N-doped lignin-based activated carbon aerogel derived from bamboo black pulp liquor for efficient removal of malachite green in wastewater.

In this study, a lignin-based aerogel (LA) was prepared through acid precipitation of BPBL, followed by sol-gel method and freeze-drying. Additionally, a one-step activation-carbonization method was used to acquire nitrogen-doped lignin-based activated carbon aerogel (NLACA). The adsorption and catalytic degradation performance for malachite green (MG) were examined. The specific surface area of NLACA after N-doping was 2644.5 m2/g. The adsorption capacity for MG was increased to 3433 mg/g with the presence of nitrogenous functional groups on surface of NLACA compared without N-doping. Meanwhile, non-radical singlet oxygen is the primary active substance and degradation efficiency arrives at 91.8% after the catalytic degradation within 20 min and it has good stability and reuse. Three possible degradation pathways during degradation were analyzed by LC-MS technique. The adsorption isotherm and kinetic data demonstrated conformity with both the Langmuir model and the pseudo-second-order kinetic model. The primary mechanisms of the adsorption for MG dyes on NLACA include hydrogen bonding, π-π interactions, attraction of electrostatic and pore filling. Hence, NLACA derived from BPBL acts as a cost-effective and high-performance adsorbent and catalyst for removal of MG in dye wastewater. This concept introduces an innovative approach of "treatment of waste with waste" for developing a low-consumption, high-efficiency dye wastewater treatment and provides significant reference to treatment dye wastewater.

Unsupervised domain adaptive building semantic segmentation network by edge-enhanced contrastive learning.

Unsupervised domain adaptation (UDA) is a weakly supervised learning technique that classifies images in the target domain when the source domain has labeled samples, and the target domain has unlabeled samples. Due to the complexity of imaging conditions and the content of remote sensing images, the use of UDA to accurately extract artificial features such as buildings from high-spatial-resolution (HSR) imagery is still challenging. In this study, we propose a new UDA method for building extraction, the contrastive domain adaptation network (CDANet), by utilizing adversarial learning and contrastive learning techniques. CDANet consists of a single multitask generator and dual discriminators. The generator employs a region and edge dual-branch structure that strengthens its edge extraction ability and is beneficial for the extraction of small and densely distributed buildings. The dual discriminators receive the region and edge prediction outputs and achieve multilevel adversarial learning. During adversarial training processing, CDANet aligns the cross-domain of similar pixel features in the embedding space by constructing the regional pixelwise contrastive loss. A self-training (ST) strategy based on pseudolabel generation is further utilized to address the target intradomain discrepancy. Comprehensive experiments are conducted to validate CDANet on three publicly accessible datasets, namely the WHU, Austin, and Massachusetts. Ablation experiments show that the generator network structure, contrastive loss and ST strategy all improve the building extraction accuracy. Method comparisons validate that CDANet achieves superior performance to several state-of-the-art methods, including AdaptSegNet, AdvEnt, IntraDA, FDANet and ADRS, in terms of F1 score and mIoU.

SexAnnoDB, a knowledgebase of sex-specific regulations from multi-omics data of human cancers.

BACKGROUND: Sexual differences across molecular levels profoundly impact cancer biology and outcomes. Patient gender significantly influences drug responses, with divergent reactions between men and women to the same drugs. Despite databases on sex differences in human tissues, understanding regulations of sex disparities in cancer is limited. These resources lack detailed mechanistic studies on sex-biased molecules. METHODS: In this study, we conducted a comprehensive examination of molecular distinctions and regulatory networks across 27 cancer types, delving into sex-biased effects. Our analyses encompassed sex-biased competitive endogenous RNA networks, regulatory networks involving sex-biased RNA binding protein-exon skipping events, sex-biased transcription factor-gene regulatory networks, as well as sex-biased expression quantitative trait loci, sex-biased expression quantitative trait methylation, sex-biased splicing quantitative trait loci, and the identification of sex-biased cancer therapeutic drug target genes. All findings from these analyses are accessible on SexAnnoDB ( https://ccsm.uth.edu/SexAnnoDB/ ). RESULTS: From these analyses, we defined 126 cancer therapeutic target sex-associated genes. Among them, 9 genes showed sex-biased at both the mRNA and protein levels. Specifically, S100A9 was the target of five drugs, of which calcium has been approved by the FDA for the treatment of colon and rectal cancers. Transcription factor (TF)-gene regulatory network analysis suggested that four TFs in the SARC male group targeted S100A9 and upregulated the expression of S100A9 in these patients. Promoter region methylation status was only associated with S100A9 expression in KIRP female patients. Hypermethylation inhibited S100A9 expression and was responsible for the downregulation of S100A9 in these female patients. CONCLUSIONS: Comprehensive network and association analyses indicated that the sex differences at the transcriptome level were partially the result of corresponding sex-biased epigenetic and genetic molecules. Overall, SexAnnoDB offers a discipline-specific search platform that could potentially assist basic experimental researchers or physicians in developing personalized treatment plans.

