IRF1 suppresses colon cancer proliferation by reducing SPI1-mediated transcriptional activation of GPX4 and promoting ferroptosis.

IRF1 is a tumor suppressor gene in colon cancer. This study aimed to explore the potential regulation of IRF1 on the ferroptosis of colon cancer and the mechanisms underlying its regulation of GPX4 transcription. IRF1 interacting transcription factors regulating GPX4 transcription were predicted and validated. The role of the IRF1/SPI1-GPX4 axis on the ferroptosis of colon cancer cells was explored. Results showed that IRF1 overexpression reduced GPX4 transcription, increased reactive oxygen species (ROS) and lipid ROS accumulation, and enhanced erastin-induced colon cancer cell growth in vitro and in vivo. SPI1 could directly bind to the GPX4 promoter (-414 to -409) and activate its transcription. IRF1 could bind to SPI1 and suppress its transcriptional activating effects on GPX4 expression. SPI1 overexpression reduced ROS and lipid ROS accumulation and increased colon cancer cell viability and colony formation upon erastin induction. These trends were reversed by IRF1 overexpression. In conclusion, this study revealed a novel oncogenic mechanism of SPI1 by reducing erastin-induced ferroptosis in colon cancer. IRF1 interacts with SPI1 and suppresses its transcriptional activating effect on GPX4 expression. Through this mechanism, IRF1 can enhance erastin-induced ferroptosis of colon cancer. The IRF1/SPI1-GPX4 axis might play a crucial role in modulating ferroptosis in colon cancer and might serve as a potential therapeutic target in the future.

Risk factors for lumbar disc herniation recurrence after percutaneous endoscopic lumbar discectomy: a meta-analysis of 58 cohort studies.

Recurrent lumbar disc herniation (rLDH) is one of the most serious complications and major causes of surgical failure and paralysis following percutaneous endoscopic lumbar discectomy (PELD). There are reports in the literature on the identification of risk factors associated with rLDH; however, the results are controversial. Therefore, we conducted a meta-analysis to identify risk factors for rLDH among patients following spinal surgery. PubMed, EMBASE, and the Cochrane Library were searched without language restrictions from inception to April 2018 for studies reporting risk factors for LDH recurrence after PELD. MOOSE guidelines were followed in this meta-analysis. We used a random effects model to aggregate odds ratios (ORs) with 95% confidence intervals (CIs). The evidence of observational studies was classified into high quality (class I), medium quality (class II/III), and low quality (class IV) based on the P value of the total sample size and heterogeneity between studies. Fifty-eight studies were identified with a mean follow-up of 38.8 months. Studies with high-quality (class I) evidence showed that postoperative LDH recurrence after PELD was significantly correlated with diabetes (OR, 1.64; 95% CI, 1.14 to 2.31), the protrusion type LDH (OR, 1.62; 95% CI, 1.02 to 2.61), and less experienced surgeons (OR, 1.54; 95% CI, 1.10 to 2.16). Studies with medium-quality (class II or III) evidence showed that postoperative LDH recurrence was significantly correlated with advanced age (OR, 1.11; 95% CI, 1.05 to 1.19), Modic changes (OR, 2.23; 95% CI, 1.53 to 2.29), smoking (OR, 1.31; 95% CI, 1.00 to 1.71), no college education (OR, 1.56; 95% CI, 1.05 to 2.31), obesity (BMI ≥ 25 kg/m2) (OR, 1.66; 95% CI, 1.11 to 2.47), and inappropriate manual labor (OR, 2.18; 95% CI, 1.33 to 3.59). Based on the current literature, eight patient-related and one surgery-related risk factor are predictors of postoperative LDH recurrence after PELD. These findings may help clinicians raise awareness of early intervention for patients at high risk of LDH recurrence after PELD.

Characterization of long G4-rich enhancer-associated genomic regions engaging in a novel loop:loop 'G4 Kissing' interaction.

