Application of Hip Pericapsular Nerve Block Combined With Spinal Anesthesia in the Treatment of Elderly Patients With Femoral Intertrochanteric Fracture.

OBJECTIVE: To investigate the effect of pericapsular nerve group (PENG) block combined with spinal anesthesia in the treatment of elderly patients with intertrochanteric fractures through "rapid diagnosis and treatment channel" PFNA internal fixation. METHODS: 52 elderly patients were randomly divided into the observation group (26 patients, PENG block combined with spinal anesthesia) and the control group (26 patients, spinal anesthesia alone). The general health, mean arterial pressure (MAP), and heart rate (HR) of both groups were compared at various stages: immediately before the administration of pain analgesia, during the positioning of spinal epidural anesthesia, at the beginning and end of the surgery, and 2 hours after surgery. Additionally, VAS scores at rest and during passive straight leg elevation by 15° were evaluated at 12 hours, 24 hours, 48 hours, 72 hours, and 7 days after surgery. RESULTS: The MAP and HR in the observation group under spinal anesthesia in the lateral position were lower than those in the control group (P < 0.05). Additionally, the VAS scores of the observation group during positioning and at 12 hours and 24 hours after surgery were lower than those in the control group under spinal epidural anesthesia (both P < 0.05). CONCLUSION: The application of ultrasound-guided PENG block combined with lumbar anesthesia can reduce pain when in lateral position, stabilize perioperative vital signs, and result in high satisfaction.

Machine learning survival prediction using tumor lipid metabolism genes for osteosarcoma.

Osteosarcoma is a primary malignant tumor that commonly affects children and adolescents, with a poor prognosis. The existence of tumor heterogeneity leads to different molecular subtypes and survival outcomes. Recently, lipid metabolism has been identified as a critical characteristic of cancer. Therefore, our study aims to identify osteosarcoma's lipid metabolism molecular subtype and develop a signature for survival outcome prediction. Four multicenter cohorts-TARGET-OS, GSE21257, GSE39058, and GSE16091-were amalgamated into a unified Meta-Cohort. Through consensus clustering, novel molecular subtypes within Meta-Cohort patients were delineated. Subsequent feature selection processes, encompassing analyses of differentially expressed genes between subtypes, univariate Cox analysis, and StepAIC, were employed to pinpoint biomarkers related to lipid metabolism in TARGET-OS. We selected the most effective algorithm for constructing a Lipid Metabolism-Related Signature (LMRS) by utilizing four machine-learning algorithms reconfigured into ten unique combinations. This selection was based on achieving the highest concordance index (C-index) in the test cohort of GSE21257, GSE39058, and GSE16091. We identified two distinct lipid metabolism molecular subtypes in osteosarcoma patients, C1 and C2, with significantly different survival rates. C1 is characterized by increased cholesterol, fatty acid synthesis, and ketone metabolism. In contrast, C2 focuses on steroid hormone biosynthesis, arachidonic acid, and glycerolipid and linoleic acid metabolism. Feature selection in the TARGET-OS identified 12 lipid metabolism genes, leading to a model predicting osteosarcoma patient survival. The LMRS, based on the 12 identified genes, consistently accurately predicted prognosis across TARGET-OS, testing cohorts, and Meta-Cohort. Incorporating 12 published signatures, LMRS showed robust and significantly superior predictive capability. Our results offer a promising tool to enhance the clinical management of osteosarcoma, potentially leading to improved clinical outcomes.

Overexpression of COX7A1 Promotes the Resistance of Gastric Cancer to Oxaliplatin and Weakens the Efficacy of Immunotherapy.

Gastric cancer (GC) is one of the most common clinical malignant tumors worldwide, with high morbidity and mortality. Presently, the overall response rate to immunotherapy is low, and current methods for predicting the prognosis of GC are not optimal. Therefore, novel biomarkers with accuracy, efficiency, stability, performance ratio and wide clinical application are needed. Based on public data sets, the Chemotherapy Cohort and the Immunotherapy Cohort from Sun Yat-sen University Cancer Center, a series of bioinformatics analyses, such as differential expression analysis, survival analysis, drug sensitivity prediction, enrichment analysis, tumor immune dysfunction and exclusion (TIDE) analysis, single-sample gene set enrichment analysis (ssGSEA), stemness index calculation, immune cell infiltration analysis, were performed for screening and preliminary exploration. Immunohistochemical staining and in vitro experiments were performed for further verification. Overexpression of COX7A1 promoted the resistance of GC cells to Oxaliplatin. COX7A1 may induce immune escape by regulating the number of fibroblasts and their cellular communication with immune cells. In summary, measuring the expression levels of COX7A1 in the clinic may be useful to predict the prognosis of GC patients, the degree of chemotherapy resistance and the efficacy of immunotherapy.

