Advantages and disadvantages of various hydrogel scaffold types: A research to improve the clinical conversion rate of loaded MSCs-Exos hydrogel scaffolds.

Mesenchymal stem cell-derived exosomes(MSCs-Exos) offer promising therapeutic potential for a wide range of tissues and organs such as bone/cartilage, nerves, skin, fat, and endocrine organs. In comparison to the application of mesenchymal stem cells (MSCs), MSCs-Exos address critical challenges related to rejection reactions and ethical concerns, positioning themselves as a promising cell-free therapy. As exosomes are extracellular vesicles, their effective delivery necessitates the use of carriers. Consequently, the selection of hydrogel materials as scaffolds for exosome delivery has become a focal point of contemporary research. The diversity of hydrogel scaffolds, which can take various forms such as injectable types, dressings, microneedles, and capsules, leads to differing choices among researchers for treating diseases within the same domain. This variability in hydrogel materials poses challenges for the translation of findings into clinical practice. The review highlights the potential of hydrogel-loaded exosomes in different fields and introduces the advantages and disadvantages of different forms of hydrogel applications. It aims to provide a multifunctional and highly recognized hydrogel scaffold option for tissue regeneration at specific sites, improve clinical translation efficiency, and benefit the majority of patients.

A feasibility study on utilizing machine learning technology to reduce the costs of gastric cancer screening in Taizhou, China.

Aim: To optimize gastric cancer screening score and reduce screening costs using machine learning models. Methods: This study included 228,634 patients from the Taizhou Gastric Cancer Screening Program. We used three machine learning models to optimize Li's gastric cancer screening score: Gradient Boosting Machine (GBM), Distributed Random Forest (DRF), and Deep Learning (DL). The performance of the binary classification models was evaluated using the area under the curve (AUC) and area under the precision-recall curve (AUCPR). Results: In the binary classification model used to distinguish low-risk and moderate- to high-risk patients, the AUC in the GBM, DRF, and DL full models were 0.9994, 0.9982, and 0.9974, respectively, and the AUCPR was 0.9982, 0.9949, and 0.9918, respectively. Excluding Helicobacter pylori IgG antibody, pepsinogen I, and pepsinogen II, the AUC in the GBM, DRF, and DL models were 0.9932, 0.9879, and 0.9900, respectively, and the AUCPR was 0.9835, 0.9716, and 0.9752, respectively. Remodel after removing variables IgG, PGI, PGII, and G-17, the AUC in GBM, DRF, and DL was 0.8524, 0.8482, 0.8477, and AUCPR was 0.6068, 0.6008, and 0.5890, respectively. When constructing a tri-classification model, we discovered that none of the three machine learning models could effectively distinguish between patients at intermediate and high risk for gastric cancer (F1 scores in the GBM model for the low, medium and high risk: 0.9750, 0.9193, 0.5334, respectively; F1 scores in the DRF model for low, medium, and high risks: 0.9888, 0.9479, 0.6694, respectively; F1 scores in the DL model for low, medium, and high risks: 0.9812, 0.9216, 0.6394, respectively). Conclusion: We concluded that gastric cancer screening indicators could be optimized when distinguishing low-risk and moderate to high-risk populations, and detecting gastrin-17 alone can achieve a good discriminative effect, thus saving huge expenditures.

An Integrated Nanoplatform via Dual Channel Excitation for Both Precise Fluorescence Imaging and Photodynamic Therapy of Orthotopic Breast Tumor in NIR-II Region.