Sexual variations at the molecular level have a profound impact on cancer biology and outcomes, influencing drug responses that diverge between men and women exposed to the same drugs. Despite existing databases on sex differences in human tissues, our understanding of the regulations governing sex disparities in cancer is limited, lacking detailed mechanistic studies on sex-biased molecules. This study addresses this gap by conducting a comprehensive examination of molecular distinctions and regulatory networks across 27 cancer types, specifically focusing on sex-biased effects. The analyses led to the identification of 126 cancer therapeutic target sex-associated genes and shed light on the intricate relationship between sexual differences and cancer. Furthermore, the findings from these analyses are made accessible through SexAnnoDB, providing a specialized search platform. This platform has the potential to assist basic experimental researchers or physicians in developing personalized treatment plans based on a deeper understanding of sex-specific factors in cancer.

Recognition of TaeKwonDo kicking techniques based on accelerometer sensors.

TaeKwonDo (TKD) is a worldwide sport in both competitive among athletes and physical exercise among the public scenarios. Measuring TKD kicks have been studied a lot in a laboratory setting but rarely in a free-living situation. Machine learning algorithm combined with accelerometer data was used to study some martial art styles, e.g., Chinese KungFu but little in TKD. The purpose of this study was to discover a method to recognize different kicking techniques in TKD. A total of 20 participants (35 % male) were recruited to perform front kick, roundhouse kick, side kick and back kick 6 times on each side with three accelerometers wore on waist, right ankle and left ankle. SVM and decision tree were used to analyze data and classify kicking movements. The usage of different combination of accelerometers were also compared. The result showed that using accelerometers on waist and both ankles, on waist and only right ankle, on only waist and combined with SVM model could have at least 0.96 accuracy of classification, while decision tree had the accuracies around 0.8. It was concluded that using SVM model on only waist data is the optimal choice because of the high accuracy and less accelerometers used.

Structural characterization and hypoglycemic effect of polysaccharides of Polygonatum sibiricum.

Polygonatum sibiricum polysaccharide (PSP) was extracted and purified from raw material obtained from P. sibiricum. The structural features of PSP were investigated by Congo red, circular dichroism spectrum, high-performance gel permeation chromatography, scanning electron microscope, atomic force microscope, ultraviolet spectroscopy, and Fourier transform infrared spectroscopy analysis. In vitro simulations were conducted to investigate the kinetics of PSP enzyme inhibition. Moreover, a type II diabetes mouse model (T2DM) with streptozotocin-induced insulin resistance was established, and the indexes of lipid quadruple, insulin resistance index, oral glucose tolerance (OGTT), organ index, and pancreatic morphology of model mice were measured. The results showed that PSP mainly consists of monosaccharides, such as mannose, glucose, galactose, xylose, and arabinose. It also has a ß-glycosidic bond of a pyranose ring and an irregular reticulated aggregated structure with a triple helix. In vitro enzyme inhibition assays revealed that PSP acts as a reversible competitive inhibitor of α-glucosidase and α-amylase. Furthermore, PSP was found to reduce insulin resistance index, increase OGTT and serum insulin levels, decrease free fatty acid content to improve lipid metabolism, and lower glycated serum protein content to enhance glucose metabolism in T2DM mice, thereby leading to a reduction in blood glucose concentration. Additionally, PSP exhibited reparative effects on the damaged liver tissue cells and pancreatic tissue in T2DM mice. The experiment results provide a preliminary basis for the therapeutic mechanism of PSP about type II diabetes and a theoretical reference for application in food and pharmaceutical development.