Transcriptomic analysis and experiments revealed that remimazolam promotes proliferation and G1/S transition in HCT8 cells.

Background: Remimazolam is a new ultrashort-acting benzodiazepine for sedation and anesthesia. The effects of remimazolam and the mechanism by which it functions in cancer cells have not been determined. This research aimed to explore the mechanism of remimazolam action in colon cancer treatment, using bioinformatics analysis and in vitro experiments. Methods: Cell cycle progression, colony formation, self-renewal capacity, and apoptosis detection were performed in HCT8 cells treated with or without remimazolam. Transcriptome sequencing, Gene Ontology, Kyoto Encyclopedia of Genes and Genome, Protein-Protein Interaction, Gene Set Enrichment Analysis, Western blotting, and qPCR were performed to investigate the mechanism of action of remimazolam in HCT8 colon cancer cells. Results: Remimazolam promoted proliferation and cell-cycle progression of HCT8 cells. After remimazolam treatment, a total of 1,096 differentially expressed genes (DEGs) were identified: 673 genes were downregulated, and 423 genes were upregulated. The DEGs were enriched mainly in "DNA replication", "cell cycle", and "G1/S transition" related pathways. There were 15 DEGs verified by qPCR, and representative biomarkers were detected by Western Bloting. The remimazolam-mediated promotion of cell proliferation and cell cycle was reversed by G1T28, a CDK4/6 inhibitor. Conclusion: Remimazolam promoted cell-cycle progression and proliferation in HCT8 colon cancer cells, indicating that the long-term use of remimazolam has potential adverse effects in the anesthesia of patients with colon cancer.

Efficacy and safety of EGFR-TKI combined with WBRT vs. WBRT alone in the treatment of brain metastases from NSCLC: a systematic review and meta-analysis.

Background: The efficacy and safety of combining epidermal growth factor receptor tyrosine kinase inhibitors (EGFR-TKIs) with whole-brain radiotherapy (WBRT) for treating brain metastases in non-small cell lung cancer patients remains to be determined. Methods: A systematic search was conducted using databases including PubMed, Embase, Web of Science, Cochrane, Wanfang, and China National Knowledge Infrastructure (CNKI), aiming to identify relevant clinical studies on the treatment of brain metastases originating from non-small cell lung cancer through the combination of EGFR-TKI and WBRT. Statistical analysis was performed utilizing Stata 17.0 software, covering clinical studies published until March 1, 2023. Results: This analysis incorporated 23 randomized controlled trials (RCTs), involving a total of 2,025 patients. Of these, 1,011 were allocated to the group receiving both EGFR-TKI and WBRT, while 1,014 were assigned to the WBRT alone group. The findings reveal that the combination of EGFR-TKI and WBRT significantly improves the intracranial objective remission rate (RR = 1.57, 95% CI: 1.42-1.74, p < 0.001), increases the intracranial disease control rate (RR = 1.30, 95% CI: 1.23-1.37, p < 0.001), and enhances the 1-year survival rate (RR = 1.48, 95% CI: 1.26-1.73, p < 0.001). Additionally, this combined treatment was associated with a significant survival advantage (RR = 1.48, 95% CI: 1.26-1.73, p < 0.001) and a reduced incidence of adverse effects (RR = 0.65, 95% CI: 0.51-0.83, p < 0.001), particularly with respect to nausea and vomiting (RR = 0.54, 95% CI: 0.37-0.81, p = 0.002) and myelosuppression (RR = 0.59, 95% CI: 0.40-0.87, p = 0.008). However, no statistically significant differences were observed for diarrhea (RR = 1.15, 95% CI: 0.82-1.62, p = 0.418), and skin rash (RR = 1.35, 95% CI: 0.88-2.07, p = 0.164). Conclusion: In contrast to WBRT alone, the combination of EGFR-TKI and WBRT significantly improves intracranial response, enhancing the objective response rate, disease control rate, and 1-year survival rate in NSCLC patients with brain metastases. Moreover, aside from mild cases of rash and diarrhea, there is no statistically significant increase in the incidence of additional adverse effects. Based on the comprehensive evidence collected, the use of third-generation EGFR-TKI combined with WBRT is recommended as the preferred treatment for NSCLC patients with brain metastases, offering superior management of metastatic brain lesions. Systematic review registration: https://www.crd.york.ac.uk/PROSPERO/#, CRD42023415566.

