Screening biomarkers for spinal cord injury using weighted gene co-expression network analysis and machine learning

Immune changes and inflammatory responses have been identified as central events in the pathological process of spinal co rd injury.They can greatly affect nerve regeneration and functional recovery.However,there is still limited understanding of the peripheral immune inflammato ry response in spinal cord inju ry.In this study.we obtained microRNA expression profiles from the peripheral blood of patients with spinal co rd injury using high-throughput sequencing.We also obtained the mRNA expression profile of spinal cord injury patients from the Gene Expression Omnibus(GEO)database(GSE151371).We identified 54 differentially expressed microRNAs and 1656 diffe rentially expressed genes using bioinformatics approaches.Functional enrichment analysis revealed that various common immune and inflammation-related signaling pathways,such as neutrophil extracellular trap formation pathway,T cell receptor signaling pathway,and nuclear factor-κB signal pathway,we re abnormally activated or inhibited in spinal cord inju ry patient samples.We applied an integrated strategy that combines weighted gene co-expression network analysis,LASSO logistic regression,and SVM-RFE algorithm and identified three biomarke rs associated with spinal cord injury:ANO10,BST1,and ZFP36L2.We verified the expression levels and diagnostic perfo rmance of these three genes in the original training dataset and clinical samples through the receiver operating characteristic curve.Quantitative polymerase chain reaction results showed that ANO20 and BST1 mRNA levels were increased and ZFP36L2 mRNA was decreased in the peripheral blood of spinal cord injury patients.We also constructed a small RNA-mRNA interaction network using Cytoscape.Additionally,we evaluated the proportion of 22 types of immune cells in the peripheral blood of spinal co rd injury patients using the CIBERSORT tool.The proportions of naive B cells,plasma cells,monocytes,and neutrophils were increased while the proportions of memory B cells,CD8^(+)T cells,resting natural killer cells,resting dendritic cells,and eosinophils were markedly decreased in spinal cord injury patients increased compared with healthy subjects,and ANO10,BST1 and ZFP26L2we re closely related to the proportion of certain immune cell types.The findings from this study provide new directions for the development of treatment strategies related to immune inflammation in spinal co rd inju ry and suggest that ANO10,BST2,and ZFP36L2 are potential biomarkers for spinal cord injury.The study was registe red in the Chinese Clinical Trial Registry(registration No.ChiCTR2200066985,December 12,2022).

Social-ecological perspective on the suicidal behaviour factors of early adolescents in China:a network analysis

Background In early adolescence,youth are highly prone to suicidal behaviours.Identifying modifiable risk factors during this critical phase is a priority to inform effective suicide prevention strategies.Aims To explore the risk and protective factors of suicidal behaviours(ie,suicidal ideation,plans and attempts)in early adolescence in China using a social-ecological perspective.Methods Using data from the cross-sectional project‘Healthy and Risky Behaviours Among Middle School Students in Anhui Province,China',stratified random cluster sampling was used to select 5724 middle school students who had completed self-report questionnaires in November 2020.Network analysis was employed to examine the correlates of suicidal ideation,plans and attempts at four levels,namely individual(sex,academic performance,serious physical llness/disability,history of self-harm,depression,impulsivity,sleep problems,resilience),family(family economic status,relationship with mother,relationship with father,family violence,childhood abuse,parental mental illness),school(relationship with teachers,relationship with classmates,school-bullying victimisation and perpetration)and social(social support,satisfaction with society).Results In total,37.9%,19.0%and 5.5%of the students reported suicidal ideation,plans and attempts in the past 6 months,respectively.The estimated network revealed that suicidal ideation,plans and attempts were collectively associated with a history of self-harm,sleep problems,childhood abuse,school bullying and victimisation.Centrality analysis indicated that the most influential nodes in the network were history of self-harm and childhood abuse.Notably,the network also showed unique correlates of suicidal ideation(sex,weight=0.60;impulsivity,weight=0.24;family violence,weight=0.17;relationship with teachers,weight=-0.03;school-bullying perpetration,weight=0.22),suicidal plans(social support,weight=-0.15)and suicidal attempts(relationship with mother,weight=-0.10;parental mental llness,weight=0.61).Conclusions This study identified the correlates of suicidal ideation,plans and attempts,and provided practical implications for suicide prevention for young adolescents in China.Firstly,this study highlighted the importance of joint interventions across multiple departments.Secondly,the common risk factors of suicidal ideation,plans and attempts were elucidated.Thirdly,this study proposed target interventions to address the unique influencing factors of suicidal ideation,plans and attempts.