Mammalian antibody switch regions (∼1500 bp) are composed of a series of closely neighboring G4-capable sequences. Whereas numerous structural and genome-wide analyses of roles for minimal G4s in transcriptional regulation have been reported, Long G4-capable regions (LG4s)-like those at antibody switch regions-remain virtually unexplored. Using a novel computational approach we have identified 301 LG4s in the human genome and find LG4s prone to mutation and significantly associated with chromosomal rearrangements in malignancy. Strikingly, 217 LG4s overlap annotated enhancers, and we find the promoters regulated by these enhancers markedly enriched in G4-capable sequences suggesting G4s facilitate promoter-enhancer interactions. Finally, and much to our surprise, we also find single-stranded loops of minimal G4s within individual LG4 loci are frequently highly complementary to one another with 178 LG4 loci averaging >35 internal loop:loop complements of >8 bp. As such, we hypothesized (then experimentally confirmed) that G4 loops within individual LG4 loci directly basepair with one another (similar to characterized stem-loop kissing interactions) forming a hitherto undescribed, higher-order, G4-based secondary structure we term a 'G4 Kiss or G4K'. In conclusion, LG4s adopt novel, higher-order, composite G4 structures directly contributing to the inherent instability, regulatory capacity, and maintenance of these conspicuous genomic regions.

The tumor immune microenvironment and immune-related signature predict the chemotherapy response in patients with osteosarcoma.

BACKGROUND: Genome-wide expression profiles have been shown to predict the response to chemotherapy. The purpose of this study was to develop a novel predictive signature for chemotherapy in patients with osteosarcoma. METHODS: We analysed the relevance of immune cell infiltration and gene expression profiles of the tumor samples of good responders with those of poor responders from the TARGET and GEO databases. Immune cell infiltration was evaluated using a single-sample gene set enrichment analysis (ssGSEA) and the CIBERSORT algorithm between good and poor chemotherapy responders. Differentially expressed genes were identified based on the chemotherapy response. LASSO regression and binary logistic regression analyses were applied to select the differentially expressed immune-related genes (IRGs) and developed a predictive signature in the training cohort. A receiver operating characteristic (ROC) curve analysis was employed to assess and validate the predictive accuracy of the predictive signature in the validation cohort. RESULTS: The analysis of immune infiltration showed a positive relationship between high-level immune infiltration and good responders, and T follicular helper cells and CD8 T cells were significantly more abundant in good responders with osteosarcoma. Two hundred eighteen differentially expressed genes were detected between good and poor responders, and a five IRGs panel comprising TNFRSF9, CD70, EGFR, PDGFD and S100A6 was determined to show predictive power for the chemotherapy response. A chemotherapy-associated predictive signature was developed based on these five IRGs. The accuracy of the predictive signature was 0.832 for the training cohort and 0.720 for the validation cohort according to ROC analysis. CONCLUSIONS: The novel predictive signature constructed with five IRGs can be effectively utilized to predict chemotherapy responsiveness and help improve the efficacy of chemotherapy in patients with osteosarcoma.

MicroRNA-93-5p expression in tumor tissue and its tumor suppressor function via targeting programmed death ligand-1 in colorectal cancer.