Single-cell and bulk RNA-sequence identified fibroblasts signature and CD8+ T-cell - fibroblast subtype predicting prognosis and immune therapeutic response of bladder cancer, based on machine-learning bioinformatics retrospective study.

BACKGROUND: Cancer-associated fibroblasts (CAFs) are found in primary and advanced tumours. They are primarily involved in tumour progression through complex mechanisms with other types of cells in the tumour microenvironment. However, essential fibroblasts-related genes (FRG) in bladder cancer still need to be explored, and there is a shortage of an ideal predictive model or molecular subtype for the progression and immune therapeutic assessment for bladder cancer, especially muscular-invasive bladder cancer based on the FRG. MATERIALS AND METHODS: CAF-related genes of bladder cancer were identified by analyzing single-cell RNA sequence datasets, and bulk transcriptome datasets and gene signatures were used to characterize them. Then, ten types of machine learning algorithms were utilized to determine the hallmark FRG and construct the FRG index (FRGI) and subtypes. Further molecular subtypes combined with CD8+ T-cells were established to predict the prognosis and immune therapy response. RESULTS: 54 BLCA-related FRG were screened by large-scale scRNA-sequence datasets. The machine learning algorithm established a 3-genes FRG index (FRGI). High FRGI represented a worse outcome. Then, FRGI combined clinical variables to construct a nomogram, which shows high predictive performance for the prognosis of bladder cancer. Furthermore, the BLCA datasets were separated into two subtypes - fibroblast hot and cold types. In five independent BLCA cohorts, the fibroblast hot type showed worse outcomes than the cold type. Multiple cancer-related hallmark pathways are distinctively enriched in these two types. In addition, high FRGI or fibroblast hot type shows a worse immune therapeutic response. Then, four subtypes called CD8-FRG subtypes were established under the combination of FRG signature and activity of CD8+ T-cells, which turned out to be effective in predicting the prognosis and immune therapeutic response of bladder cancer in multiple independent datasets. Pathway enrichment analysis, multiple gene signatures, and epigenetic alteration characterize the CD8-FRG subtypes and provide a potential combination strategy method against bladder cancer. CONCLUSIONS: In summary, we established a novel FRGI and CD8-FRG subtype by large-scale datasets and organized analyses, which could accurately predict clinical outcomes and immune therapeutic response of BLCA after surgery.

Desertivirga arenae gen. nov., sp. nov. and Desertivirga brevis sp. nov., isolated from desert soil, and reclassification of Pedobacter xinjiangensis as Desertivirga xinjiangensis comb. nov. and Pedobacter mongoliensis as Paradesertivirga mongoliensis gen.nov., comb. nov.

Two novel bacterial strains, designated as SYSU D00823T and SYSU D00873T, were isolated from sandy soil of the Gurbantunggut Desert in Xinjiang, north-west China. SYSU D00823T and SYSU D00873T shared 99.0 % 16S rRNA gene sequence identity, and were both most closely related to Pedobacter xinjiangensis 12157T with 96.1 % and 96.0 % similarities, respectively. Phylogenetic and phylogenomic analyses revealed that the two isolates and P. xinjiangensis 12157T formed a separate distinct cluster in a stable subclade with the nearby species Pedobacter mongoliensis 1-32T, as well as the genera Pararcticibacter and Arcticibacter. Furthermore, P. mongoliensis 1-32T formed a separate deep-branching lineage and did not form a cluster with members of the genus Pedobacter. The average nucleotide identity and digital DNA-DNA hybridization values between SYSU D00823T and SYSU D00873T and related species were well below the thresholds for species delineation (<81.0 % and <24.0 %, respectively). The genomes of SYSU D00823T and SYSU D00873T were 6.19 and 6.43 Mbp in size with 40.4 % and 40.5 % DNA G+C contents, respectively. The predominant fatty acids (>10 %) of SYSU D00823T and SYSU D00873T were iso-C15 : 0, iso-C17 : 0 3-OH and summed feature 3 (C16 : 1 ω7c and/or C16 : 1 ω6c). Menaquinone-7 was the only respiratory quinone. The major polar lipids were phosphatidylethanolamine, glycosphingolipid, aminoglycolipid/glycolipid, aminophospholipid and three or four unidentified polar lipids. These data indicated that strains SYSU D00823T and SYSU D00873T should be assigned to two novel species of a new genus within the family Sphingobacteriaceae, for which the names Desertivirga arenae gen. nov., sp. nov. and Desertivirga brevis sp. nov. are proposed. The type strains are SYSU D00823T (=CGMCC 1.18630T=MCCC 1K04973T=KCTC 82278T) and SYSU D00873T (=CGMCC 1.18629T=MCCC 1K04974T=KCTC 82281T), respectively. Accordingly, the reclassification of P. xinjiangensis as Desertivirga xinjiangensis comb. nov., and P. mongoliensis as Paradesertivirga mongoliensis gen. nov., comb. nov. are also proposed.

