metal and organic pollution. After feature fusion and selection, 10 indexes based on pollution sources and pathways were used to establish the soil pollution identification index system. The Multi-gate Mixture-of-Experts network (MMoE) were constructed to carry out the multi-tasks of soil heavy metals, VOCs and SVOCs pollution identification simultaneously. The SHAP framework was used to reveal the importance of pollution identification indexes on the multiple outputs of MMoE and obtain their driving factors. The results showed that the accuracies of MMoE model were 0.600, 0.783 and 0.850 for soil heavy metals, VOCs and SVOCs pollution identifications, respectively, which were 0-20 % higher than their accuracies of BP neural networks of single tasks. The indexes of raw material containing organic compounds, enterprise scale, soil pollution traces and industry types have the different significant importance on site soil pollutions. This study proposed a more efficient and accurate method to identify site soil pollutions and their driving factors, which offers a step towards realizing intelligent identification and risk control of site soil pollution globally.

LINC01088/miR-22/CDC6 Axis Regulates Prostate Cancer Progression by Activating the PI3K/AKT Pathway.

Background: Prostate cancer (PCa) harms the male reproductive system, and lncRNA may play an important role in it. Here, we report that the LINC01088/microRNA- (miRNA/miR-) 22/cell division cycle 6 (CDC6) axis regulated through the phosphatidylinositide 3-kinases- (PI3K-) protein kinase B (AKT) signaling pathway controls the development of PCa. Methods: lncRNA/miRNA/mRNA associated with PCa was downloaded and analyzed by Gene Expression Omnibus. The expression and correlation of LINC01088/miR-22/CDC6 in PCa were analyzed and verified by RT-qPCR. Dual-luciferase was used to analyze the binding between miR-22 and LINC01088 or CDC6. Cell Counting Kit-8 and Transwell were used to analyze the effects of LINC01088/miR-22/CDC6 interactions on PCa cell viability or migration/invasion ability. Localization of LINC01088 in cells was analyzed by nuclear cytoplasmic separation. The effect of LINC01088/miR-22/CDC6 interaction on downstream PI3K/AKT signaling was analyzed by Western blot. Results: LINC01088 or CDC6 was upregulated in prostate tumor tissues or cells, whereas miR-22 was downregulated, miR-22 directly targets both LINC01088 and CDC6. si-LINC01088 inhibits the PCa process by suppressing the PI3K/AKT pathway. CDC6 reverses si-linc01088-mediated cell growth inhibition and reduction of PI3K and AKT protein levels. Conclusion: Our results demonstrate that the LINC01088/miR-22/CDC6 axis functions in PCa progression and provide a promising diagnostic and therapeutic target.

Feature fusion improves performance and interpretability of machine learning models in identifying soil pollution of potentially contaminated sites.

Owing to the rapid development of big data technology, use of machine learning methods to identify soil pollution of potentially contaminated sites (PCS) at regional scales and in different industries has become a research hot spot. However, due to the difficulty in obtaining key indexes of site pollution sources and pathways, current methods have problems such as low accuracy of model predictions and insufficient scientific basis. In this study, we collected the environmental data of 199 PCS in 6 typical industries involving heavy metal and organic pollution. Then, 21 indexes based on basic information, potential for pollution from product and raw material, pollution control level, and migration capacity of soil pollutants were used to established the soil pollution identification index system. We fused the original indexes into the new feature subset with 11 indexes through the method of consolidation calculation. The new feature subset was then used to train machine learning models of random forest (RF), support vector machine (SVM), and multilayer perceptron (MLP), and tested to determine whether it improved the accuracy and precision of soil pollination identification models. The results of correlation analysis showed that the four new indexes created by feature fusion have the correlation with soil pollution is similar to the original indexes. The accuracies and precisions of three machine learning models trained on the new feature subset were 67.4%- 72.9% and 72.0%- 74.7%, which were 2.1%- 2.5% and 0.3%- 5.7% higher than these of the models trained on original indexes, respectively. When the PCS were divided into typical heavy metal and organic pollution sites according to the enterprise industries, the accuracy of the model trained on the two datasets for identifying soil heavy metal and organic pollution were significantly improve to approximately 80%. Owing to the imbalance in positive and negative samples in the prediction of soil organic pollution, the precisions of soil organic pollution identification models were 58%- 72.5%, which were significantly lower than their accuracies. According to the factors analysis based on the model interpretability of SHAP, most of the indexes of basic information, potential for pollution from product and raw material, and pollution control level had different degrees of impact on soil pollution. However, the indexes of migration capacity of soil pollutants had the least effect in the classification task of soil pollution identification of PCS. Among the indexes, traces of soil pollution, industrial utilization years/start-up time, pollution control risk scores and enterprise scale having the greatest effects on soil pollution with the mean SHAP values of 0.17-0.36, which reflected their contribution rate on soil pollution and could help to optimize the current index scoring of the technical regulation for identifying site soil pollution. This study provides a new technical method to identify soil pollution based on big data and machine learning methods, in addition to providing a reference and scientific basis for environmental management and soil pollution control of PCS.