Although research on photodynamic therapy (PDT) of malignant tumor has made considerable progress in recent years, it is a remaining challenge to extend PDT to the second near-infrared window (NIR-II) along with real-time and accurate NIR-II fluorescence imaging to determine drug enrichment status and achieve high treatment efficacy. In this work, lanthanide nanoparticles (Ln NPs)-based nanoplatform (LCR) equipped with photosensitizer Chlorin e6 (Ce6) and targeting molecular NH2-PEG1000-cRGDfK are developed, which can achieve NIR-II photodynamic therapy (PDT) and NIR-II fluorescence imaging by dual channel excitation. Under 808 nm excitation, Nd3+ in the outer layer can absorb the energy and transfer inward to emit strong NIR-II emissions (1064 and 1525 nm). Due to the low background noise of NIR-II light and the targeting effect of NH2-PEG1000-cRGDfK, LCR can recognize tiny tumor tissue (≈3 mm) and monitor drug distribution in vivo. Under 1530 nm excitation, internal Er3+ can be self-sensitized, generating intense upconversion emission (662 nm) that can effectively activate Ce6 for in vivo PDT due to the deep tissue penetration of NIR-II light. This study provides a paradigm of theranostic nanoplatform for both real-time fluorescence imaging and PDT of orthotopic breast tumor in NIR-II window.

High-Resolution Haplotyping of the PAH Gene Enables Early Gestation Noninvasive Prenatal Diagnosis of Phenylketonuria and Evolution Analysis of Recurrent Pathogenic Variations.

BACKGROUND: The clinical performance of RHDO-based NIPD for PKU during early gestation remains under-evaluated. Furthermore, studies focused on SNP loci obtained by next-generation sequencing to analyze the genetic evolution of pathogenic variations in PKU is limited. METHODS: Maternal peripheral blood, along with proband and paternal samples, was collected between 7 and 12 weeks of gestation. The PAH gene and surrounding high heterozygosity SNPs were targeted for enrichment and sequencing. Fetal genotypes were inferred using RHDO-based NIPD. High-resolution PAH haplotypes were used for the analysis of two common pathogenic variants in the Chinese population: c.728G>A and c.1238G>C. RESULTS: Sixty one PKU families participated with an average fetal fraction of 6.08%. The median gestational age was 8+6 weeks. RHDO-based NIPD successfully identified fetal genotypes in 59 cases (96.72%, 59/62). Two cases failed because of insufficient informative SNPs. In addition, a recombination event was assessed in one fetus of 59 cases. Six, and three haplotypes were identified for c.728G>A(p.Arg243Gln) and c.1238G>C(p.Arg413Pro), respectively. Hap_3 and hap_8 were identified as the ancestral haplotypes for these pathogenic variants, with other haplotypes arising from mutations or recombination based on these ancestral haplotypes. CONCLUSIONS: This study validates the feasibility of an RHDO-based assay for NIPD of PKU in early pregnancy and introduces its application in the demonstration of founder effects in recurrent pathogenic variations, offering new insights into the evolutionary analysis of PAH variations.

Photorespiratory glycine contributes to photosynthetic induction during low to high light transition.

Leaves experience near-constant light fluctuations daily. Past studies have identified many limiting factors of slow photosynthetic induction when leaves transition from low light to high light. However, the contribution of photorespiration in influencing photosynthesis during transient light conditions is largely unknown. This study employs dynamic measurements of gas exchange and metabolic responses to examine the contribution of photorespiration in constraining net rates of carbon assimilation during light induction. This work indicates that photorespiratory glycine accumulation during the early light induction contributes 5-7% to the additional carbon fixed relative to the low light conditions. Mutants with large glycine pools under photorespiratory conditions (5-formyl THF cycloligase and hydroxypyruvate reductase 1) showed a transient spike in net CO2 assimilation during light induction, with glycine buildup accounting for 22-36% of the extra carbon assimilated. Interestingly, levels of many C3 cycle intermediates remained relatively constant in both mutants and wild-type throughout the light induction period where glycine accumulated, indicating that recycling of carbon into the C3 cycle via photorespiration is not needed to maintain C3 cycle activity under transient conditions. Furthermore, our data show that oxygen transient experiments can be used as a proxy to identify the photorespiratory component of light-induced photosynthetic changes.

In situ engineered magnesium alloy implant for preventing postsurgical tumor recurrence.