Mammographic texture versus conventional Cumulus measure of density in breast cancer risk prediction: a literature review.

Mammographic textures show promise as breast cancer risk predictors, distinct from mammographic density. Yet, it lacks comprehensive evidence to determine the stronger risk predictor between textures and density, and the reliability of texture-based measures. We searched PubMed database for research publications, published up to November 2023, which assessed breast cancer risk associations(odds ratios[OR]) with texture-based measures and percent mammographic density(PMD), and their discrimination(area under the receiver operating characteristics curve[AUC]), using same datasets. Of 11 publications, for textures, six found stronger associations(P<0.05) with 11%-508% increases on log scale by study and four found weaker associations(P<0.05) with 14%-100% decreases, compared with PMD. Risk associations remained significant when fitting textures and PMD together. Eleven of 17 publications show greater AUCs for textures than PMD(P<0.05); increases were 0.04-0.25 by study. Discrimination of PMD and these textures jointly was significantly higher than PMD alone (P<0.05). Therefore, different textures could capture distinct breast cancer risk information, partially independent of mammographic density, suggesting their joint role in breast cancer risk prediction. Certain textures could outperform mammographic density for predicting breast cancer risk. However, obtaining reliable texture-based measures necessitates addressing various issues. Collaboration of researchers from diverse fields could be beneficial for advancing this complex field.

Impact of bariatric surgery on carotid intima-media thickness and arterial stiffness in metabolically healthy obesity: a prospective study.

PURPOSE: Cardiovascular disease is one of the leading causes of mortality in patients with obesity. Metabolically healthy obesity (MHO), in which people do not have metabolic disorders, is a transient state of obesity. However, over the long term, a proportion of individuals with MHO develop metabolic syndrome (MetS). We aimed to investigate the effect of substantial weight loss following bariatric surgery in MHO on carotid intima-media thickness (CIMT) and pulse-wave velocity (PWV), which are independent predictors of subclinical atherosclerosis. METHODS: This prospective study included 38 patients (34 women, four men) undergoing bariatric surgery who had severe obesity but without comorbidities (hypertension, diabetes, and hyperlipidemia), and 28 control individuals who were matched for age and sex. CIMT and PWV of the left common carotid artery were measured. At 12-month follow-up after bariatric surgery, measurements were repeated in the 38 patients with obesity. RESULTS: Mean baseline body mass index (BMI) in the MHO group was 40.55 ± 3.59 kg/m2, which decreased by 33.1% after bariatric surgery. Compared with controls, CIMT and PWV were increased in MHO (543.53 ± 55.29 vs. 407.82 ± 53.09 µm, 6.70 ± 1.22 vs. 5.45 ± 0.74 m/s, respectively; all P < 0.001). At 12 months post-bariatric surgery, CIMT in MHO was lower than baseline (466.79 ± 53.74 vs. 543.53 ± 55.29 µm, P = 0.009), but PWV was not significantly different from baseline (6.27 ± 0.86 vs. 6.70 ± 1.22 m/s, P = 0.132). Multivariate regression showed that BMI was an independent predictor of CIMT (ß = 0.531, P < 0.001). CONCLUSION: Carotid artery structure and function were impaired in MHO, and improved carotid artery structure was associated with weight loss in MHO after bariatric surgery.

Breast Cancer Risk for Women With a TP53 Pathogenic Variant.

The article highlights that breast cancer risk for women with TP53 mutations depends on family history.

Development of erythrina-based PARP-1/FTase dual-target inhibitors against lung cancer epithelial-mesenchymal transition (EMT) in vivo and in vitro.

