Clinical Significance and Potential Role of LSM4 Overexpression in Hepatocellular Carcinoma: An Integrated Analysis Based on Multiple Databases

Background: Hepatocellular carcinoma (HCC) is a solid tumor with high recurrence rate and high mortality. It is crucial to discover available biomarkers to achieve early diagnosis and improve the prognosis. The effect of LSM4 in HCC still remains unrevealed. Our study is dedicated to exploring the expression of LSM4 in HCC, demonstrating its clinical significance and potential molecular mechanisms.Methods: Clinical information and LSM4 expression values of HCC were obtained from Gene Expression Omnibus (GEO) and The Cancer Genome Atlas (TCGA) databases. Survival analysis and receiver operating characteristic (ROC) curve analysis were applied to evaluate the prognostic and diagnostic significance of LSM4. Calculating pooled standardized mean difference (SMD) and performing summary receiver operating characteristic (sROC) curve analysis to further determine its expression status and diagnostic significance. LSM4-related co-expressed genes (CEGs) were obtained and explored their clinical significance in HCC. LSM4-associated pathways were identified through Gene set enrichment analysis (GSEA).Results: Up-regulated LSM4 was detected in HCC tissues (SMD = 1.56, 95% CI: 1.29–1.84) and overexpressed LSM4 had excellent distinguishing ability (AUC = 0.91, 95% CI: 0.88–0.93). LSM4 was associated with clinical stage, tumor grade, and lymph node metastasis status (p < 0.05). Survival analysis showed that high LSM4 expression was related to poor overall survival (OS) of HCC patients. Cox regression analysis suggested that high LSM4 expression may be an independent risk factor for HCC. We obtained nine up-regulated CEGs of LSM4 in HCC tissues, and six CEGs had good prognostic and diagnostic significance. GSEA analysis showed that up-regulated LSM4 was closely related to the cell cycle, cell replication, focal adhesion, and several metabolism-associated pathways, including fatty acid metabolism.Conclusion: Overexpressed LSM4 may serve as a promising diagnostic and prognostic biomarker of HCC. Besides, LSM4 may play a synergistic effect with CEGs in promoting the growth and metastasis of HCC cells via regulating crucial pathways such as cell cycle, focal adhesion, and metabolism-associated pathways.

The Use of Artificial Neural Networks for the Prediction of Surgical Site Infection Following TKA

This is a retrospective study. Surgical site infection (SSI) is associated with adverse postoperative outcomes following total knee arthroplasty (TKA). However, accurately predicting SSI remains a clinical challenge due to the multitude of patient and surgical factors associated with SSI. This study aimed to develop and validate machine learning models for the prediction of SSI following primary TKA. This is a retrospective study for patients who underwent primary TKA. Chart review was performed to identify patients with superficial or deep SSIs, defined in concordance with the criteria of the Musculoskeletal Infection Society. All patients had a minimum follow-up of 2 years (range: 2.1–4.7 years). Five machine learning algorithms were developed to predict this outcome, and model assessment was performed by discrimination, calibration, and decision curve analysis. A total of 10,021 consecutive primary TKA patients was included in this study. At an average follow-up of 2.8 ± 1.1 years, SSIs were reported in 404 (4.0%) TKA patients, including 223 superficial SSIs and 181 deep SSIs. The neural network model achieved the best performance across discrimination (area under the receiver operating characteristic curve = 0.84), calibration, and decision curve analysis. The strongest predictors of the occurrence of SSI following primary TKA, in order, were Charlson comorbidity index, obesity (BMI >30 kg/m2), and smoking. The neural network model presented in this study represents an accurate method to predict patient-specific superficial and deep SSIs following primary TKA, which may be employed to assist in clinical decision-making to optimize outcomes in at-risk patients.