This work aimed to investigate miR-93-5p expression in tumor tissue and its in vitro effects in colorectal cancer (CRC) by targeting programmed death ligand-1 (PD-L1). MiR-93-5p and PD-L1 expression was detected in CRC and adjacent normal tissues by quantitative real-time polymerase chain reaction and immunohistochemistry. The correlation between miR-93-5p and PD-L1 was validated by a dual-luciferase reporter assay. HCT116 and SW480 cells were divided into blank, miR-NC, miR-93-5p mimics, miR-93-5p inhibitor, PD-L1 small interfering RNA (siRNA) and miR-93-5p inhibitor + PD-L1 siRNA groups, and wound-healing and transwell assays were performed to detect cell migration and invasion, respectively. Protein expression was measured by western blotting. The secretion of cytokines was detected in the CRC cell/T coculture models. MiR-93-5p was downregulated in CRC tissues with upregulated PD-L1. In PD-L1-negative patients, miR-93-5p expression was increased compared with that in PD-L1-positive patients. MiR-93-5p and PD-L1 expression levels were associated with the tumor differentiation, lymphatic metastasis, TNM, Duke's stage, and prognosis of CRC. PD-L1 siRNA weakened the migration and invasion abilities via decreased expression of matrix metalloproteinase-1 (MMP-1), -2, and -9, and these effects were abolished by the miR-93-5p inhibitor. Additionally, anti-PD-L1 upregulated the expressions of interleukin-2 (IL-2), tumor necrosis factor-α (TNF-α), and interferon γ (IFN-γ) in the coculture of T cells with CRC cells, but downregulated the expressions of IL-1ß, IL-10, and TGF-ß. However, these changes were partially reversed by miR-93-5p inhibition. miR-93-5p is expected to be a novel target for CRC treatment since it decreases the migration and invasion, as well as the immune evasion, of CRC cells via targeting PD-L1.

A partial encryption algorithm for medical images based on quick response code and reversible data hiding technology.

BACKGROUND: Medical image data, like most patient information, have a strong requirement for privacy and confidentiality. This makes transmitting medical image data, within an open network, problematic, due to the aforementioned issues, along with the dangers of data/information leakage. Possible solutions in the past have included the utilization of information-hiding and image-encryption technologies; however, these methods can cause difficulties when attempting to recover the original images. METHODS: In this work, we developed an algorithm for protecting medical image key regions. Coefficient of variation is first employed to identify key regions, a.k.a. image lesion areas; then additional areas are processed as blocks and texture complexity is analyzed. Next, our novel reversible data-hiding algorithm embeds lesion area contents into a high-texture area, after which an Arnold transformation is utilized to protect the original lesion information. After this, we use image basic information ciphertext and decryption parameters to generate a quick response (QR) code used in place of original key regions. RESULTS: The approach presented here allows for the storage (and sending) of medical image data within open network environments, while ensuring only authorized personnel are able to recover sensitive patient information (both image and meta-data) without information loss. DISCUSSION: Peak signal to noise ratio and the Structural Similarity Index measures show that the algorithm presented in this work can encrypt and restore original images without information loss. Moreover, by adjusting the threshold and the Mean Squared Error, we can control the overall quality of the image: the higher the threshold, the better the quality and vice versa. This allows the encryptor to control the amount of degradation as, at appropriate amounts, degradation aids in the protection of the image. CONCLUSIONS: As shown in the experimental results, the proposed method allows for (a) the safe transmission and storage of medical image data, (b) the full recovery (no information loss) of sensitive regions within the medical image following encryption, and (c) meta-data about the patient and image to be stored within and recovered from the public image.

Development of a support vector machine learning and smart phone Internet of Things-based architecture for real-time sleep apnea diagnosis.

BACKGROUND: The breathing disorder obstructive sleep apnea syndrome (OSAS) only occurs while asleep. While polysomnography (PSG) represents the premiere standard for diagnosing OSAS, it is quite costly, complicated to use, and carries a significant delay between testing and diagnosis. METHODS: This work describes a novel architecture and algorithm designed to efficiently diagnose OSAS via the use of smart phones. In our algorithm, features are extracted from the data, specifically blood oxygen saturation as represented by SpO2. These features are used by a support vector machine (SVM) based strategy to create a classification model. The resultant SVM classification model can then be employed to diagnose OSAS. To allow remote diagnosis, we have combined a simple monitoring system with our algorithm. The system allows physiological data to be obtained from a smart phone, the data to be uploaded to the cloud for processing, and finally population of a diagnostic report sent back to the smart phone in real-time. RESULTS: Our initial evaluation of this algorithm utilizing actual patient data finds its sensitivity, accuracy, and specificity to be 87.6%, 90.2%, and 94.1%, respectively. DISCUSSION: Our architecture can monitor human physiological readings in real time and give early warning of abnormal physiological parameters. Moreover, after our evaluation, we find 5G technology offers higher bandwidth with lower delays ensuring more effective monitoring. In addition, we evaluate our algorithm utilizing real-world data; the proposed approach has high accuracy, sensitivity, and specific, demonstrating that our approach is very promising. CONCLUSIONS: Experimental results on the apnea data in University College Dublin (UCD) Database have proven the efficiency and effectiveness of our methodology. This work is a pilot project and still under development. There is no clinical validation and no support. In addition, the Internet of Things (IoT) architecture enables real-time monitoring of human physiological parameters, combined with diagnostic algorithms to provide early warning of abnormal data.