A novel series of erythrina derivatives as PARP-1/FTase inhibitors were synthesized, and evaluated for their biological activities. Compound T9 had excellent inhibitory effects on cell viability (A549: IC50 = 1.74 µM; A549/5-Fu: IC50 = 1.03 µM) and in vitro enzyme activities (PARP-1: IC50 = 0.40 µM; FTase: IC50 = 0.067 µM). Molecular docking and point mutation assays demonstrated the interaction of compound T9 with key amino acid residues. The compound T9 exhibited potent anti-proliferation and anti-migration capabilities against A549 and A549/5-Fu cells. PCR array and western blot results showed that compound T9 could effectively inhibit EMT-related proteins in A549 and A549/5-Fu cells, thereby inhibiting the development of lung cancer. Importantly, compound T9 could significantly inhibit tumor growth in the A549 xenograft tumor model (TGI = 65.3 %). In conclusion, this study was the first presentation of the concept of dual-target inhibitors of the PARP-1/FTase enzymes. It also provides the basis for further research and development of novel PARP-1/FTase inhibitors.

Variability in the prevalence of depression among adults with chronic pain: UK Biobank analysis through clinical prediction models.

BACKGROUND: The prevalence of depression among people with chronic pain remains unclear due to the heterogeneity of study samples and definitions of depression. We aimed to identify sources of variation in the prevalence of depression among people with chronic pain and generate clinical prediction models to estimate the probability of depression among individuals with chronic pain. METHODS: Participants were from the UK Biobank. The primary outcome was a "lifetime" history of depression. The model's performance was evaluated using discrimination (optimism-corrected C statistic) and calibration (calibration plot). RESULTS: Analyses included 24,405 patients with chronic pain (mean age 64.1 years). Among participants with chronic widespread pain, the prevalence of having a "lifetime" history of depression was 45.7% and varied (25.0-66.7%) depending on patient characteristics. The final clinical prediction model (optimism-corrected C statistic: 0.66; good calibration on the calibration plot) included age, BMI, smoking status, physical activity, socioeconomic status, gender, history of asthma, history of heart failure, and history of peripheral artery disease. Among participants with chronic regional pain, the prevalence of having a "lifetime" history of depression was 30.2% and varied (21.4-70.6%) depending on patient characteristics. The final clinical prediction model (optimism-corrected C statistic: 0.65; good calibration on the calibration plot) included age, gender, nature of pain, smoking status, regular opioid use, history of asthma, pain location that bothers you most, and BMI. CONCLUSIONS: There was substantial variability in the prevalence of depression among patients with chronic pain. Clinically relevant factors were selected to develop prediction models. Clinicians can use these models to assess patients' treatment needs. These predictors are convenient to collect during daily practice, making it easy for busy clinicians to use them.

Study on mNGS Technique in Diagnosing Pneumocystis jirovecii Pneumonia in Non-HIV-Infected Patients.

Objective: To investigate the value of metagenomic Next-Generation Sequencing (mNGS) in diagnosing Pneumocystis jirovecii pneumonia (PJP) in non-human immunodeficiency virus (HIV)-infected patients. Methods: In this retrospective study, non-HIV-infected patients with PJP and those diagnosed with non-PJP from August 2022 to December 2024 were selected as subjects. The presence of Pneumocystis jirovecii (PJ) and other co-pathogens in bronchoalveolar lavage fluid (BALF) was analyzed, and the diagnostic efficacy of NGS, polymerase chain reaction (PCR) and serum 1,3-ß-D-glucan (BDG) in PJP was compared with the reference standard of clinical compound diagnosis. Results: Eighty-nine non-HIV-infected patients were recruited, with dyspnea as the primary symptom (69.66%) and solid malignant tumor as the most common underlying disease (20.22%). Taking clinical compound diagnosis as the reference standard, the sensitivity, specificity, negative predictive value and positive predictive value of mNGS were higher than those detected by PCR and serum BDG. Among 42 non-HIV-infected patients with PJP who underwent mNGS and conventional pathogen detection of BALF, 6 had simple PJ infection and 36 had combined PJ infection. The detection rate of mNGS in mixed infections was significantly higher than that of conventional pathogen detection (85.71 vs 61.70%, P = 0.012). A total of 127 pathogens were detected in BALF using mNGS, among which fungi had the highest detection rate (46.46%). The fungi, viruses and bacteria detected were mainly Pneumocystis jirovecii, human gammaherpesvirus 4 and Acinetobacter baumannii. Conclusion: mNGS is highly effective in diagnosing non-HIV-infected patients with PJP and exhibits ideal performance in the detection of co-pathogens. In addition, it has certain value for clinical diagnosis and guidance of targeted anti-infective drug treatment.