Iron and ferritin effects on intensive care unit mortality:A metaanalysis

BACKGROUND The effect of serum iron or ferritin parameters on mortality among critically ill patients is not well characterized.AIM To determine the association between serum iron or ferritin parameters and mortality among critically ill patients.METHODS Web of Science,Embase,PubMed,and Cochrane Library databases were searched for studies on serum iron or ferritin parameters and mortality among critically ill patients.Two reviewers independently assessed,selected,and abstracted data from studies reporting on serum iron or ferritin parameters and mortality among critically ill patients.Data on serum iron or ferritin levels,mortality,and demographics were extracted.RESULTS Nineteen studies comprising 125490 patients were eligible for inclusion.We observed a slight negative effect of serum ferritin on mortality in the United States population[relative risk(RR)1.002;95%CI:1.002-1.004].In patients with sepsis,serum iron had a significant negative effect on mortality(RR=1.567;95%CI:1.208-1.925).CONCLUSION This systematic review presents evidence of a negative correlation between serum iron levels and mortality among patients with sepsis.Furthermore,it reveals a minor yet adverse impact of serum ferritin on mortality among the United States population.

A Meta-Analysis of the Prognostic and Clinicopathological Significance of circZFR in Human Gastrointestinal Cancers

Background: Studies of gastrointestinal (GIT) cancers have shown that circZFR could be involved in the development and progression of various GIT cancers. However, small sample sizes limit the clinical significance of these studies. Here, a meta-analysis was conducted to ascertain the actual involvement of circZFR in the development and prognosis of GIT cancers. Methods: PubMed, Embase, Web of Science, and the Cochrane Library were searched up to December 31, 2023. Hazard ratios (HRs) or odds ratios (ORs) with 95% confidence intervals (CIs) were pooled to evaluate the association between circZFR expression and overall survival (OS). Publication bias was measured using the funnel plot and Egger’s test. Results: 10 studies having 659 participants were enrolled for meta-analysis. High circZFR expression was associated with poor OS (HR = 1.4, 95% CI: 1.20, 1.70). High circZFR expression also predicted larger tumor size (OR = 4.38, 95% CI 2.65, 7.25), advanced clinical stage (OR = 5.33, 95% CI 3.10, 9.16), and tendency for distant metastasis (OR = 2.89, 95% CI: 1.62, 5.11), but was not related to age, gender, and histological grade. Conclusions: In summary, high circZFR expression was associated with poor OS, larger tumor size, advanced stage cancer and tendency for distant metastasis. These findings suggested that circZFR could be a prognostic marker for GIT cancers.

Geometric prior guided hybrid deep neural network for facial beauty analysis

Facial beauty analysis is an important topic in human society.It may be used as a guidance for face beautification applications such as cosmetic surgery.Deep neural networks(DNNs)have recently been adopted for facial beauty analysis and have achieved remarkable performance.However,most existing DNN-based models regard facial beauty analysis as a normal classification task.They ignore important prior knowledge in traditional machine learning models which illustrate the significant contribution of the geometric features in facial beauty analysis.To be specific,landmarks of the whole face and facial organs are introduced to extract geometric features to make the decision.Inspired by this,we introduce a novel dual-branch network for facial beauty analysis:one branch takes the Swin Transformer as the backbone to model the full face and global patterns,and another branch focuses on the masked facial organs with the residual network to model the local patterns of certain facial parts.Additionally,the designed multi-scale feature fusion module can further facilitate our network to learn complementary semantic information between the two branches.In model optimisation,we propose a hybrid loss function,where especially geometric regulation is introduced by regressing the facial landmarks and it can force the extracted features to convey facial geometric features.Experiments performed on the SCUT-FBP5500 dataset and the SCUT-FBP dataset demonstrate that our model outperforms the state-of-the-art convolutional neural networks models,which proves the effectiveness of the proposed geometric regularisation and dual-branch structure with the hybrid network.To the best of our knowledge,this is the first study to introduce a Vision Transformer into the facial beauty analysis task.