Clinical significance of quantitative bone SPECT/CT in the evaluation of hand and wrist pain in patients with rheumatic disease

We investigated the diagnostic value of the maximum standardized uptake value (SUV) at hand and wrist joints for differentiating rheumatic diseases via bone single-photon emission computed tomography (SPECT)/computed tomography (CT). A total of 84 patients manifesting hand and wrist pain (58 women; age, 49.8 ± 15.4 years) were finally diagnosed with rheumatoid arthritis (RA, n = 42), osteoarthritis (OA, n = 16), fibromyalgia (FM, n = 2), and other rheumatic diseases (n = 24). The SUV of each patient was measured in 32 joints including the distal interphalangeal (DIP), proximal interphalangeal (PIP), metacarpophalangeal (MCP), and wrist joints bilaterally. Differences in pain and SUVs between specific rheumatic diseases were assessed using the chi-squared test or one-way analysis of variance. Using the highest SUV (hSUV) in each patient, the diagnostic performance in differentiating specific diseases was evaluated by receiver operating characteristic (ROC) curve analysis. Pain symptoms were present in 886 (33.0%) sites in a total of 2688 joints. In four joint groups (DIP, PIP, MCP, and wrist), the SUVs of joints with pain were significantly higher than those of pain-free joints (all P < 0.001). Active joint sites with higher SUVs than the median value of each joint group were the most common in RA (55.1%). RA showed the greatest hSUV in the PIP (3.0 ± 2.4), MCP (3.5 ± 3.4), and wrist (3.3 ± 1.9) joint groups. FM was characterized by the lowest hSUV of all joint groups. In ROC curve analysis, the cumulative hSUV of the PIP, MCP, and wrist joint groups showed good performance for evaluating RA (area under the curve (AUC), 0.668; P = 0.005). The summation of the hSUVs at all joint groups had an excellent predictive performance for FM (AUC, 0.878; P < 0.001). Consequently, the arthritic activity of the hand and wrist joints based on SUV differed according to specific rheumatic diseases. Quantitative SPECT/CT may provide objective information related to arthritic activity for differentiating specific rheumatic diseases.

Gray matter volume alterations in patients with strabismus and amblyopia: voxel-based morphometry study

This study proposes the use of the voxel-based morphometry (VBM) technique to investigate structural alterations of the cerebral cortex in patients with strabismus and amblyopia (SA). Sixteen patients with SA and sixteen healthy controls (HCs) underwent magnetic resonance imaging. Original whole brain images were analyzed using the VBM method. Pearson correlation analysis was performed to evaluate the relationship between mean gray matter volume (GMV) and clinical manifestations. Receiver operating characteristic (ROC) curve analysis was applied to classify the mean GMV values of the SA group and HCs. Compared with the HCs, GMV values in the SA group showed a significant difference in the right superior temporal gyrus, posterior and anterior lobes of the cerebellum, bilateral parahippocampal gyrus, and left anterior cingulate cortex. The mean GMV value in the right superior temporal gyrus, posterior and anterior lobes of the cerebellum, and bilateral parahippocampal gyrus were negatively correlated with the angle of strabismus. The ROC curve analysis of each cerebral region confirmed the accuracy of the area under the curve. Patients with SA have reduced GMV values in some brain regions. These findings might help to reveal the potential pathogenesis of SA and its relationship with the atrophy of specific regions of the brain.

The effect of BKV reactivation on cytokines behavior in kidney transplanted patients

Background BK virus associated nephropathy (BKVAN) is one of the common causes of graft loss among kidney transplanted recipients (KTRs). The current treatment for BKV nephropathy is decreasing the immunosuppressive regimen in KTRs. Interleukin-27 (IL-27) is a multifunctional cytokine that might be the front-runner of an important pathway in this regard. Therefore, in current study it is tried to evaluate the changes in the expression level of IL-27 and some related molecules, resulting from BKV reactivation in KTR patients. Methods EDTA-treated blood samples were collected from all participants. Patients were divided into two groups, 31 kidney transplant recipients with active and 32 inactive BKV infection, after being monitored by Real time PCR (Taq-Man) in plasma. Total of 30 normal individuals were considered as healthy control group. Real time PCR (SYBR Green) technique is used to determine the expression level of studied genes. Results The results of gene expression comparisons showed that the expression level of IL-27, IFN-γ, TNF-α, TNFR2 and IRF7 genes was significantly higher in inactive group in comparison to active group. The expression level of TLR4 was lower in both active and inactive groups in comparison to control group. ROC curve analysis showed that IL-27 and IRF7 are significantly different amongst other studied genes. Finally, the analyses revealed that the expression level of most of the studied genes (except for TNF-α and TLR4) have significant correlation with viral load. Conclusions Our findings revealed that IL-27, IFN-γ, TNF-α, TNFR2 and IRF7 expression level is higher in inactive group and TLR4 expression level is lower in patients’ groups in comparison to control group. Also, ROC curve analysis showed IL-27 and IRF7 can significantly differentiate studied groups (BKV active vs. inactive). Therefore, these results might help elucidating the pattern in charge of BKV reactivation in kidney transplanted patients.