Cation-free siRNA-cored nanocapsules for tumor-targeted RNAi therapy.

Cation-associated cytotoxicity limits the systemic administration of RNA delivery in vivo, demanding the development of non-cationic nanosystems. In this study, cation-free polymer-siRNA nanocapsules with disulfide-crosslinked interlayer, namely T-SS(-), were prepared via the following steps: 1) complexation of siRNA with a cationic block polymer cRGD-poly(ethylene glycol)-b-poly[(2-aminoethanethiol)aspartamide]-b-poly{N'-[N-(2-aminoethyl)-2-ethylimino-1-aminomethyl]aspartamide}, abbreviated as cRGD-PEG-PAsp(MEA)-PAsp(C=N-DETA), 2) interlayer crosslinking via disulfide bond in pH 7.4 solution, and 3) removal of cationic DETA pendant at pH 5.0 via breakage of imide bond. The cationic-free nanocapsules with siRNA cores not only showed great performance (such as efficient siRNA encapsulation, high stability in serum, cancer cell targeting via cRGD modification, and GSH-triggered siRNA release), but also achieved tumor-targeted gene silencing in vivo. Moreover, the nanocapsules loaded with siRNA against polo-like kinase 1 (siRNA-PLK1) significantly inhibited tumor growth without showing cation-associated toxicity side effects and remarkably improved the survival rate of PC-3 tumor-bearing mice. The cation-free nanocapsules could potentially serve as a safe and effective platform for siRNA delivery. STATEMENT OF SIGNIFICANCE: Cation-associated toxicity limits the clinical translation of cationic carriers for siRNA delivery. Recently, several non-cationic carriers, such as siRNA micelles, DNA-based nanogels, and bottlebrush-architectured poly(ethylene glycol), have been developed to deliver siRNA. However, in these designs, siRNA as a hydrophilic macromolecule was attached to the nanoparticle surface instead of being encapsulated. Thus, it was easily degraded by serum nuclease and often induced immunogenicity. Herein, we demonstrate a new type of cation-free siRNA-cored polymeric nanocapsules. The developed nanocapsules not only showed capacities including efficient siRNA encapsulation, high stability in serum, and cancer cell targeting via cRGD modification, but also achieved an efficient tumor-targeted gene silencing in vivo. Importantly, unlike cationic carriers, the nanocapsules exhibited no cation-associated side effects.

Prediction of Prognosis in Patients with Hepatocellular Carcinoma Based on Molecular Subtypes of Immune Genes.

For those patients with hepatocellular carcinoma (HCC), it is really a heavy burden. Herein, the immune genes of HCC were analyzed in groups to determine prognostic biomarkers related to immune genes in HCC. The mRNA data, clinical data in TCGA-LIHC dataset, and immune gene in the ImmPort database were collected for the combining usage with K-means concordance clustering to cluster HCC patients according to the immune gene matrix. Based on ssGSEA analysis result, HCC patients were sorted into high- and low-immune subtypes, and survival curve presented that patients in high-immune subtypes had a better prognosis. Subsequently, differential expression analysis was performed to obtain immune-related differentially expressed genes (IRGs). Cox and lasso analyses were performed for obtaining five optimal immune genes related to prognosis, and a risk assessment model was then established. Patient samples in the training and validation sets were, respectively, divided into high- and low-risk groups. K-M survival curves presented a better prognosis of patients in the low-risk group than in the high-risk group. The ROC curve indicated that this model was finely used for the prediction of prognosis. In addition, immune infiltration assessment revealed that NR0B1 and FGF9 had potential to impact the tumor immune microenvironment. Finally, using qRT-PCR and transwell assays, it was demonstrated that the macrophage chemotaxis was enhanced when NR0B1 and FGF9 were highly expressed in HCC cells. In general, a 5-gene prognostic risk assessment model was constructed based on immune genes and bioinformatics analysis methods, which provides some reference for the prognosis of HCC as well as immunotherapy.