Loss-of-function mutation of REV1 (p.R704Q) mediates cetuximab primary resistance by activating autophagy in RAS-wild type metastatic colorectal cancer.

Cetuximab in combination with FOLFIRI/FOLFOX is the standard first-line treatment for patients with RAS wild-type metastatic colorectal cancer (mCRC). However, some patients experience rapid tumor progression after treatment with cetuximab (primary resistance). Our previous research identified a gene mutation, REV1 p.R704Q, which may be a key biomarker for primary cetuximab resistance. This study aimed to study the mechanism of cetuximab resistance caused by REV1 p.R704Q mutation and reveal a novel mechanism to induce cetuximab resistance. Sanger sequencing and multivariate clinical prognostic analysis of 208 patients with mCRC showed that REV1 p.R704Q mutation is an independent risk factor for tumor progression after treatment with cetuximab in patients with RAS wild-type mCRC (Hazard ratio = 2.481, 95 % Confidence interval: 1.389-4.431, P = 0.002). The sensitivity of REV1 p.R704Q mutant cell lines to cetuximab decreased in vitro Cell Counting Kit-8 assay and in vivo subcutaneous tumor model. In vitro, we observed that decreased stability and accelerated degradation of REV1 mutant protein results in REV1 dysfunction, which activated autophagy and mediated cetuximab resistance. These findings suggested that REV1 p.R704Q mutation could predict cetuximab primary resistance in mCRC. REV1 p.R704Q mutation caused decreased stability and degradation of REV1 protein, as well as dysfunction of p.R704Q protein. REV1 p.R704Q mutation activates autophagy and mediates cetuximab resistance; further, inhibition of autophagy could reverse cetuximab resistance.

Shared genetic architecture highlights the bidirectional association between major depressive disorder and fracture risk.

Background: There is limited evidence suggesting that osteoporosis might exacerbate depressive symptoms, while more studies demonstrate that depression negatively affects bone density and increases fracture risk. Aims: To explore the relationship between major depressive disorder (MDD) and fracture risk. Methods: We conducted a nested case-control analysis (32 670 patients with fracture and 397 017 individuals without fracture) and a matched cohort analysis (16 496 patients with MDD and 435 492 individuals without MDD) in the same prospective UK Biobank data set. Further, we investigated the shared genetic architecture between MDD and fracture with linkage disequilibrium score regression and the MiXeR statistical tools. We used the conditional/conjunctional false discovery rate approach to identify the specific shared loci. We calculated the weighted genetic risk score for individuals in the UK Biobank and logistic regression was used to confirm the association observed in the prospective study. Results: We found that MDD was associated with a 14% increase in fracture risk (hazard ratio (HR) 1.14, 95% CI 1.14 to 1.15, p<0.001) in the nested case-control analysis, while fracture was associated with a 72% increase in MDD risk (HR 1.72, 95% CI 1.64 to 1.79, p<0.001) in the matched cohort analysis, suggesting a longitudinal and bidirectional relationship. Further, genetic summary data suggested a genetic overlap between MDD and fracture. Specifically, we identified four shared genomic loci, with the top signal (rs7554101) near SGIP1. The protein encoded by SGIP1 is involved in cannabinoid receptor type 1 signalling. We found that genetically predicted MDD was associated with a higher risk of fracture and vice versa. In addition, we found that the higher expression level of SGIP1 in the spinal cord and muscle was associated with an increased risk of fracture and MDD. Conclusions: The genetic pleiotropy between MDD and fracture highlights the bidirectional association observed in the epidemiological analysis. The shared genetic components (such as SGIP1) between the diseases suggest that modulating the endocannabinoid system could be a potential therapeutic strategy for both MDD and bone loss.

Cucurbitacin B induces ferroptosis in oral leukoplakia via the SLC7A11/mitochondrial oxidative stress pathway.