Invasive tumors are difficult to be completely resected in clinical surgery due to the lack of clear resection margins, which greatly increases the risk of postoperative recurrence. However, chemotherapy and radiotherapy as the traditional means of postoperative adjuvant therapy, are limited in postoperative applications, such as multi-drug resistance and low sensitivity, etc. Therefore, an engineered magnesium alloy rod is designed as a postoperative implant to completely remove postoperative residual tumor tissue and inhibit tumor recurrence by gas and mild magnetic hyperthermia therapy (MMHT). As a reactive metal, magnesium alloy responds to the acidic tumor microenvironment by continuously generating hydrogen. The in-situ generation of hydrogen not only protects the surrounding normal tissue, but also enables the magnesium alloy to achieve MMHT under low-intensity alternating magnetic field (AMF). Furthermore, the numerous reactive oxygen species (ROS) produced by heat stress will combine with nitric oxide (NO) generated in situ, to produce more toxic reactive nitrogen species (RNS) storm. In summary, engineered magnesium alloy can completely remove residual tumor tissue and inhibit tumor recurrence by MMHT and RNS storm under low-intensity AMF, and the biodegradability of magnesium alloy makes great potential for clinical application.

Comparison of robotic-assisted total knee arthroplasty: an updated systematic review and meta-analysis.

This study was conducted to compare the changes in different clinical scores and imaging indexes of patients who underwent robot-assisted total knee arthroplasty (RA-TKA) and manual total knee arthroplasty (M-TKA). PubMed, Web of Science, Cochrane Library and Embase were searched according to PRISMA guidelines in June 2024. Search terms included "robot-assisted", "manual" and "total knee arthroplasty". Outcome indicators included American Knee Society Score (KSS), Western Ontario McMaster Universities Osteoarthritis Index (WOMAC), Oxford Knee Score (OKS), range of motion (ROM), Hospital for Special Surgery (HSS) score, Forgotten Joint Score (FJS), 36-Item Short Form Health Survey (SF-36), operation duration (min), intraoperative blood loss (ml), pain score, patient's satisfaction scores, hip-knee-ankle (HKA) angle, frontal femoral component angle, frontal tibia component angle, lateral femoral component angle and lateral tibia component angle. A total of 1,033 articles were obtained after removing duplicates, and 12 studies involving 2,863 patients (1,449 RA-TKAs and 1,414 M-TKAs) were finally meta-analyzed (22-32). The baseline data of both groups were similar in all results. Meta-analysis suggested a better performance of the RA-TKA group than the M-TKA group regarding the HKA angle. The manual TKA reduced the operation time and significantly improved the range of motion. The results of > 6 months follow-up showed that M-TKA was better than RA-TKA in terms of KSS score and WOMAC. Compared with M-TKA, RA-TKA can produce more accurate prosthetic alignment, but it does not lead to better clinical results. Orthopedic surgeons should choose between two surgical procedures according to their own experience and patients' characteristics.

Qualitative study on the perception of good death in patients with end-stage cancer in oncology nurses.

OBJECTIVE: To explore the perception of good death of patients with end-stage cancer by nurses in the oncology department. METHOD: In the study we used a phenomenological approach and semi-structured interviews. A total of 11 nurses from the oncology department of a Grade A hospital in Taizhou were interviewed on the cognition of good death from July 1 to September 30, 2022. Colaizzi's analysis method was used to analyse the interview data. This study followed the consolidated criteria for reporting qualitative research (COREQ). RESULT: Four themes were identified: a strong sense of responsibility and mission; To sustain hope and faith; The important role of family members; Improve patients' quality of life. CONCLUSION: The nurses in the department of oncology have a low level of knowledge about the "good death", and the correct understanding and view of the "good death" is the premise of the realization of " good death". The ability of nursing staff to improve the "good death", attention, and meet the needs and wishes of individuals and families, is the guarantee of the realization of "good death".

Using Dynamic Gas Exchange Measurements During Oxygen Transients to Study Nonsteady-State Photorespiration.

Leaf-level gas exchange is widely used to investigate the largest carbon fluxes in illuminated leaves, offering a nondestructive way to investigate the impact of photorespiration on plant carbon balance. Modern commercial gas exchange systems allow high temporal resolution measurements under changing environments, aiding the development of nonsteady-state approaches for measuring dynamic photosynthetic responses. Here, we describe a nonsteady-state technique for acquiring the dynamic response of net CO2 assimilation to changes in photorespiratory fluxes manipulated by O2 mole fractions. This technique allows for the screening of plant genotypes with variations in their efficiencies of photorespiration under nonsteady-state conditions.