An integrative approach to investigate the association among high-sensitive C-reactive protein, body fat mass distribution, and other cardiometabolic risk factors in young healthy women.

Prior research has indicated that as an important biomarker of chronic low-grade inflammation, high-sensitivity C-reactive protein (hs-CRP) can play important roles on the onset of metabolic syndrome and cardiovascular diseases (CVD). We conducted an integrative approach, which combines biological wet-lab experiments, statistical analysis, and semantics-oriented bioinformatics & computational analysis, to investigate the association among hs-CRP, body fat mass (FM) distribution, and other cardiometabolic risk factors in young healthy women. Research outcomes in this study resulted in two novel discoveries. Discovery 1: There are four primary determinants for hs-CRP, i.e., central/abdominal FM (a.k.a. trunk FM) accumulation, leptin, high density lipoprotein cholesterol (HDL-C), and plasminogen activator inhibitior-1 (PAI-1). Discovery 2: Chronic inflammation may involve in adipocyte-cytokine interaction underlying the metabolic derangement in healthy young women.

Using an artificial neural network to map cancer common data elements to the biomedical research integrated domain group model in a semi-automated manner.

BACKGROUND: The medical community uses a variety of data standards for both clinical and research reporting needs. ISO 11179 Common Data Elements (CDEs) represent one such standard that provides robust data point definitions. Another standard is the Biomedical Research Integrated Domain Group (BRIDG) model, which is a domain analysis model that provides a contextual framework for biomedical and clinical research data. Mapping the CDEs to the BRIDG model is important; in particular, it can facilitate mapping the CDEs to other standards. Unfortunately, manual mapping, which is the current method for creating the CDE mappings, is error-prone and time-consuming; this creates a significant barrier for researchers who utilize CDEs. METHODS: In this work, we developed a semi-automated algorithm to map CDEs to likely BRIDG classes. First, we extended and improved our previously developed artificial neural network (ANN) alignment algorithm. We then used a collection of 1284 CDEs with robust mappings to BRIDG classes as the gold standard to train and obtain the appropriate weights of six attributes in CDEs. Afterward, we calculated the similarity between a CDE and each BRIDG class. Finally, the algorithm produces a list of candidate BRIDG classes to which the CDE of interest may belong. RESULTS: For CDEs semantically similar to those used in training, a match rate of over 90% was achieved. For those partially similar, a match rate of 80% was obtained and for those with drastically different semantics, a match rate of up to 70% was achieved. DISCUSSION: Our semi-automated mapping process reduces the burden of domain experts. The weights are all significant in six attributes. Experimental results indicate that the availability of training data is more important than the semantic similarity of the testing data to the training data. We address the overfitting problem by selecting CDEs randomly and adjusting the ratio of training and verification samples. CONCLUSIONS: Experimental results on real-world use cases have proven the effectiveness and efficiency of our proposed methodology in mapping CDEs with BRIDG classes, both those CDEs seen before as well as new, unseen CDEs. In addition, it reduces the mapping burden and improves the mapping quality.

Muscle fatigue detection and treatment system driven by internet of things.

