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.

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.

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.

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.

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.

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.

Determination of a DNA repair-related gene signature with potential implications for prognosis and therapeutic response in pancreatic adenocarcinoma.

Background: Pancreatic adenocarcinoma (PAAD) is one of the leading causes of cancer death worldwide. Alterations in DNA repair-related genes (DRGs) are observed in a variety of cancers and have been shown to affect the development and treatment of cancers. The aim of this study was to develop a DRG-related signature for predicting prognosis and therapeutic response in PAAD. Methods: We constructed a DRG signature using least absolute shrinkage and selection operator (LASSO) Cox regression analysis in the TCGA training set. GEO datasets were used as the validation set. A predictive nomogram was constructed based on multivariate Cox regression. Calibration curve and decision curve analysis (DCA) were applied to validate the performance of the nomogram. The CIBERSORT and ssGSEA algorithms were utilized to explore the relationship between the prognostic signature and immune cell infiltration. The pRRophetic algorithm was used to estimate sensitivity to chemotherapeutic agents. The CellMiner database and PAAD cell lines were used to investigate the relationship between DRG expression and therapeutic response. Results: We developed a DRG signature consisting of three DRGs (RECQL, POLQ, and RAD17) that can predict prognosis in PAAD patients. A prognostic nomogram combining the risk score and clinical factors was developed for prognostic prediction. The DCA curve and the calibration curve demonstrated that the nomogram has a higher net benefit than the risk score and TNM staging system. Immune infiltration analysis demonstrated that the risk score was positively correlated with the proportions of activated NK cells and monocytes. Drug sensitivity analysis indicated that the signature has potential predictive value for chemotherapy. Analyses utilizing the CellMiner database showed that RAD17 expression is correlated with oxaliplatin. The dynamic changes in three DRGs in response to oxaliplatin were examined by RT-qPCR, and the results show that RAD17 is upregulated in response to oxaliplatin in PAAD cell lines. Conclusion: We constructed and validated a novel DRG signature for prediction of the prognosis and drug sensitivity of patients with PAAD. Our study provides a theoretical basis for further unraveling the molecular pathogenesis of PAAD and helps clinicians tailor systemic therapies within the framework of individualized treatment.

Exenatide improves hepatocyte insulin resistance induced by different regional adipose tissue.

Obesity is resulted from energy surplus and is characterized by abnormal adipose tissue accumulation and/or distribution. Adipokines secreted by different regional adipose tissue can induce changes in key proteins of the insulin signaling pathway in hepatocytes and result in impaired hepatic glucose metabolism. This study aimed to investigate whether exenatide affects key proteins of IRS2/PI3K/Akt2 signaling pathway in hepatocytes altered by the different regional fat depots. Six non-obese patients without endocrine diseases were selected as the research subjects. Their subcutaneous adipose tissue (SAT) and visceral adipose tissue (VAT)were co-cultured with HepG2 cells in the transwell chamber. In the presence or absence of exenatide, adipokines content in the supernatant of each experimental group was detected by ELISA. In addition, HepG2 cells in each co-culture group with and without insulin were collected, and the expression of key proteins IRS2, p-IRS2(S731), PI3K-p85, Akt2, and p-Akt2(S473) was detected by western blotting (WB). The results showed that the adipokines IL-8, MCP-1, VEGF, and sTNFR2 in the supernatant of HepG2 cells induced by different regional adipose tissue were significantly higher than those in the HepG2 group, and VAT released more adipokines than SAT. Furthermore, these adipokines were significantly inhibited by exenatide. Importantly, the different regional fat depot affects the IRS2/PI3K/Akt2 insulin signaling pathway of hepatocytes. Exenatide can up-regulate the expression of hepatocyte proteins IRS2, PI3K-p85, p-Akt2(S731) inhibited by adipose tissue, and down-regulate the expression of hepatocyte proteins p-IRS2(S731) promoted by adipose tissue. The effect of VAT on the expression of these key proteins in hepatocytes is more significant than that of SAT. But there was no statistical difference in the expression of Akt2 protein among each experimental group, suggesting that exenatide has no influence on the expression of Akt2 protein in hepatocytes. In conclusion, exenatide may improve hepatic insulin resistance (IR) by inhibiting adipokines and regulating the expression of key proteins in the IRS2/PI3K/Akt2 pathway.

Optical coherence tomography as a useful adjunct in the early detection of meningioma with optic nerve compression.