Combining Gas Exchange and Rapid Quenching of Leaf Tissue for Mass Spectrometry Analysis Directly in Gas Exchange Cuvette.

Isotopically nonstationary metabolic flux analysis (INST-MFA) is a powerful technique for studying plant central metabolism, which involves introducing a 13CO2 tracer to plant leaves and sampling the labeled metabolic intermediates during the transient period before reaching an isotopic steady state. The metabolic intermediates involved in the C3 cycle have exceptionally fast turnover rates, with some intermediates turning over many times a second. As a result, it is necessary to rapidly introduce the label and then rapidly quench the plant tissue to determine concentrations in the light or capture the labeling kinetics of these intermediates at early labeling time points. Here, we describe a rapid quenching (0.1-0.5 s) system for 13CO2 labeling experiments in plant leaves to minimize metabolic changes during labeling and quenching experiments. This system is integrated into a commercially available gas exchange analyzer to measure initial rates of gas exchange, precisely control ambient conditions, and monitor the conversion from 12CO2 to 13CO2.

An eco-friendly and high-yield extraction of rare earth from the leaching solution of ion adsorbed minerals.

Ion-adsorbed rare earth minerals are rich in medium and heavy rare earth (RE), which are important strategic resources. In this article, a novel approach for the extraction of RE from ion adsorbed minerals was developed. Through a comprehensive assessment of their extraction and separation performance, the hydrophobic deep eutectic solvents (HDES) with a composition of trioctylphosphine oxide (TOPO): dodecanol (LA): 2-thiophenoyltrifluoroacetone (HTTA) = 1:1:1 was determined as the optimal configuration. Under optimized conditions, only RE were extracted by the HDES, while Al, Ca, Mg were not extracted at all. The HDES based extraction obviated the need for diluent such as kerosene, eliminating the generation of impurity removal residues. The RE in the stripping solution could be successfully enriched by saponified lauric acid, achieving an impressive precipitation rate of 99.7%. The RE precipitate underwent further enrichment, resulting in a RE concentration of 176 g/L (REO = 210 g/L). Unlike industrial precipitants such as oxalic acid and ammonium bicarbonate, lauric acid can be effectively recycled, thereby avoiding a large amount of wastewater and carbon dioxide emissions. The obtained RE solution product exhibits high yield and purity, this study provides an eco-friendly and high-yield approach for extracting RE.

Noninvasive twin genotyping for recessive monogenic disorders by relative haplotype dosage.

OBJECTIVE: To establish a haplotype-based noninvasive prenatal testing (NIPT) workflow for single-gene recessive disorders that adapt to dizygotic (DZ) twin pregnancies. METHOD: Twin pregnancies at risk of Duchenne muscular dystrophy, Becker muscular dystrophy, hemophilia B, spinal muscular atrophy, phenylketonuria, and nonsyndromic hearing loss were recruited. For subsequent analysis, capture sequencing targeting highly heterozygotic single nucleotide polymorphism sites was conducted. Paternal-specific alleles were used to calculate the total and individual fetal fractions and determine zygosity. A two-step Bayes Factor model was applied to clarify the complex genomic landscape in the maternal plasma: the first step involved determining whether the twins inherited the same haplotype, and the second step involved estimating their individual genotypes. NIPT results were subsequently confirmed by invasive diagnosis. RESULTS: Nine twin pregnancies were recruited, including five DZ and four monozygotic (MZ) twins. The earliest gestational age was 8+0 weeks, and the minimum fetal fraction was 4.6%. Three twin pregnancies were reported with one affected fetus, while the remaining six were reported without affected fetuses. Two dichorionic diamniotic twin pregnancies were confirmed to be MZ twins. The NIPT results were 100% consistent with those of invasive procedures or diagnostic genetic testing after birth. CONCLUSION: This study is the first to perform NIPT for single-gene disorders in twin pregnancies and preliminarily confirm its clinical feasibility. Acknowledging the twins' genotypes in the first trimester is valuable as it empowers obstetric care providers and parents to have adequate time for pregnancy management and decision-making.