BACKGROUND: Oral leukoplakia (OLK), characterized by abnormal epithelial hyperplasia, is the most common precancerous oral mucosa lesion and is closely related to oxidative stress. Cucurbitacin B (CuB), a tetracyclic triterpenoid molecule derived from plants, has shown promising anti-proliferative and antioxidant effects in preclinical studies. However, whether CuB can play an antiproliferative role in OLK by regulating oxidative stress remains elusive. PURPOSE: To investigate the role of CuB in inhibiting the malignant progression of oral leukoplakia and to further explore its underlying mechanisms of action. METHODS: In vitro, the effect of CuB on the proliferation, migration, apoptosis, and cell cycle of OLK cells DOK was detected. The core genes and key pathways of OLK and CuB were analyzed in the transcriptome database, by using immunofluorescence, qRT-PCR, and Western blot to evaluate the expression levels of the ferroptosis markers ROS, GSH, MDA, Fe2+, and marker genes SLC7A11, GPX4, and FTH1. Immunohistochemistry of human tissue was performed to investigate the expression of the SLC7A11. In vivo, the model of OLK was established in C57BL/6 mice and the biosafety of CuB treatment for OLK was further evaluated. RESULTS: CuB substantially suppressed the proliferation of DOK cells. Bioinformatics analysis showed that the core targets of OLK crossing with CuB include SLC7A11 and that the essential pathways involve ROS and ferroptosis. In vitro experiments indicated that CuB might promote ferroptosis by down-regulating the expression of SLC7A11. We observed a gradual increase in SLC7A11 expression levels during the progression from normal oral mucosa to oral leukoplakia with varying degrees of epithelial dysplasia. In vivo experiments demonstrated that CuB inhibited the malignant progression of OLK by promoting ferroptosis in OLK mice and exhibited a certain level of biosafety. CONCLUSION: This study demonstrated for the first time that CuB could effectively inhibit the malignant progression of OLK by inducing ferroptosis via activating the SLC7A11/ mitochondrial oxidative stress pathway. These findings indicate that CuB could serve as the lead compound for the future development of anti-oral leukoplakia drugs.

Deciphering the complex relationship between type 2 diabetes and fracture risk with both genetic and observational evidence.

The 'diabetic bone paradox' suggested that type 2 diabetes (T2D) patients would have higher areal bone mineral density (BMD) but higher fracture risk than individuals without T2D. In this study, we found that the genetically predicted T2D was associated with higher BMD and lower risk of fracture in both weighted genetic risk score (wGRS) and two-sample Mendelian randomization (MR) analyses. We also identified ten genomic loci shared between T2D and fracture, with the top signal at SNP rs4580892 in the intron of gene RSPO3. And the higher expression in adipose subcutaneous and higher protein level in plasma of RSPO3 were associated with increased risk of T2D, but decreased risk of fracture. In the prospective study, T2D was observed to be associated with higher risk of fracture, but BMI mediated 30.2% of the protective effect. However, when stratified by the T2D-related risk factors for fracture, we observed that the effect of T2D on the risk of fracture decreased when the number of T2D-related risk factors decreased, and the association became non-significant if the T2D patients carried none of the risk factors. In conclusion, the genetically determined T2D might not be associated with higher risk of fracture. And the shared genetic architecture between T2D and fracture suggested a top signal around RSPO3 gene. The observed effect size of T2D on fracture risk decreased if the T2D-related risk factors could be eliminated. Therefore, it is important to manage the complications of T2D to prevent the risk of fracture.

Genetic and microbial determinants of azoxymethane-induced colorectal tumor susceptibility in Collaborative Cross mice and their implication in human cancer.

The insights into interactions between host genetics and gut microbiome (GM) in colorectal tumor susceptibility (CTS) remains lacking. We used Collaborative Cross mouse population model to identify genetic and microbial determinants of Azoxymethane-induced CTS. We identified 4417 CTS-associated single nucleotide polymorphisms (SNPs) containing 334 genes that were transcriptionally altered in human colorectal cancers (CRCs) and consistently clustered independent human CRC cohorts into two subgroups with different prognosis. We discovered a set of genera in early-life associated with CTS and defined a 16-genus signature that accurately predicted CTS, the majority of which were correlated with human CRCs. We identified 547 SNPs associated with abundances of these genera. Mediation analysis revealed GM as mediators partially exerting the effect of SNP UNC3869242 within Duox2 on CTS. Intestine cell-specific depletion of Duox2 altered GM composition and contribution of Duox2 depletion to CTS was significantly influenced by GM. Our findings provide potential novel targets for personalized CRC prevention and treatment.