Network analysis of the relationships between depressive symptoms and social participation activities among Chinese older adults and its implications for nursing

Objective:Network analysis was used to explore the complex inter-relationships between social participation activities and depressive symptoms among the Chinese older population,and the differences in network structures among different genders,age groups,and urban-rural residency would be compared.Methods:Based on the 2018 wave of the Chinese Longitudinal Healthy Longevity Survey(CLHLS),12,043 people aged 65 to 105 were included.The 10-item Center for Epidemiologic Studies Depression(CESD)Scale was used to assess depressive symptoms and 10 types of social participation activities were collected,including housework,tai-chi,square dancing,visiting and interacting with friends,garden work,reading newspapers or books,raising domestic animals,playing cards or mahjong,watching TV or listening to radio,and organized social activities.R 4.2.1 software was used to estimate the network model and calculate strength and bridge strength.Results:21.60%(2,601/12,043)of the participants had depressive symptoms.The total social participation score was negatively associated with depressive symptoms after adjusting for sociodemographic factors.The network of social participation and depressive symptoms showed that“D9(Inability to get going)”and“S9(Watching TV and/or listening to the radio)”had the highest strength within depressive symptoms and social participation communities,respectively,and“S1(Housework)”,“S9(Watching TV and/or listening to the radio)”,and“D5(Hopelessness)”were the most prominent bridging nodes between the two communities.Most edges linking the two communities were negative.“S5(Graden work)-D5(Hopelessness)”and“S6(Reading newspapers/books)-D4(Everything was an effort)”were the top 2 strongest negative edges.Older females had significantly denser network structures than older males.Compared to older people aged 65e80,the age group 81e105 showed higher network global strength.Conclusions:This study provides novel insights into the complex relationships between social participation and depressive symptoms.Except for doing housework,other social participation activities were found to be protective for depression levels.Different nursing strategies should be taken to prevent and alleviate depressive symptoms for different genders and older people of different ages.

Meta-analysis of transarterial chemoembolization combined with cryoablation vs transarterial chemoembolization alone for≥5 cm hepatocellular carcinoma

BACKGROUND Hepatocellular carcinoma(HCC)ranks sixth globally in cancer incidence and third in mortality rates.Unfortunately,over 70% of HCC patients forego the opportunity for curative surgery or liver transplantation due to inadequate physical examinations,poor physical condition,and limited organ availability upon diagnosis.Clinical guidelines endorse transarterial chemoembolization(TACE)as the frontline treatment for intermediate to advanced-stage HCC.Cryoablation(CRA)is an emerging local ablative therapy increasingly used in HCC management.Recent studies suggest that combining CRA with TACE offers complementary and synergistic effects,potentially improving long-term survival rates.However,the superiority of combined TACE+CRA therapy over TACE alone for HCC lesions equal to or exceeding 5 cm requires further investigation.AIM To compare the efficacy and safety of TACE combined with CRA vs TACE alone in the treatment of HCC with a diameter of≥5 cm.METHODS PubMed,EMBASE,Cochrane Library,CNKI,Wanfang,and VIP databases were searched to retrieve all relevant studies on TACE and CRA up to July 2022.Meta-analysis was performed using RevMan 5.3 software.RESULTS After screening according to the inclusion and exclusion criteria,6 articles were included,including 2 randomized controlled trials and 4 nonrandomized controlled trials,with a total of 575 patients included in the meta-analysis.The results showed that the objective response rate[odds ratio(OR)=2.56,95%confidence interval(CI):1.66-3.96,P<0.0001],disease control rate(OR=3.03,95%CI:1.88-4.89,P<0.00001),1-year survival rate(OR=3.79,95%CI:2.50-5.76,P<0.00001),2-year survival rate(OR=2.34,95%CI:1.43-3.85,P=0.0008),and 3-year survival rate(OR=3.34,95%CI:1.61-6.94,P=0.001)were all superior to those of the control group;the postoperative decrease in alpha-fetoprotein value(OR=295.53,95%CI:250.22-340.85,P<0.0001),the postoperative increase in CD4 value(OR=10.59,95%CI:8.78-12.40,P<0.00001),and the postoperative decrease in CD8 value(OR=6.47,95%CI:4.44-8.50,P<0.00001)were also significantly higher than those in the TACE-alone treatment group.CONCLUSION Compared with TACE-alone treatment,TACE+CRA combined treatment not only improves the immune function of HCC patients with a diameter of≥5 cm,but also enhances the therapeutic efficacy and long-term survival rate,without increasing the risk of complications.Therefore,TACE+CRA combined treatment may be a more recommended treatment for patients with HCC with a diameter of≥5 cm.