Deep learning assists detection of esophageal cancer and precursor lesions in a prospective, randomized controlled study.

Endoscopy is the primary modality for detecting asymptomatic esophageal squamous cell carcinoma (ESCC) and precancerous lesions. Improving detection rate remains challenging. We developed a system based on deep convolutional neural networks (CNNs) for detecting esophageal cancer and precancerous lesions [high-risk esophageal lesions (HrELs)] and validated its efficacy in improving HrEL detection rate in clinical practice (trial registration ChiCTR2100044126 at www.chictr.org.cn). Between April 2021 and March 2022, 3117 patients ≥50 years old were consecutively recruited from Taizhou Hospital, Zhejiang Province, and randomly assigned 1:1 to an experimental group (CNN-assisted endoscopy) or a control group (unassisted endoscopy) based on block randomization. The primary endpoint was the HrEL detection rate. In the intention-to-treat population, the HrEL detection rate [28 of 1556 (1.8%)] was significantly higher in the experimental group than in the control group [14 of 1561 (0.9%), P = 0.029], and the experimental group detection rate was twice that of the control group. Similar findings were observed between the experimental and control groups [28 of 1524 (1.9%) versus 13 of 1534 (0.9%), respectively; P = 0.021]. The system's sensitivity, specificity, and accuracy for detecting HrELs were 89.7, 98.5, and 98.2%, respectively. No adverse events occurred. The proposed system thus improved HrEL detection rate during endoscopy and was safe. Deep learning assistance may enhance early diagnosis and treatment of esophageal cancer and may become a useful tool for esophageal cancer screening.

Relationship between the microenvironment and survival in kidney transplantation: a bibliometric analysis from 2013 to 2023.

Background: Kidney transplantation is considered the most effective treatment for end-stage renal failure. Recent studies have shown that the significance of the immune microenvironment after kidney transplantation in determining prognosis of patients. Therefore, this study aimed to conduct a bibliometric analysis to provide an overview of the knowledge structure and research trends regarding the immune microenvironment and survival in kidney transplantation. Methods: Our search included relevant publications from 2013 to 2023 retrieved from the Web of Science core repository and finally included 865 articles. To perform the bibliometric analysis, we utilized tools such as VOSviewer, CiteSpace, and the R package "bibliometrix". The analysis focused on various aspects, including country, author, year, topic, reference, and keyword clustering. Results: Based on the inclusion criteria, a total of 865 articles were found, with a trend of steady increase. China and the United States were the countries with the most publications. Nanjing Medical University was the most productive institution. High-frequency keywords were clustered into 6 areas, including kidney transplantation, transforming growth factor ß, macrophage, antibody-mediated rejection, necrosis factor alpha, and dysfunction. Antibody mediated rejection (2019-2023) was the main area of research in recent years. Conclusion: This groundbreaking bibliometric study comprehensively summarizes the research trends and advances related to the immune microenvironment and survival after kidney transplantation. It identifies recent frontiers of research and highlights promising directions for future studies, potentially offering fresh perspectives to scholars in the field.

Crosstalk of HDAC4, PP1, and GSDMD in controlling pyroptosis.

Gasdermin D (GSDMD) functions as a pivotal executor of pyroptosis, eliciting cytokine secretion following cleavage by inflammatory caspases. However, the role of posttranslational modifications (PTMs) in GSDMD-mediated pyroptosis remains largely unexplored. In this study, we demonstrate that GSDMD can undergo acetylation at the Lysine 248 residue, and this acetylation enhances pyroptosis. We identify histone deacetylase 4 (HDAC4) as the specific deacetylase responsible for mediating GSDMD deacetylation, leading to the inhibition of pyroptosis both in vitro and in vivo. Deacetylation of GSDMD impairs its ubiquitination, resulting in the inhibition of pyroptosis. Intriguingly, phosphorylation of HDAC4 emerges as a critical regulatory mechanism promoting its ability to deacetylate GSDMD and suppress GSDMD-mediated pyroptosis. Additionally, we implicate Protein phosphatase 1 (PP1) catalytic subunits (PP1α and PP1γ) in the dephosphorylation of HDAC4, thereby nullifying its deacetylase activity on GSDMD. This study reveals a complex regulatory network involving HDAC4, PP1, and GSDMD. These findings provide valuable insights into the interplay among acetylation, ubiquitination, and phosphorylation in the regulation of pyroptosis, offering potential targets for further investigation in the field of inflammatory cell death.