RyR2 Stabilizer Attenuates Cardiac Hypertrophy by Downregulating TNF-α/NF-κB/NLRP3 Signaling Pathway through Inhibiting Calcineurin.

The effect of Ryanodine receptor2 (RyR2) and its stabilizer on cardiac hypertrophy is not well known. C57/BL6 mice underwent transverse aortic contraction (TAC) or sham surgery were administered dantrolene, the RyR2 stabilizer, or control drug. Dantrolene significantly alleviated TAC-induced cardiac hypertrophy in mice, and RNA sequencing was performed implying calcineurin/NFAT3 and TNF-α/NF-κB/NLRP3 as critical signaling pathways. Further expression analysis and Western blot with heart tissue as well as neonatal rat cardiomyocyte (NRCM) model confirmed dantrolene decreases the activation of calcineurin/NFAT3 signaling pathway and TNF-α/NF-κB/NLRP3 signaling pathway, which was similar to FK506 and might be attenuated by calcineurin overexpression. The present study shows for the first time that RyR2 stabilizer dantrolene attenuates cardiac hypertrophy by inhibiting the calcineurin, therefore downregulating the TNF-α/NF-κB/NLRP3 pathway.

A High-Quality Sample Generation Method for Improving Steel Surface Defect Inspection.

Defect inspection is a critical task in ensuring the surface quality of steel plates. Deep neural networks have the potential to achieve excellent inspection accuracy if defect samples are sufficient. Nevertheless, it is very different to collect enough samples using cameras alone. To a certain extent, generative models can alleviate this problem but poor sample quality can greatly affect the final inspection performance. A sample generation method, which employs a generative adversarial network (GAN), is proposed to generate high-quality defect samples for training accurate inspection models. To improve generation quality, we propose a production-and-elimination, two-stage sample generation process by simulating the formation of defects on the surface of steel plates. The production stage learns to generate defects on defect-free background samples, and the elimination stage learns to erase defects on defective samples. By minimizing the differences between the samples at both stages, the proposed model can make generated background samples close to real ones while guiding the generated defect samples to be more realistic. Experimental results show that the proposed method has the ability to generate high-quality samples that can help train powerful inspection models and thereby improve inspection performance.

Arginine and its metabolites stimulate proliferation, differentiation, and physiological function of porcine trophoblast cells through ß-catenin and mTOR pathways.

Arginine, which is metabolized into ornithine, proline, and nitric oxide, plays an important role in embryonic development. The present study was conducted to investigate the molecular mechanism of arginine in proliferation, differentiation, and physiological function of porcine trophoblast cells (pTr2) through metabolic pathways. The results showed that arginine significantly increased cell viability (P < 0.05). The addition of arginine had a quadratic tendency to increase the content of progesterone (P = 0.06) and protein synthesis rate (P = 0.03), in which the maximum protein synthesis rate was observed at 0.4 mM arginine. Arginine quadratically increased (P < 0.05) the intracellular contents of spermine, spermidine and putrescine, as well as linearly increased (P < 0.05) the intracellular content of NO in a dose-dependent manner. Arginine showed a quadratic tendency to increase the content of putrescine (P = 0.07) and a linear tendency to increase NO content (P = 0.09) in cell supernatant. Moreover, increasing arginine activated (P < 0.05) the mRNA expressions for ARG, ODC, iNOS and PCNA. Furthermore, inhibitors of arginine metabolism (L-NMMA and DFMO) both inhibited cell proliferation, while addition of its metabolites (NO and putrescine) promoted the cell proliferation and cell cycle, the mRNA expressions of PCNA, EGF and IGF-1, and increased (P < 0.05) cellular protein synthesis rate, as well as estradiol and hCG secretion (P < 0.05). In conclusion, our results suggested that arginine could promote cell proliferation and physiological function by regulating the metabolic pathway. Further studies showed that arginine and its metabolites modulate cell function mainly through ß-catenin and mTOR pathways.

Clinicopathological factors predict residual lymph node metastasis in locally advanced rectal cancer with ypT0-2 after neoadjuvant chemoradiotherapy.