Application of fixed-frequency ultrasound in the cultivation of Saccharomyces cerevisiae for rice wine fermentation.

BACKGROUND: Rice wine (RW) fermentation is limited by its long fermentation time, weak taste and unpleasant flavors such as oil and odor. In this study, a novel ultrasound technology of Saccharomyces cerevisiae was used with the aim of improving fermentation efficiency and volatile flavor quality of RW. RESULTS: The results showed that fixed-frequency ultrasonic treatment (28 kHz, 45 W L-1, 20 min) of S. cerevisiae seed culture at its logarithmic metaphase significantly increased the biomass and alcohol yield by 31.58% and 26.45%, respectively, and reduced fermentation time by nearly 2 days. Flavor analysis indicated that the flavor compounds in RW, specifically the esters and alcohols, were also increased in quantity after the ultrasonic treatment of S. cerevisiae seed liquid. Isobutyl acetate, ethyl butyrate, ethyl hexanoate and phenethyl acetate contents were increased by 78.92%, 129.19%, 7.79% and 97.84%, respectively, as compared to the control. CONCLUSION: Ultrasonic treatment of S. cerevisiae reduced fermentation time and enhanced the flavor profile of RW. This study could provide a theoretical and/or technological basis for the research and development of RW. © 2024 Society of Chemical Industry.

Study of prognostic splicing factors in cancer using machine learning approaches.

Splicing factors (SFs) are the major RNA-binding proteins (RBPs) and key molecules that regulate the splicing of mRNA molecules through binding to mRNAs. The expression of splicing factors is frequently deregulated in different cancer types, causing the generation of oncogenic proteins involved in cancer hallmarks. In this study, we investigated the genes that encode RNA-binding proteins and identified potential splicing factors that contribute to the aberrant splicing applying a random forest classification model. The result suggested 56 splicing factors were related to the prognosis of 13 cancers, two SF complexes in liver hepatocellular carcinoma, and one SF complex in esophageal carcinoma. Further systematic bioinformatics studies on these cancer prognostic splicing factors and their related alternative splicing events revealed the potential regulations in a cancer-specific manner. Our analysis found high ILF2-ILF3 expression correlates with poor prognosis in LIHC through alternative splicing. These findings emphasize the importance of SFs as potential indicators for prognosis or targets for therapeutic interventions. Their roles in cancer exhibit complexity and are contingent upon the specific context in which they operate. This recognition further underscores the need for a comprehensive understanding and exploration of the role of SFs in different types of cancer, paving the way for their potential utilization in prognostic assessments and the development of targeted therapies.

Downregulation of mesenteric afferent sensitivity following long-term systemic treatment of vincristine in mice.

AIMS: Gastrointestinal paresthesia and dysmotility are common side effects of vincristine (VCR) chemotherapy, which have become one of the factors for dose reduction, therapy delay or discontinuation. However, the mechanism is not entirely clear, whether it is related to autonomic nerves injury remains unknown. Therefore, we aimed to study whether VCR-induced gastrointestinal toxicity is related to changes in mesenteric afferent activity. METHODS: The effects of a single VCR stimulation and long-term systemic VCR treatment on mesenteric afferent activity were investigated by directly recording mesenteric afferent discharge in vitro. RESULTS: Our results showed that a single VCR (0.001-1 µmol/L) stimulation obviously increased the spontaneous, chemically evoked and mechanically evoked discharge of jejunal and colonic mesenteric afferents. This kind of hypersensitivity of VCR could be blocked by capsazepine, a transient receptor potential vanilloid 1 (TRPV1) antagonist. For the mice treated with VCR (0.1 mg/kg/d, i.p.) for 14 days, the abdominal withdrawal reflex and writhing response scores were reduced. Meanwhile, the spontaneous discharge of colonic mesenteric afferents and the afferent response to VCR was downregulated, and the afferent sensitivity to chemical and mechanical stimulation was reduced. Moreover, the expression of TRPV1 in colon was decreased. CONCLUSIONS: These results suggest that the direct stimulation by VCR increases the mesenteric afferent sensitivity by activating TRPV1, which may be the reason of VCR-induced abdominal pain; the long-term systemic treatment of VCR decreases mesenteric afferent sensitivity by reducing TRPV1, which may be the reason of VCR-induced constipation.