A 48-year-old woman presented with persistent clouding vision in her lower field in the right eye for 5 months. A small retinal hemorrhage was initially reported. Her visual acuity was 20/30 in the right eye and 20/20 in the left, with normal color vision and pupil response. Fundus examination did not reveal any retinal hemorrhage. Although optical coherence tomography (OCT) showed normal macula and retinal nerve fiber layers in both eyes, asymmetric thinning of the ganglion cell inner plexiform layer was found in the superior macula of the right eye in ganglion cell analysis (GCA). Visual field examination revealed a subtle inferonasal scotoma. Compressive optic neuropathy (CON) was suspected. The visual evoked potential test revealed delayed P100 latency. A tuberculum sellae meningioma was found with right medial optic canal extension. The visual acuity of the right eye returned to 20/25 after decompression surgery. OCT can be used to differentiate between retinopathy and optic neuropathy. GCA can help in the early detection of CON and achieve a good visual outcome after surgery.

Supratentorial hemangioblastoma: A rare case report and literature review.

Hemangioblastomas are rare and benign tumors of the central nervous system. They account for 1.5%-2.5% of all intracranial tumors and have an incidence of 3.2%. The resemblance of hemangioblastomas to other tumors renders preoperative diagnosis and management challenging. Herein, we report a case of a supratentorial hemangioblastoma accompanied by extensive reactive gliosis and diagnosed through magnetic resonance imaging. In addition, we review the relevant literature.

Determination of a prediction model for therapeutic response and prognosis based on chemokine signaling-related genes in stage I-III lung squamous cell carcinoma.

Background: The chemokine signaling pathway plays an essential role in the development, progression, and immune surveillance of lung squamous cell carcinoma (LUSC). Our study aimed to systematically analyze chemokine signaling-related genes (CSRGs) in LUSC patients with stage I-III disease and develop a prediction model to predict the prognosis and therapeutic response. Methods: A total of 610 LUSC patients with stage I-III disease from three independent cohorts were included in our study. Least absolute shrinkage and selection operator (LASSO) and stepwise multivariate Cox regression analyses were used to develop a CSRG-related signature. GSVA and GSEA were performed to identify potential biological pathways. The ESTIMATE algorithm, ssGSEA method, and CIBERSORT analyses were applied to explore the correlation between the CSRG signature and the tumor immune microenvironment. The TCIA database and pRRophetic algorithm were utilized to predict responses to immunochemotherapy and targeted therapy. Results: A signature based on three CSRGs (CCL15, CXCL7, and VAV2) was developed in the TCGA training set and validated in the TCGA testing set and GEO external validation sets. A Kaplan-Meier survival analysis revealed that patients in the high-risk group had significantly shorter survival than those in the low-risk group. A nomogram combined with clinical parameters was established for clinical OS prediction. The calibration and DCA curves confirmed that the prognostic nomogram had good discrimination and accuracy. An immune cell landscape analysis demonstrated that immune score and immune-related functions were abundant in the high-risk group. Interestingly, the proportion of CD8 T-cells was higher in the low-risk group than in the high-risk group. Immunotherapy response prediction indicated that patients in the high-risk group had a better response to CTLA-4 inhibitors. We also found that patients in the low-risk group were more sensitive to first-line chemotherapeutic treatment and EGFR tyrosine kinase inhibitors. In addition, the expression of genes in the CSRG signature was validated by qRT‒PCR in clinical tumor specimens. Conclusion: In the present study, we developed a CSRG-related signature that could predict the prognosis and sensitivity to immunochemotherapy and targeted therapy in LUSC patients with stage I-III disease. Our study provides an insight into the multifaceted role of the chemokine signaling pathway in LUSC and may help clinicians implement optimal individualized treatment for patients.

Antiviral Effects and Underlying Mechanisms of Probiotics as Promising Antivirals.

Probiotics exert a variety of beneficial effects, including maintaining homeostasis and the balance of intestinal microorganisms, activating the immune system, and regulating immune responses. Due to the beneficial effects of probiotics, a wide range of probiotics have been developed as probiotic agents for animal and human health. Viral diseases cause serious economic losses to the livestock every year and remain a great challenge for animals. Moreover, strategies for the prevention and control of viral diseases are limited. Viruses enter the host through the skin and mucosal surface, in which are colonized by hundreds of millions of microorganisms. The antiviral effects of probiotics have been proved, including modulation of chemical, microbial, physical, and immune barriers through various probiotics, probiotic metabolites, and host signaling pathways. It is of great significance yet far from enough to elucidate the antiviral mechanisms of probiotics. The major interest of this review is to discuss the antiviral effects and underlying mechanisms of probiotics and to provide targets for the development of novel antivirals.

MicroRNA-like snoRNA-Derived RNAs (sdRNAs) Promote Castration-Resistant Prostate Cancer.