BACKGROUND: Internet of things is fast becoming the norm in everyday life, and integrating the Internet into medical treatment, which is increasing day by day, is of high utility to both clinical doctors and patients. While there are a number of different health-related problems encountered in daily life, muscle fatigue is a common problem encountered by many. METHODS: To facilitate muscle fatigue detection, a pulse width modulation (PWM) and ESP8266-based fatigue detection and recovery system is introduced in this paper to help alleviate muscle fatigue. The ESP8266 is employed as the main controller and communicator, and PWM technology is employed to achieve adaptive muscle recovery. Muscle fatigue can be detected by surface electromyography signals and monitored in real-time via a wireless network. RESULTS: With the help of the proposed system, human muscle fatigue status can be monitored in real-time, and the recovery vibration motor status can be optimized according to muscle activity state. DISCUSSION: Environmental factors had little effect on the response time and accuracy of the system, and the response time was stable between 1 and 2 s. As indicated by the consistent change of digital value, muscle fatigue was clearly diminished using this system. CONCLUSIONS: Experiments show that environmental factors have little effect on the response time and accuracy of the system. The response time is stably between 1 and 2 s, and, as indicated by the consistent change of digital value, our systems clearly diminishes muscle fatigue. Additionally, the experimental results show that the proposed system requires minimal power and is both sensitive and stable.

Co-expression analysis among microRNAs, long non-coding RNAs, and messenger RNAs to understand the pathogenesis and progression of diabetic kidney disease at the genetic level.

Diabetic kidney disease (DKD) is a serious disease that presents a major health problem worldwide. There is a desperate need to explore novel biomarkers to further facilitate the early diagnosis and effective treatment in DKD patients, thus preventing them from developing end-stage renal disease (ESRD). However, most regulation mechanisms at the genetic level in DKD still remain unclear. In this paper, we describe our innovative methodologies that integrate biological, computational, and statistical approaches to investigate important roles performed by regulations among microRNAs (miRs), long non-coding RNAs (lncRNAs), and messenger RNAs (mRNAs) in DKD. We conducted fully transparent, rigorously designed experiments. Our robust and reproducible results identified hsa-miR-223-3p as a candidate novel biomarker performing important roles in DKD disease process.

Introduction: selected extended articles from the 2nd International Workshop on Semantics-Powered Data Analytics (SEPDA 2017).

In this editorial, we first summarize the 2nd International Workshop on Semantics-Powered Data Analytics (SEPDA 2017) held on November 13, 2017 in Kansas City, Missouri, U.S.A., and then briefly introduce 13 research articles included in this supplement issue, covering topics such as Semantic Integration, Deep Learning, Knowledge Base Construction, and Natural Language Processing.

A semantics-oriented computational approach to investigate microRNA regulation on glucocorticoid resistance in pediatric acute lymphoblastic leukemia.

BACKGROUND: Acute lymphoblastic leukemia is the most prevalent neoplasia among children. Despite the tremendous achievements of state-of-the-art treatment strategies, drug resistance is still a major cause of chemotherapy failure leading to relapse in pediatric acute lymphoblastic leukemia. The underlying mechanisms of such phenomenon are not yet clear and subject to further exploration. Prior research has shown that microRNAs can act as post-transcriptional regulators of many genes related to drug resistance. However, details of microRNA regulation mechanisms in pediatric acute lymphoblastic leukemia are far from completely understood. METHODS: We utilized a computational approach based upon emerging biomedical and biological ontologies and semantic technologies to investigate the important roles of microRNA: mRNA regulation on glucocorticoid resistance in pediatric acute lymphoblastic leukemia. In particular, various filtering mechanisms were designed based on the user-provided MeSH term to narrow down the most promising microRNAs in an effective manner. RESULTS: During our manual search on background literature, we found a total of 18 candidate microRNAs that possibly regulate glucocorticoid resistance in pediatric acute lymphoblastic leukemia. After the first-round filtering using the Broader-Match option where both the user-provided MeSH term and its direct parent term were utilized, the number of targets for 18 microRNAs was reduced from 232 to 74. During the second-round filtering with the Exact-Match option where only the MeSH term itself was utilized, the number of targets was further reduced to 19. Finally, we conducted semantic searches in the OmniSearch software tool on the five likely regulating microRNAs and identified two most likely microRNAs. CONCLUSIONS: We successfully identified two microRNAs, hsa-miR-142-3p and hsa-miR-17-5p, which are computationally predicted to closely relate to glucocorticoid resistance, thus potentially serving as novel biomarkers and therapeutic targets in pediatric acute lymphoblastic leukemia.