Derivation of Soil Criteria of Cadmium for Safe Rice Production Applying Soil-Plant Transfer Model and Species Sensitivity Distribution.

Widespread soil contamination is hazardous to agricultural products, posing harmful effects on human health through the food chain. In China, Cadmium (Cd) is the primary contaminant in soils and easily accumulates in rice, the main food for the Chinese population. Therefore, it is essential to derive soil criteria to safeguard rice products by assessing Cd intake risk through the soil-grain-human pathway. Based on a 2-year field investigation, a total of 328 soil-rice grain paired samples were collected in China, covering a wide variation in soil Cd concentrations and physicochemical properties. Two probabilistic methods used to derive soil criteria are soil-plant transfer models (SPT), with predictive intervals, and species sensitivity distribution (SSD), composed of soil type-specific bioconcentration factor (BCF, Cd concentration ratio in rice grain to soil). The soil criteria were back-calculated from the Chinese food quality standard. The results suggested that field data with a proper Cd concentration gradient could increase the model accuracy in the soil-plant transfer system. The derived soil criteria based on soil pH were 0.06-0.11, 0.33-0.59, and 1.51-2.82 mg kg-1 for protecting 95%, 50% and 5% of the rice safety, respectively. The soil criteria with soil pH further validated the soil as being safe for rice grains.

Immune Analysis and Small Molecule Drug Prediction of Hepatocellular Carcinoma Based on Single Sample Gene Set Enrichment Analysis.

Though patients with hepatocellular carcinoma (HCC) benefit from the treatment of immune checkpoint inhibitor (ICB), it is still of vital significance to develop more effective drugs and predict patients' response to ICB therapy. Herein, we utilized single sample gene set enrichment analysis (ssGSEA) to score the downloaded tumor samples from TCGA-LIHC based on 29 immune gene sets, thus reflecting the immunologic competence of samples. Then samples were classified into high, moderate, and low immunity groups. Additionally, we utilized survival analysis and ESTIMATE score to verify the reliability of the immunity grouping. We then performed differential expression analysis on the samples in these two groups and obtained 716 differentially expressed genes (DEGs). Next, the DEGs mentioned above were subjected to GO and KEGG analyses. The outcomes demonstrated that these DEGs were mostly correlated with the immune-related biological functions. To further verify biological processes in which DEGs might be involved, we constructed a protein-protein interaction network. Afterward, we used MCODE plugin to conduct subnetwork analysis. Thereafter, KEGG enrichment analysis was performed on two genes with the highest score in the subnetwork. The results exhibited that these genes were gathered in pathways such as Th1 and Th2 cell differentiation and NF-κB. Finally, we utilized Connectivity Map to find possible drugs for the treatment of HCC and obtained complex methyl-angolensate. The above results may contribute to distinguishing HCC patients who are eligible for immunotherapy and providing the foundations for the development of therapeutic drugs for HCC.

Overexpression of chaperonin containing TCP1 subunit 7 has diagnostic and prognostic value for hepatocellular carcinoma.

Chaperonin containing TCP1 subunit 7 (CCT7) regulates the expression of many tumor-related proteins. We investigated the diagnostic and prognostic value of CCT7 expression for hepatocellular carcinoma (HCC). In datasets from The Cancer Genome Atlas and the Gene Expression Omnibus, CCT7 mRNA levels were greater in HCC tissues than adjacent normal tissues, and these results were validated using immunohistochemistry. In patients with early-stage disease and low alpha-fetoprotein expression, CCT7 expression was still higher in HCC tissues than normal tissues. Receiver operating characteristic curve analyses indicated that CCT7 expression had better diagnostic value than alpha-fetoprotein for HCC patients with early-stage disease and low alpha-fetoprotein expression. The positive predictive value of CCT7 expression was higher than that of alpha-fetoprotein expression. Higher CCT7 mRNA and protein levels were independent risk factors for poorer overall and recurrence-free survival in HCC patients. Greater methylation of the CpG site cg19515186 was associated with better overall survival in HCC patients. Genes co-expressed with CCT7 were upregulated in HCC and associated with poorer overall survival. Gene Ontology, Kyoto Encyclopedia of Genes and Genomes and Gene Set Enrichment Analyses demonstrated that CCT7 expression correlated with spliceosome signaling. These findings demonstrate that CCT7 has diagnostic and prognostic value for HCC.

The diagnostic and prognostic significance of small nuclear ribonucleoprotein Sm D1 aberrantly high expression in hepatocellular carcinoma.