Burnout and depression in college students.

Research on burnout has garnered considerable attention since its inception. However, the ongoing debate persists regarding the conceptual model of burnout and its relationship with depression. Thus, we conducted a network analysis to determine the dimensional structure of burnout and the burnout-depression overlap. The Maslach Burnout Inventory-Student Survey and Patient Health Questionnaire-9 were used to measure burnout and depression among 1096 college students. We constructed networks for burnout, depression, and a burnout-depression co-occurrence network. The results showed that cynicism symptom was the most central to the burnout network. In the co-occurrence network, depressive symptoms ("anhedonia", "fatigue") and burnout symptom ("doubting the significance of studies") were the most significant in causing burnout-depression comorbidity. Community detection revealed three communities within burnout symptoms, aligning closely with their three dimensions identified through factor analysis. Additionally, there was no overlap between burnout and depression. In conclusion, our findings support a multidimensional structure of burnout, affirming it as a distinct concept separate from depression. Cynicism, rather than exhaustion, plays the most important role in burnout and the burnout-depression comorbidity.

Perioperative immunotherapy: the main clinical treatment for resectable non-small cell lung cancer.

Phase 3 clinical trials of perioperative immunotherapy for resectable non-small cell lung cancer (NSCLC): In recent years, immunotherapy for NSCLC is not only limited to advanced disease, but also has shown gratifying efficacy for early resectable NSCLC. With the publication of the results of several phase 3 clinical trials, perioperative immunotherapy will become one of the main treatment modalities for resectable NSCLC.

Searching across-cohort relatives in 54,092 GWAS samples via encrypted genotype regression.

Explicitly sharing individual level data in genomics studies has many merits comparing to sharing summary statistics, including more strict QCs, common statistical analyses, relative identification and improved statistical power in GWAS, but it is hampered by privacy or ethical constraints. In this study, we developed encG-reg, a regression approach that can detect relatives of various degrees based on encrypted genomic data, which is immune of ethical constraints. The encryption properties of encG-reg are based on the random matrix theory by masking the original genotypic matrix without sacrificing precision of individual-level genotype data. We established a connection between the dimension of a random matrix, which masked genotype matrices, and the required precision of a study for encrypted genotype data. encG-reg has false positive and false negative rates equivalent to sharing original individual level data, and is computationally efficient when searching relatives. We split the UK Biobank into their respective centers, and then encrypted the genotype data. We observed that the relatives estimated using encG-reg was equivalently accurate with the estimation by KING, which is a widely used software but requires original genotype data. In a more complex application, we launched a finely devised multi-center collaboration across 5 research institutes in China, covering 9 cohorts of 54,092 GWAS samples. encG-reg again identified true relatives existing across the cohorts with even different ethnic backgrounds and genotypic qualities. Our study clearly demonstrates that encrypted genomic data can be used for data sharing without loss of information or data sharing barrier.

Large language models in bioinformatics: applications and perspectives.

Large language models (LLMs) are a class of artificial intelligence models based on deep learning, which have great performance in various tasks, especially in natural language processing (NLP). Large language models typically consist of artificial neural networks with numerous parameters, trained on large amounts of unlabeled input using self-supervised or semi-supervised learning. However, their potential for solving bioinformatics problems may even exceed their proficiency in modeling human language. In this review, we will present a summary of the prominent large language models used in natural language processing, such as BERT and GPT, and focus on exploring the applications of large language models at different omics levels in bioinformatics, mainly including applications of large language models in genomics, transcriptomics, proteomics, drug discovery and single cell analysis. Finally, this review summarizes the potential and prospects of large language models in solving bioinformatic problems.

Prognostic significance and immunological role of HPRT1 in human cancers.