Investigating the impact of Wnt pathway-related genes on biomarker and diagnostic model development for osteoporosis in postmenopausal females.

The Wnt signaling pathway is essential for bone development and maintaining skeletal homeostasis, making it particularly relevant in osteoporosis patients. Our study aimed to identify distinct molecular clusters associated with the Wnt pathway and develop a diagnostic model for osteoporosis in postmenopausal Caucasian women. We downloaded three datasets (GSE56814, GSE56815 and GSE2208) related to osteoporosis from the GEO database. Our analysis identified a total of 371 differentially expressed genes (DEGs) between low and high bone mineral density (BMD) groups, with 12 genes associated with the Wnt signaling pathway, referred to as osteoporosis-associated Wnt pathway-related genes. Employing four independent machine learning models, we established a diagnostic model using the 12 osteoporosis-associated Wnt pathway-related genes in the training set. The XGB model showed the most promising discriminative potential. We further validate the predictive capability of our diagnostic model by applying it to three external datasets specifically related to osteoporosis. Subsequently, we constructed a diagnostic nomogram based on the five crucial genes identified from the XGB model. In addition, through the utilization of DGIdb, we identified a total of 30 molecular compounds or medications that exhibit potential as promising therapeutic targets for osteoporosis. In summary, our comprehensive analysis provides valuable insights into the relationship between the osteoporosis and Wnt signaling pathway.

Green Guidewire Combined with Epidural Needle-Saline Separating Minimize Invasiveness and Optimize Outcomes in Single-Port Laparoscopic Treatment for Pediatric Inguinal Hernia.

Objective: To investigate the application value, feasibility, and safety of modified single-port laparoscopic surgery in the treatment of pediatric inguinal hernia. Methods: One hundred and twenty cases of children with indirect inguinal hernia admitted from 2017 to 2022 were enrolled in the Control and Observation groups, with 80 and 40 cases, respectively. They underwent traditional open high ligation of the hernia sac and modified single-port laparoscopic high ligation of the hernia sac, respectively. The operation duration, surgical incision size, intraoperative bleeding, postoperative hospital stay, first ambulation time, and hospitalization expenses were compared between the two groups, as well as the incidence of surgical complications in the two groups. Results: The surgical incision size, intraoperative bleeding, postoperative hospital stay, and first ambulation time of the Observation group were less than those of the Control group. There was no significant difference in operation duration or hospitalization expenses between the two groups. Only two cases in the Observation group showed suture knot reactions after surgery, with no incision infection, inguinal hematoma, iatrogenic cryptorchidism, etc. The overall incidence of complications in the Observation group was lower than that of the Control group. Conclusion: Modified single-port laparoscopic surgery for inguinal hernia in children has the advantages of minimal invasiveness, and enhanced recovery, along with fewer complications and recurrence, hence it is worthy of recommendation in clinical practice.

The impact of perioperative opioid use on postoperative outcomes following spinal surgery: a meta-analysis of 60 cohort studies with 13 million participants.