PURPOSE: Residual lymph node metastases (RLNM) remained a great concern in the implementation of organ-preserving strategies and led to poor prognosis in locally advanced rectal cancer (LARC). In this study, we aimed to identify the clinicopathological factors correlated with RLNM in LARC patients with ypT0-2 after neoadjuvant chemoradiotherapy (NCRT). METHODS: We retrospectively analyzed 417 patients histologically diagnosed middle-low LARC after NCRT and total mesorectal excision (TME), whose pathological staging was ypT0-2. All patients received pelvic magnetic resonance imaging (MRI) before NCRT. The radiation doses were 50-50.6 Gy for the planning gross tumor volume and 41.8-45 Gy for the planning target volume, respectively. A nomogram for predicting RLNM was constructed using a binary logistic regression. Nomogram performance was assessed by receiver operating characteristic (ROC) curve, calibration curve, decision curve analysis (DCA) and clinical impact curve (CIC). RESULTS: After surgery, 191 patients (45.8%) were ypT0, 43 patients (10.3%) were ypT1 and 183 patients (43.9%) were ypT2, and a total of 49 patients (11.8%) were found the presence of RLNM. Multivariable analyses identified MRI-defined mesorectal fascia (MRF)-positive, high-grade histopathology at biopsy, advanced ypT-category, and the presence of perineural invasion (PNI) as the predictive factors. The nomogram, incorporating all these predictors, showed good discrimination and calibration efficacy, with the areas under the ROC curve of 0.690 (95% CI: 0.610-0.771). Both DCA and CIC demonstrated that this nomogram has good clinical usefulness. CONCLUSION: The nomogram model can predict RLNM in patients with ypT0-2 tumors. It can help select suitable patients for performing organ-preserving strategies after NCRT.

Impact of the addition of dexmedetomidine to patient-controlled intravenous analgesia on postoperative pain-sleep interaction cycle and delirium: A systematic review and meta-analysis of randomized controlled trials.

Background: The reciprocal nexus between sleep and pain is well-documented, with the deleterious impact of operative trauma potentially playing a pivotal role in the dysregulation of this interplay, which could significantly contribute to the manifestation of postoperative delirium (POD). Studies have investigated the effect of adding dexmedetomidine (DEX) to patient-controlled intravenous analgesia (PCIA) pumps on postoperative pain-sleep interaction cycle and POD, but conclusions remained uncertain. The objective of this investigation is to perform a meta-analysis that thoroughly assesses the impact of integrating DEX into PCIA, focusing on analgesic effectiveness, sleep quality, and the incidence of delirium in postoperative patients. Methods: PubMed, Embase, Cochrane Library, SinoMed, and Wanfang Data Knowledge Service Platform were searched, for publications in any language, from database inception to September 2023. Our analysis encompassed randomized controlled trials (RCTs) that examine the therapeutic efficacy and risk profile of adding DEX to the PCIA on the postoperative pain-sleep interaction cycle, by focusing on changes in postoperative analgesia (Visual analog scale (VAS) score), sleep efficiency, sleep structure, subjective sleep score (Assen insomnia scale and numerical rating scale) and adverse event rate. Results: 34 RCTs (4324 patients) were analyzed. This study shows DEX improved analgesia and reduced VAS scores at 6, 12, and 24 h after surgery. Sleep efficiency was enhanced on the 1st and 2nd postoperative night. DEX improved sleep structure at the 1st postoperative night by reducing non-rapid eye movement stage 1 (N1) sleep and increasing non-rapid eye movement stage 2 (N2) and non-rapid eye movement stage 3 (N3) sleep. At the 2nd night, DEX reduced N1 sleep and increased N2 sleep, but not N3 sleep. Data from AIS and NRS showed improvement in subjective sleep scores on the 1st postoperative night and 2nd night. Additionally, DEX decreased the occurrence of POD on the 24 h and first-three days. Conclusion: This study shows that the typical DEX doses added to PCIA with sufentanil were 2-5 µg/kg or approximately 200-250 µg, and the addition of DEX to PCIA can improve pain-sleep interaction cycle from multiple perspectives, and further decrease the occurrence of POD.

Biomarkers and computational models for predicting efficacy to tumor ICI immunotherapy.