Small nuclear ribonucleoprotein Sm D1 (SNRPD1), one of the crucial genes encoding core spliceosome components, was abnormally highly expressed in multiple types of tumors. In this study, we investigated the diagnostic and prognostic significance of SNRPD1 in hepatocellular carcinoma (HCC). The investigation of datasets from GEO and TCGA databases revealed that SNRPD1 expression in HCC was significantly higher than adjacent normal liver tissues, which was validated by immunohistochemistry (IHC). Both GO, KEGG analysis showed that the SNRPD1 co-expressed genes mainly enriched in Cell division, Nuclear import, mRNA splicing via spliceosome, Ribosome, Cell cycle, etc. Survival analysis from the GSE14520 dataset and 154 HCC cohorts exhibited a significant association of high SNRPD1 expression with poor overall survival and recurrence-free survival. ROC analysis showed that the abnormally high SNRPD1 mRNA expression has diagnostic significance in distinguishing between HCC and normal liver tissue (AUC = 0.819). Gene set enrichment analysis (GSEA) demonstrated that the high expression of SNRPD1 might regulate HCC tumorigenesis and progression by affecting the cell cycle, mismatch repair, DNA replication, and RNA degradation, etc. The luciferase report assay revealed that SNRPD1 was the direct target gene of miR-100 manifested by decreased SNRPD1 expression and luciferase activity in the HCC cells upon miR-100 overexpression. Finally, SNRPD1 may as an oncogene affecting the progression of HCC through regulates the mTOR pathway and autophagy.

Risk factors for recurrent autoimmune liver diseases after liver transplantation: A meta-analysis.

BACKGROUND: Autoimmune liver disease (ALD) is a chronic liver disease caused by immune dysfunction in the body. However, no causative or curative medical treatment with proven efficacy exists to cure ALDs, and liver transplantation (LT) remains the only effective treatment available. However, the problem of recurrence of ALDs (rALDs) still remains after LT, which seriously affects the survival rate of the patients. Therefore, clinicians need to be aware of the risk factors affecting rALDs after LT. Therefore, this meta-analysis aims to define the risk factors for rALDs, which include the recurrence of primary biliary cirrhosis, primary sclerosing cholangitis and autoimmune hepatitis. METHODS: A systematic search in Pubmed, Embase, Cochrane library and Web of Science databases was performed from 1980 to 2019. The inclusion criteria were risk factors for developing rALDs after LT. However, case series, case reports, reviews, meta-analysis and studies only including human immunodeficiency virus cases, children, and pregnant patients were excluded. RESULTS: The electronic database search yielded 1728 results. Sixty-three retrospective cohort studies met the inclusion criteria and 13 were included in the meta-analysis. The final cohort included 5077 patients, and among them, 21.96% developed rALDs. Colectomy before LT, HR 0.59 (95% confidence interval [CI]: 0.37-0.96), cholangiocarcinoma, HR 3.42 (95% CI: 1.88-6.21), multiple episodes of acute cellular rejection, HR 2.07 (95% CI: 1.27-3.37), model for end-stage liver disease score, HR 1.05 (95% CI: 1.02-1.08), use of mycophenolate mofetil, HR 1.46 (95% CI: 1.00-2.12) and the use of cyclosporin A, HR 0.69 (95% CI: 0.49-0.97) were associated with the risk of rprimary sclerosing cholangitis. In addition, the use of tacrolimus, HR 1.73 (95% CI: 1.00-2.99) and cyclosporin A, HR 0.59 (95% CI: 0.39-0.88) were associated with the risk of rALD. CONCLUSIONS: Multiple risk factors for rALDs were identified, such as colectomy before LT, cholangiocacinoma, multiple episodes of acute cellular rejection, model for end-stage liver disease score, and especially the use of mycophenolate mofetil, cyclosporin A and tacrolimus.

Hybrid minigene splicing assay verifies the pathogenicity of a novel splice site variant in the COL1A1 gene of a chinese patient with osteogenesis imperfecta type I.