BACKGROUND CONTEXT: An important factor for the prognosis of spinal surgery is the perioperative use of opioids. However, the relationship is not clear. PURPOSE: The purpose of this study was to evaluate the effect of perioperative opioid use on the prognosis of patients following spinal surgery. STUDY DESIGN/SETTING: Systematic review and meta-analysis. OUTCOME MEASURES: A meta-analysis was conducted using the random-effects method to calculate pooled odds ratios (ORs) with 95% confidence intervals (CIs). METHODS: The PubMed, Embase, and Cochrane Library databases were systematically searched to find relevant articles that were published until September 2, 2022. The primary outcome was prolonged postoperative opioid use, and secondary outcomes included the length of stay (LOS), reoperation, the time to return to work (RTW), postoperative complications, gastrointestinal complications, new permanent disability, central nervous system events and infection. In addition, subgroup analysis of the primary outcome was conducted to explore the main sources of heterogeneity, and sensitivity analysis of all outcomes was performed to evaluate the stability of the results. RESULTS: A total of 60 cohort studies involving 13,219,228 individuals met the inclusion criteria. Meta-analysis showed that perioperative opioid use was specifically related to prolonged postoperative opioid use (OR 6.91, 95% CI 6.09 to 7.84, p<.01). Furthermore, the results also showed that perioperative opioid use was significantly associated with prolonged LOS (OR 1.74, 95% CI 1.39 to 2.18, p<.01), postoperative complications (OR 1.72, 95% CI 1.26 to 2.36, p<.01), reoperation (OR 2.38, 95% CI 1.85 to 3.07, p<.01), the time to RTW (OR 0.45, 95% CI 0.39 to 0.52, p<.01), gastrointestinal complications (OR 1.39, 95% CI 1.30 to 1.48, p<.01), central nervous system events (OR 1.99, 95% CI 1.21 to 3.27, p=.07) and infection (OR 1.22, 95% CI 1.09 to 1.36, p=.01). These results were corroborated by the trim-and-fill procedure and leave-one-out sensitivity analyses. CONCLUSIONS: Based on the current evidence, patients with perioperative opioid use, in comparison to controls, appear to have prolonged postoperative opioid use, which may increase the risk of poor outcomes including prolonged LOS, complications, reoperation, RTW and so on. However, these results must be carefully interpreted as the number of studies included was small and the studies were statistically heterogeneous. These findings may help clinicians to realize the harmfulness of perioperative use of opioids, reduce the use of prescription opioids, necessarily withdraw before operation or significantly wean to the lowest tolerable preoperative amount, and provide some inspiration for standardizing the use of opioids in the future.

Promotion of stem cell-like phenotype of lung adenocarcinoma by FAM83A via stabilization of ErbB2.

Lung cancer stands as the leading cause of mortality among all types of tumors, with over 40% of cases being lung adenocarcinoma (LUAD). Family with sequence similarity 83 member A (FAM83A) emerges as a notable focus due to its frequent overexpression in LUAD. Despite this, the precise role of FAM83A remains elusive. This study addresses this gap by unveiling the crucial involvement of FAM83A in maintaining the cancer stem cell-like (CSC-like) phenotype of LUAD. Through a global proteomics analysis, the study identifies human epidermal growth factor receptor 2 (HER2 or ErbB2) as a crucial target of FAM83A. Mechanistically, FAM83A facilitated ErbB2 expression at the posttranslational modification level via the E3 ubiquitin ligase STUB1 (STIP1-homologous U-Box containing protein 1). More importantly, the interaction between FAM83A and ErbB2 at Arg241 promotes calcineurin (CALN)-mediated dephosphorylation of ErbB2, followed by inhibition of STUB1-mediated ubiquitin-proteasomal ErbB2 degradation. The maintenance of the CSC-like phenotype by FAM83A, achieved through the posttranslational regulation of ErbB2, offers valuable insights for identifying potential therapeutic targets for LUAD.

Detection of Changes in CEA and ProGRP Levels in BALF of Patients with Peripheral Lung Cancer and the Relationship with CT Signs.