3D printed high-precision porous scaffolds prepared by fused deposition modeling induce macrophage polarization to promote bone regeneration.

Macrophage-mediated bone immune responses significantly influence the repair of bone defects when utilizing tissue-engineered scaffolds. Notably, the scaffolds' physical structure critically impacts macrophage polarization. The optimal pore size for facilitating bone repair remains a topic of debate due to the imprecision of traditional methods in controlling scaffold pore dimensions and spatial architecture. In this investigation, we utilized fused deposition modeling (FDM) technology to fabricate high-precision porous polycaprolactone (PCL) scaffolds, aiming to elucidate the impact of pore size on macrophage polarization. We assessed the scaffolds' mechanical attributes and biocompatibility. Real-time quantitative reverse transcription polymerase chain reaction was used to detect the expression levels of macrophage-related genes, and enzyme linked immunosorbent assay for cytokine secretion levels.In vitroosteogenic capacity was determined through alkaline phosphatase and alizarin red staining. Our findings indicated that macroporous scaffolds enhanced macrophage adhesion and drove their differentiation towards the M2 phenotype. This led to the increased production of anti-inflammatory factors and a reduction in pro-inflammatory agents, highlighting the scaffolds' immunomodulatory capabilities. Moreover, conditioned media from macrophages cultured on these macroporous scaffolds bolstered the osteogenic differentiation of bone marrow mesenchymal stem cells, exhibiting superior osteogenic differentiation potential. Consequently, FDM-fabricated PCL scaffolds, with precision-controlled pore sizes, present promising prospects as superior materials for bone tissue engineering, leveraging the regulation of macrophage polarization.

First-trimester noninvasive prenatal diagnosis of seven facioscapulohumeral muscular dystrophy type 1 families using SNP-based amplicon sequencing: An earlier, rapid and safer way.

The study is to explore the feasibility and value of SNP-based noninvasive prenatal diagnosis (NIPD) for facioscapulohumeral muscular dystrophy type 1 (FSHD1) in early pregnancy weeks. We prospectively collected seven FSHD1 families, with an average gestational age of 8+6. Among these seven couples, there were three affected FSHD1 mothers and four affected fathers. A multiplex-PCR panel comprising 402 amplicons was designed to selective enrich for highly heterozygous SNPs upstream of the DUX4 gene. Risk haplotype was constructed based on familial linkage analysis. Fetal genotypes were accurately inferred through relative haplotype dosage analysis using Bayes Factor. All tests were successfully completed in a single attempt, and no recombination events were detected. NIPD results were provided within a week, which is 4 weeks earlier than karyomapping and 7 weeks earlier than Bionano single-molecule optical mapping (BOM). Ultimately, five FSHD1 fetuses and two normal fetuses were successfully identified, with a 100% concordance rate with karyomapping and BOM. Therefore, SNP-based NIPD for FSHD1 was demonstrated to be feasible and accurate in early weeks of gestation, although the risk of recombination events cannot be completely eliminated. In the future, testing of more cases is still necessary to fully determine the clinical utility.

The role of surface functionalization in quantum dot-based photocatalytic CO2 reduction: balancing efficiency and stability.