Prediction of long-term mortality by using machine learning models in Chinese patients with connective tissue disease-associated interstitial lung disease

Background The exact risk assessment is crucial for the management of connective tissue disease-associated interstitial lung disease (CTD-ILD) patients. In the present study, we develop a nomogram to predict 3‑ and 5-year mortality by using machine learning approach and test the ILD-GAP model in Chinese CTD-ILD patients. Methods CTD-ILD patients who were diagnosed and treated at the First Affiliated Hospital of Zhengzhou University were enrolled based on a prior well-designed criterion between February 2011 and July 2018. Cox regression with the least absolute shrinkage and selection operator (LASSO) was used to screen out the predictors and generate a nomogram. Internal validation was performed using bootstrap resampling. Then, the nomogram and ILD-GAP model were assessed via likelihood ratio testing, Harrell’s C index, integrated discrimination improvement (IDI), the net reclassification improvement (NRI) and decision curve analysis. Results A total of 675 consecutive CTD-ILD patients were enrolled in this study, during the median follow-up period of 50 (interquartile range, 38–65) months, 158 patients died (mortality rate 23.4%). After feature selection, 9 variables were identified: age, rheumatoid arthritis, lung diffusing capacity for carbon monoxide, right ventricular diameter, right atrial area, honeycombing, immunosuppressive agents, aspartate transaminase and albumin. A predictive nomogram was generated by integrating these variables, which provided better mortality estimates than ILD-GAP model based on the likelihood ratio testing, Harrell’s C index (0.767 and 0.652 respectively) and calibration plots. Application of the nomogram resulted in an improved IDI (3- and 5-year, 0.137 and 0.136 respectively) and NRI (3- and 5-year, 0.294 and 0.325 respectively) compared with ILD-GAP model. In addition, the nomogram was more clinically useful revealed by decision curve analysis. Conclusions The results from our study prove that the ILD-GAP model may exhibit an inapplicable role in predicting mortality risk in Chinese CTD-ILD patients. The nomogram we developed performed well in predicting 3‑ and 5-year mortality risk of Chinese CTD-ILD patients, but further studies and external validation will be required to determine the clinical usefulness of the nomogram.

Non-Invasive Assessment Of Liver Fibrosis In Patients With Non-Alcoholic Fatty Liver Disease In A Tertiary Care Hospital

Aims :To compare the NAFLD fibrosis score and FIBROSIS 4 score to fibroscan, and affirm whether the scores shall be used as a screening tool for liver fibrosis, in place of fibroscan. Methodology: It was a cross-sectional study. Patients with fatty liver on ultrasonological examination with 200 sample size. After obtaining the informed consent the following details were collected socio-demographic details, history, co-morbidities, anthropometric measurements, Laboratory investigations. Results: the ROC curve analysis of fibroscan reveals the area under curve of 0.499 and based on the cut off value of 4.50Kpas the sensitivity and specificity was found to be 85.7% and 83.5% respectively. The ROC curve analysis of fibrosis-4 reveals the area under curve of 0.495 and based on the cut off value of 0.80 the sensitivity and specificity was found to be 91.9% and 92.1% respectively. Analysis of NAFLD fibrosis score reveals the area under curve of 0.476 and based on the cut off value of -1.53 the sensitivity and specificity was found to be 93.1% and 93.9% respectively. Conclusion: Henceforth the study suggests that NAFLD fibrosis score shall be used as a non -invasive bedside assessment of liver fibrosis in high risk population and hence guiding their follow up for prevention of morbidity in resource limited settings.