Objective: To investigate the relationship between the detection of changes in the levels of carcinoembryonic antigen (CEA) and progastrin-releasing peptide (ProGRP) in bronchoalveolar lavage fluid (BALF) and CT signs in patients with peripheral lung cancer. Methods: Retrospective analysis of 108 patients with perihilar lung cancer who attended our hospital from January 2019 to January 2022, 54 cases were randomly selected as the observation group and 50 cases as the control group. Patients in both groups received CT examination and BALF test at the same time to observe and compare the differences in serum levels, the relationship between CT signs and serum indices, and the diagnostic value of peripheral lung cancer between the two groups. Results: The serum levels of ProGrp, CEA, CA211, and NSE in the observation group were significantly higher than those in the control group, and the difference was statistically significant (P < 0.05). The morphology, density, mass enhancement pattern, bronchial morphology, obstructive signs, and lymph node fusion of CT signs were compared between the observation group and the control group, indicating that CT signs were more helpful for the localization, diagnosis, and staging of lung cancer. The results of ROC curve analysis showed that the AUC value of low-dose CT combined with serum ProGrp, CEA, CA211, and NSE was 0.892, sensitivity was 96.21%, and specificity of 90.05%, which were significantly higher than those of the single tests, respectively. The positive likelihood ratio was 84.41% and the negative likelihood ratio was 87.11%. Conclusion: The combination of CT signs and serum tumour markers helps to improve the detection rate, sensitivity, and specificity of lung cancer, which has a high diagnostic rate for lung cancer and may provide evidence for the early diagnosis of lung cancer.

The role of ChatGPT in scientific communication: writing better scientific review articles.

Artificial intelligence tools represent an exciting opportunity for scientists to streamline their research and write impactful articles. Using artificial intelligence tools like ChatGPT can greatly improve writing review articles for scientists, by enhancing efficiency and quality. ChatGPT speeds up writing, develops outlines, adds details, and helps improve writing style. However, ChatGPT's limitations must be kept in mind, and generated text must be reviewed and edited to avoid plagiarism and fabrication. Despite these limitations, ChatGPT is a powerful tool that allows scientists to focus on analyzing and interpreting literature reviews. Embracing these tools can help scientists produce meaningful research in a more efficient and effective manner, however caution must be taken and unchecked use of ChatGPT in writing should be avoided.

Relationships of adiponectin to regional adiposity, insulin sensitivity, serum lipids, and inflammatory markers in sedentary and endurance-trained Japanese young women.

Introduction: This study aims to compare the differences in circulating adiponectin levels and their relationships to regional adiposity, insulin resistance, serum lipid, and inflammatory factors in young, healthy Japanese women with different physical activity statuses. Methods: Adipokines (adiponectin and leptin), full serum lipid, and inflammatory factors [white blood cell counts, C-reactive protein, tumor necrosis factor-α, tissue plasminogen activator inhibitor-1 (PAI-1)] were measured in 101 sedentary and 100 endurance-trained healthy Japanese women (aged 18-23 years). Insulin sensitivity was obtained through a quantitative insulin-sensitivity check index (QUICKI). Regional adiposity [trunk fat mass (TFM), lower-body fat mass (LFM), and arm fat mass (AFM)] was evaluated using the dual-energy X-ray absorptiometry method. Results: No significant difference was observed between the sedentary and trained women in terms of adiponectin levels. The LFM-to-TFM ratio and the high-density lipoprotein cholesterol (HDL-C) were the strong positive determinants for adiponectin in both groups. Triglyceride in the sedentary women was closely and negatively associated with adiponectin, as well as PAI-1 in the trained women. The QUICKI level was higher in the trained than sedentary women. However, no significant correlation between adiponectin and insulin sensitivity was detected in both groups. Furthermore, LFM was associated with a favorable lipid profile against cardiovascular diseases (CVDs) in the whole study cohort, but this association became insignificant when adiponectin was taken into account. Conclusions: These findings suggest that adiponectin is primarily associated with regional adiposity and HDL-C regardless of insulin sensitivity and physical activity status in young, healthy women. The associations among adiponectin, lipid, and inflammatory factors are likely different in women with different physical activity statuses. The correlation of LFM and a favorable lipid profile against CVD and adiponectin is likely involved in this association.