Numerous studies have shown that immune checkpoint inhibitor (ICI) immunotherapy has great potential as a cancer treatment, leading to significant clinical improvements in numerous cases. However, it benefits a minority of patients, underscoring the importance of discovering reliable biomarkers that can be used to screen for potential beneficiaries and ultimately reduce the risk of overtreatment. Our comprehensive review focuses on the latest advancements in predictive biomarkers for ICI therapy, particularly emphasizing those that enhance the efficacy of programmed cell death protein 1 (PD-1)/programmed cell death-ligand 1 (PD-L1) inhibitors and cytotoxic T-lymphocyte antigen-4 (CTLA-4) inhibitors immunotherapies. We explore biomarkers derived from various sources, including tumor cells, the tumor immune microenvironment (TIME), body fluids, gut microbes, and metabolites. Among them, tumor cells-derived biomarkers include tumor mutational burden (TMB) biomarker, tumor neoantigen burden (TNB) biomarker, microsatellite instability (MSI) biomarker, PD-L1 expression biomarker, mutated gene biomarkers in pathways, and epigenetic biomarkers. TIME-derived biomarkers include immune landscape of TIME biomarkers, inhibitory checkpoints biomarkers, and immune repertoire biomarkers. We also discuss various techniques used to detect and assess these biomarkers, detailing their respective datasets, strengths, weaknesses, and evaluative metrics. Furthermore, we present a comprehensive review of computer models for predicting the response to ICI therapy. The computer models include knowledge-based mechanistic models and data-based machine learning (ML) models. Among the knowledge-based mechanistic models are pharmacokinetic/pharmacodynamic (PK/PD) models, partial differential equation (PDE) models, signal networks-based models, quantitative systems pharmacology (QSP) models, and agent-based models (ABMs). ML models include linear regression models, logistic regression models, support vector machine (SVM)/random forest/extra trees/k-nearest neighbors (KNN) models, artificial neural network (ANN) and deep learning models. Additionally, there are hybrid models of systems biology and ML. We summarized the details of these models, outlining the datasets they utilize, their evaluation methods/metrics, and their respective strengths and limitations. By summarizing the major advances in the research on predictive biomarkers and computer models for the therapeutic effect and clinical utility of tumor ICI, we aim to assist researchers in choosing appropriate biomarkers or computer models for research exploration and help clinicians conduct precision medicine by selecting the best biomarkers.

Construction of a gene model related to the prognosis of patients with gastric cancer receiving immunotherapy and exploration of COX7A1 gene function.

BACKGROUND: GC is a highly heterogeneous tumor with different responses to immunotherapy, and the positive response depends on the unique interaction between the tumor and the tumor microenvironment (TME). However, the currently available methods for prognostic prediction are not satisfactory. Therefore, this study aims to construct a novel model that integrates relevant gene sets to predict the clinical efficacy of immunotherapy and the prognosis of GC patients based on machine learning. METHODS: Seven GC datasets were collected from the Gene Expression Omnibus (GEO) database, The Cancer Genome Atlas (TCGA) database and literature sources. Based on the immunotherapy cohort, we first obtained a list of immunotherapy related genes through differential expression analysis. Then, Cox regression analysis was applied to divide these genes with prognostic significancy into protective and risky types. Then, the Single Sample Gene Set Enrichment Analysis (ssGSEA) algorithm was used to score the two categories of gene sets separately, and the scores differences between the two gene sets were used as the basis for constructing the prognostic model. Subsequently, Weighted Correlation Network Analysis (WGCNA) and Cytoscape were applied to further screen the gene sets of the constructed model, and finally COX7A1 was selected for the exploration and prediction of the relationship between the clinical efficacy of immunotherapy for GC. The correlation between COX7A1 and immune cell infiltration, drug sensitivity scoring, and immunohistochemical staining were performed to initially understand the potential role of COX7A1 in the development and progression of GC. Finally, the differential expression of COX7A1 was verified in those GC patients receiving immunotherapy. RESULTS: First, 47 protective genes and 408 risky genes were obtained, and the ssGSEA algorithm was applied for model construction, showing good prognostic discrimination ability. In addition, the patients with high model scores showed higher TMB and MSI levels, and lower tumor heterogeneity scores. Then, it is found that the COX7A1 expressions in GC tissues were significantly lower than those in their corresponding paracancerous tissues. Meanwhile, the patients with high COX7A1 expression showed higher probability of cancer invasion, worse clinical efficacy of immunotherapy, worse overall survival (OS) and worse disease-free survival (DFS). CONCLUSIONS: The ssGSEA score we constructed can serve as a biomarker for GC patients and provide important guidance for individualized treatment. In addition, the COX7A1 gene can accurately distinguish the prognosis of GC patients and predict the clinical efficacy of immunotherapy for GC patients.