We have identified 38 specifically excised, differentially expressed snoRNA fragments (sdRNAs) in TCGA prostate cancer (PCa) patient samples as compared to normal prostate controls. SnoRNA-derived fragments sdRNA-D19b and -A24 emerged among the most differentially expressed and were selected for further experimentation. We found that the overexpression of either sdRNA significantly increased PC3 (a well-established model of castration-resistant prostate cancer (CRPC)) cell proliferation, and that sdRNA-D19b overexpression also markedly increased the rate of PC3 cell migration. In addition, both sdRNAs provided drug-specific resistances with sdRNA-D19b levels correlating with paclitaxel resistance and sdRNA-24A conferring dasatinib resistance. In silico and in vitro analyses revealed that two established PCa tumor suppressor genes, CD44 and CDK12, represent targets for sdRNA-D19b and sdRNA-A24, respectively. This outlines a biologically coherent mechanism by which sdRNAs downregulate tumor suppressors in AR-PCa to enhance proliferative and metastatic capabilities and to encourage chemotherapeutic resistance. Aggressive proliferation, rampant metastasis, and recalcitrance to chemotherapy are core characteristics of CRPC that synergize to produce a pathology that ranks second in cancer-related deaths for men. This study defines sdRNA-D19b and -A24 as contributors to AR-PCa, potentially providing novel biomarkers and therapeutic targets of use in PCa clinical intervention.

Electroencephalogram-Based Motor Imagery Classification Using Deep Residual Convolutional Networks.

The classification of electroencephalogram (EEG) signals is of significant importance in brain-computer interface (BCI) systems. Aiming to achieve intelligent classification of motor imagery EEG types with high accuracy, a classification methodology using the wavelet packet decomposition (WPD) and the proposed deep residual convolutional networks (DRes-CNN) is proposed. Firstly, EEG waveforms are segmented into sub-signals. Then the EEG signal features are obtained through the WPD algorithm, and some selected wavelet coefficients are retained and reconstructed into EEG signals in their respective frequency bands. Subsequently, the reconstructed EEG signals were utilized as input of the proposed deep residual convolutional networks to classify EEG signals. Finally, EEG types of motor imagination are classified by the DRes-CNN classifier intelligently. The datasets from BCI Competition were used to test the performance of the proposed deep learning classifier. Classification experiments show that the average recognition accuracy of this method reaches 98.76%. The proposed method can be further applied to the BCI system of motor imagination control.

Utilizing Machine Learning Techniques to Predict the Efficacy of Aerobic Exercise Intervention on Young Hypertensive Patients Based on Cardiopulmonary Exercise Testing.

Recently, the incidence of hypertension has significantly increased among young adults. While aerobic exercise intervention (AEI) has long been recognized as an effective treatment, individual differences in response to AEI can seriously influence clinicians' decisions. In particular, only a few studies have been conducted to predict the efficacy of AEI on lowering blood pressure (BP) in young hypertensive patients. As such, this paper aims to explore the implications of various cardiopulmonary metabolic indicators in the field by mining patients' cardiopulmonary exercise testing (CPET) data before making treatment plans. CPET data are collected "breath by breath" by using an oxygenation analyzer attached to a mask and then divided into four phases: resting, warm-up, exercise, and recovery. To mitigate the effects of redundant information and noise in the CPET data, a sparse representation classifier based on analytic dictionary learning was designed to accurately predict the individual responsiveness to AEI. Importantly, the experimental results showed that the model presented herein performed better than the baseline method based on BP change and traditional machine learning models. Furthermore, the data from the exercise phase were found to produce the best predictions compared with the data from other phases. This study paves the way towards the customization of personalized aerobic exercise programs for young hypertensive patients.

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.

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 Intelligent EEG Classification Methodology Based on Sparse Representation Enhanced Deep Learning Networks.

The classification of electroencephalogram (EEG) signals is of significant importance in brain-computer interface (BCI) systems. Aiming to achieve intelligent classification of EEG types with high accuracy, a classification methodology using sparse representation (SR) and fast compression residual convolutional neural networks (FCRes-CNNs) is proposed. In the proposed methodology, EEG waveforms of classes 1 and 2 are segmented into subsignals, and 140 experimental samples were achieved for each type of EEG signal. The common spatial patterns algorithm is used to obtain the features of the EEG signal. Subsequently, the redundant dictionary with sparse representation is constructed based on these features. Finally, the samples of the EEG types were imported into the FCRes-CNN model having fast down-sampling module and residual block structural units to be identified and classified. The datasets from BCI Competition 2005 (dataset IVa) and BCI Competition 2003 (dataset III) were used to test the performance of the proposed deep learning classifier. The classification experiments show that the recognition averaged accuracy of the proposed method is 98.82%. The experimental results show that the classification method provides better classification performance compared with sparse representation classification (SRC) method. The method can be applied successfully to BCI systems where the amount of data is large due to daily recording.