BACKGROUND: Osteogenesis imperfecta (OI) is a rare genetic bone disease associated with brittle bones and fractures. Among all known types, OI type I is the most common type and characterized by increased bone fragility, low bone mass, distinctly blue-gray sclera, and susceptibility to conductive hearing loss beginning in adolescence. Mutations in genes encoding type I collagen (COL1A1 and COL1A2) contribute to the main pathogenic mechanism of OI. METHODS: Subtle mutation of the COL1A1 gene in the proband was detected by targeted next-generation sequencing (NGS) and confirmed by Sanger sequencing. We then assessed the effect of the mutation on the splicing of the COL1A1 gene by bioinformatics prediction and hybrid minigene splicing assay (HMSA). RESULTS: A novel splice site mutation c.1821+1 G>C was discovered in the proband by NGS and further confirmed by Sanger sequencing, which was also simultaneously identified from the proband's mother and elder sister. Bioinformatics predicted that this mutation would result in a disappearance of the 5' donor splice site in intron 26, thereby leading to abnormal splicing and generation of premature stop codon. The follow-up experimental data generated by HMSA was consistent with this prediction. CONCLUSION: Our study identified a novel splice site mutation that caused OI type I in the proband by abnormal splicing and demonstrated that combined applications of NGS, bioinformatics and HMSA are comprehensive and effective methods for diagnosis and aberrant splicing study of OI.

Taurine enhances mouse cochlear neural stem cell transplantation via the cochlear lateral wall for replacement of degenerated spiral ganglion neurons via sonic hedgehog signaling pathway.

The aim of this paper is to investigate the potential beneficial effects of taurine in cochlear neural stem cell (NSC) transplantation and elucidate the underlying molecular mechanism. The NSC cells were isolated from neonatal Balb/c mice and an auditory neuropathy gerbil model was established by microinjection of ouabain. The spiral ganglion neurons (SGN) were characterized with immunofluorescence stained with Tuj1 antibody. Cell proliferation was determined by BrdU incorporation assay and the morphologic index was measured under the light microscope. The relative protein level was determined by immunoblotting. The hearing of the animal model was scored by click- and tone burst-evoked auditory brainstem response (ABR). Here we consolidated our previous finding that taurine stimulated SGN density and the proliferation index, which were completely abolished by Shh inhibitor, cyclopamine. Transplantation of cochlear NSCs combined with taurine significantly improved ouabain-induced auditory neuropathy in gerbils. In addition, cyclopamine antagonized taurine's effect on glutamatergic and GABAergic neuron population via suppression of VGLUT1 and GAT1 expression. Mechanistically, taurine evidently activated the Sonic HedgeHog pathway and upregulated Shh, Ptc-1, Smo and Gli-1 proteins, which were specifically blockaded by cyclopamine. Here, for the first time demonstrated we that co-administration with taurine significantly improved NSC transplantation and the Shh pathway was identified in this beneficial effect.

The clinical application of preimplantation genetic diagnosis for X-linked retinitis pigmentosa.

OBJECTIVE: To investigate the usefulness of preimplantation genetic diagnosis (PGD) based on mutated allele revealed by sequencing with aneuploidy and linkage analyses (MARSALA) for a pedigree with X-linked retinitis pigmentosa (XLRP). METHODS: One pathogenic mutation (c.494G > A) of the retinitis pigmentosa GTPase regulator (RPGR) gene was identified in a pedigree affected by XLRP. Then, PGD was carried out for the couple, of which the wife was an XLRP carrier. Three blastocysts were biopsied and then MARSALA was performed by next-generation sequencing (NGS). Prenatal diagnosis was also carried out to confirm the PGD results. RESULTS: Three blastocysts were all unaffected. Then, one of the embryos was chosen randomly to be transferred, and the pregnancy was acquired successfully. The results of prenatal diagnosis were consistent with the PGD results. The fetus did not carry RPGR mutation (c.494G > A) and had normal chromosome karyotype. As a result, a healthy baby free of XLRP condition was born. CONCLUSION: The PGD method based on MARSALA was established and applied to a family with XLRP successfully. MARSALA will be a valid tool, not only for XLRP families but also for families affected with other monogenetic disorders, to prevent transmission of the genetic disease from parents to offspring.

Taurine enhances mouse cochlear neural stem cells proliferation and differentiation to sprial gangli through activating sonic hedgehog signaling pathway.

To investigate the molecular mechanism underlying taurine-stimulated proliferation and differentiation of cochlear neural stem cells (NSCs) and potential involvement of Sonic Hedgehog (Shh) pathway. The NSCs were characterized with immunofluorescence stained with nestin antibody. Cell viability was determined by MTT assay. The relative proliferation was measured by BrdU incorporation assay. The morphologic index was measured under light microscope. The relative protein level was determined by immunoblotting. Here we presented our findings that taurine stimulated proliferation and neurite outgrowth of NSCs, which was completely abolished by Shh inhibitor cyclopamine. In addition, cyclopamine antagonized taurine's effect on glutamatergic and GABAergic neuron population via suppressing expressions of Ptc-1, Smo and Gli-1. Our data supported the critical role of Shh pathway underlying the protective effect of taurine on auditory neural system.