Hypoxanthine phosphoribosyl transferase 1 (HPRT1), once considered a housekeeping gene, has been identified as playing an important role in several tumors. Its role in pan-cancer, however, has not been systematically studied. This study evaluates the relationship between HPRT1 and clinical parameters, survival prognosis, and tumor immunity based on multi omics data from the Cancer Genome Atlas (TCGA) and the Gene Expression Omnibus (GEO) databases. Drug sensitivity analysis screened 16 effective drugs against HPRT1, exploring the interactions with chemicals and genes. The significance of HPRT1 in tumor immunotherapy has also been investigated. Immunohistochemistry confirmed significant differences in the expression of HPRT1 between five tumor types (colon adenocarcinoma [COAD], head-neck squamous cell carcinoma [HNSC], lung adenocarcinoma [LUAD], thyroid carcinoma [THCA], and uterine corpus endometrial carcinoma [UCEC]) and adjacent normal tissues (P < 0.05). HPRT1 competitive endogenous RNA network was constructed in HNSC. Through cytological experiments, it was verified that HPRT1 plays a carcinogenic role in HNSC and is associated with tumor cell proliferation, migration, invasion, and apoptosis. In addition, there was a significant positive correlation between HPRT1 and programmed cell death-1 (PD-1) expression in HNSC (P < 0.05). These findings suggest that HPRT1 may be a potential biomarker for predicting and treating cancer.

How loneliness linked to anxiety and depression: a network analysis based on Chinese university students.

BACKGROUND: There is conclusive evidence of a multifaceted and bidirectional relationship between loneliness and depression and anxiety. Nonetheless, more extensive research is needed to examine their relationships at a more granular level. This study employed a network analysis approach to identify the pathological mechanisms underpinning those relationships and to identify important bridge nodes as potential targets for intervention. METHODS: 941 University students were included in this study. The ULS-6 (the short-form UCLA Loneliness Scale) was used to assess loneliness, the PHQ-9 (Patient Health questionnaire-9) and GAD-7 (Generalized anxiety disorder 7-item) scales were used to assess the symptoms of depression and anxiety. We constructed two network structures of loneliness-anxiety and loneliness-depression and computed bridge expected influence for each symptom. In addition, we showed a flow network of "Suicide" containing symptoms of depression and loneliness. RESULTS: All edges were positive in both networks constructed and the strongest edges were present within disorder communities. The overall connection between loneliness and depression was stronger compared to anxiety. The results demonstrated that the loneliness item "People are around me but not with me" was identified as bridge symptom in both networks. Furthermore, "Suicide" was directly connected to five symptoms of depression and four items of loneliness, with the strongest connections being between it and "Feeling of worthlessness" and "Psychomotor agitation/retardation". CONCLUSIONS: Our findings provide a more nuanced explanation of the link between loneliness and depression and anxiety. The results identified the bridge symptom "People are around me but not with me", which had the strongest effect on enhancing symptoms of depression and anxiety. Clinical improvements based on the findings of this study and the impact of the intervention are discussed.

Exploring the cultivation of psychological resilience in medical students from the perspective of doctor-patient relationship.

The current medical model has transitioned from the original biomedical treatment model to a bio-psycho-social model, where patients now have higher demands for service awareness. Consequently, doctor-patient relationships have become a crucial aspect of the contemporary medical process. Currently, psychological resilience among medical students in China tends to be at a moderately lower level. Medical students often exhibit poor psychological qualities in handling contradictions in doctor-patient relationships. Moreover, there is a lack of emphasis on the education of corresponding psychological qualities for medical students during the teaching process. This deficiency is highly disadvantageous for medical students in their future management of doctor-patient relationships. The article explores how to cultivate psychological resilience in medical students and enhance their ability to handle conflicts in doctor-patient relationships from the perspective of doctor-patient relationships. The author suggests that schools should place greater emphasis on the psychological resilience of medical students, change teaching methods, incorporate online education to enhance the mentalization level of medical students, and propose an eight-week mindfulness cognitive therapy program to improve psychological resilience. The intervention process should consider establishing positive coping mechanisms, promoting good interpersonal relationships among medical students, and regulating individual negative emotions. Through simulating doctor-patient scenarios, teachers should consciously train the psychological resilience of medical students, improve their cognition and thinking abilities, and accelerate their psychological health recovery.