Photocatalytic CO2 reduction offers a promising strategy to produce hydrocarbons without reliance on fossil fuels. Visible light-absorbing colloidal nanomaterials composed of earth-abundant metals suspended in aqueous media are particularly attractive owing to their low-cost, ease of separation, and highly modifiable surfaces. The current study explores such a system by employing water-soluble ZnSe quantum dots and a Co-based molecular catalyst. Water solubilization of the quantum dots is achieved with either carboxylate (3-mercaptopropionic acid) or ammonium (2-aminoethanethiol) functionalized ligands to produce nanoparticles with either negatively or positively-charged surfaces. Photocatalysis experiments are performed to compare the effectiveness of these two surface functionalization strategies on CO2 reduction and ultrafast spectroscopy is used to reveal the underlying photoexcited charge dynamics. We find that the positively-charged quantum dots can support sub-picosecond electron transfer to the carboxylate-based molecular catalyst and also produce >30% selectivity for CO and >170 mmolCO gZnSe-1. However, aggregation reduces activity in approximately one day. In contrast, the negatively-charged quantum dots exhibit >10 ps electron transfer and substantially lower CO selectivity, but they are colloidally stable for days. These results highlight the importance of the quantum dot-catalyst interaction for CO2 reduction. Furthermore, multi-dentate catalyst molecules create a trade-off between photocatalytic efficiency from strong interactions and deleterious aggregation of quantum dot-catalyst assemblies.

Developing a Prognostic Model for Primary Biliary Cholangitis Based on a Random Survival Forest Model.

Background: Primary biliary cholangitis (PBC) is a rare autoimmune liver disease with few effective treatments and a poor prognosis, and its incidence is on the rise. There is an urgent need for more targeted treatment strategies to accurately identify high-risk patients. The use of stochastic survival forest models in machine learning is an innovative approach to constructing a prognostic model for PBC that can improve the prognosis by identifying high-risk patients for targeted treatment. Method: Based on the inclusion and exclusion criteria, the clinical data and follow-up data of patients diagnosed with PBC-associated cirrhosis between January 2011 and December 2021 at Taizhou Hospital of Zhejiang Province were retrospectively collected and analyzed. Data analyses and random survival forest model construction were based on the R language. Result: Through a Cox univariate regression analysis of 90 included samples and 46 variables, 17 variables with p-values <0.1 were selected for initial model construction. The out-of-bag (OOB) performance error was 0.2094, and K-fold cross-validation yielded an internal validation C-index of 0.8182. Through model selection, cholinesterase, bile acid, the white blood cell count, total bilirubin, and albumin were chosen for the final predictive model, with a final OOB performance error of 0.2002 and C-index of 0.7805. Using the final model, patients were stratified into high- and low-risk groups, which showed significant differences with a P value <0.0001. The area under the curve was used to evaluate the predictive ability for patients in the first, third, and fifth years, with respective results of 0.9595, 0.8898, and 0.9088. Conclusion: The present study constructed a prognostic model for PBC-associated cirrhosis patients using a random survival forest model, which accurately stratified patients into low- and high-risk groups. Treatment strategies can thus be more targeted, leading to improved outcomes for high-risk patients.

Anoikis-related signature identifies tumor microenvironment landscape and predicts prognosis and drug sensitivity in colorectal cancer.

Background: Anoikis, a mechanism of programmed apoptosis, plays an important role in growth and metastasis of tumors. However, there are still few available comprehensive reports on the impact of anoikis on colorectal cancer. Method: A clustering analysis was done on 133 anoikis-related genes in GSE39582, and we compared clinical features between clusters, the tumor microenvironment was analyzed with algorithms such as "Cibersort" and "ssGSEA". We investigated risk scores of clinical feature groups and anoikis-associated gene mutations after creating a predictive model. We incorporated clinical traits to build a nomogram. Additionally, the quantitative real-time PCR was employed to investigate the mRNA expression of selected anoikis-associated genes. Result: We identified two anoikis-related clusters with distinct prognoses, clinical characteristics, and biological functions. One of the clusters was associated with anoikis resistance, which activated multiple pathways encouraging tumor metastasis. In our prognostic model, oxaliplatin may be a sensitive drug for low-risk patients. The nomogram showed good ability to predict survival time. And SIRT3, PIK3CA, ITGA3, DAPK1, and CASP3 increased in CRC group through the PCR assay. Conclusion: Our study identified two distinct modes of anoikis in colorectal cancer, with active metastasis-promoting pathways inducing an anti-anoikis subtype, which has a stronger propensity for metastasis and a worse prognosis than an anoikis-activated subtype. Massive immune cell infiltration may be an indicator of anoikis resistance. Anoikis' role in the colorectal cancer remains to be investigated.