A Multilayer Network Constructed for Herb and Prescription Efficacy Analysis

Chinese Medicine(CM)has been widely used as an important avenue for disease prevention and treatment in China especially in the form of CM prescriptions combining sets of herbs to address patients’symptoms and syndromes.However,the selection and compatibility of herbs are complex and abstract due to intrinsic relationships between herbal properties and their overall functions.Network analysis is applied to demonstrate the complex relationships between individual herbal efficacy and the overall function of CM prescriptions.To illustrate their connections and correlations,prescription function(PF),prescription herb(PH),and herbal efficacy(HE)intranetworks are proposed based on CM theory to identify relationships between herbs and prescriptions.These three networks are then connected by PF-PH and PH-HE interlayer networks adopting herb dosage to form a multidimensional heterogeneous network,a Prescription-Herb-Function Network(PHFN).The network is applied to 112 classic prescriptions from Treatise on Exogenous Febrile and Miscellaneous Diseases to illustrate the application of PHFN.The PHFN is constructed including 146 functions in PF intra network,89 herbs in the PH intra network,and 163 herbal efficacies in the HE intra network.The results show that herb pairs with synergistic actions have stronger relevance,such as licorice-cassia twig,licorice-Chinese date,fresh ginger-Chinese date,etc.The integration of dosage to the network helps to indicate the main herbs for cluster analysis and automatic formulation.PHFN also reveals the internal relationships between the functions of prescriptions and composed herbal efficacies.

Using deep neural networks coupled with principal component analysis for ore production forecasting at open-pit mines

Ore production is usually affected by multiple influencing inputs at open-pit mines.Nevertheless,the complex nonlinear relationships between these inputs and ore production remain unclear.This becomes even more challenging when training data(e.g.truck haulage information and weather conditions)are massive.In machine learning(ML)algorithms,deep neural network(DNN)is a superior method for processing nonlinear and massive data by adjusting the amount of neurons and hidden layers.This study adopted DNN to forecast ore production using truck haulage information and weather conditions at open-pit mines as training data.Before the prediction models were built,principal component analysis(PCA)was employed to reduce the data dimensionality and eliminate the multicollinearity among highly correlated input variables.To verify the superiority of DNN,three ANNs containing only one hidden layer and six traditional ML models were established as benchmark models.The DNN model with multiple hidden layers performed better than the ANN models with a single hidden layer.The DNN model outperformed the extensively applied benchmark models in predicting ore production.This can provide engineers and researchers with an accurate method to forecast ore production,which helps make sound budgetary decisions and mine planning at open-pit mines.

Mechanism of imipenem-induced mental disorder: A meta-analysis

BACKGROUND Imipenem is a highly effective carbapenem antibiotic,which is widely used in the treatment of many serious bacterial infections.At the same time,it can also cause some adverse reactions,mental abnormalities are the most concerned central nervous system adverse reactions.Different patients respond differently to imipenem,and the effect of imipenem on psychiatric disorders is unclear.Therefore,meta-analysis summarizing the results of multiple previous studies can provide stronger evidence support for clinical guidelines to guide clinical rational use of imipenem to minimize risks.After reviewing the literature published between 2003 and 2017,seven controlled trials with a total of 550 patients were included,with 273 and 277 patients in the control and experimental groups,respectively.The sample size of the study ranged from a minimum of 30 cases to a maximum of 61 cases.Patients in the experimental group were treated with imipenem while the control group was treated with conventional drugs.Meta-analysis showed that the incidence of mental disorders in the experimental group was higher than that in the control group(odds ratio=3.66,95%confidence interval:1.11-12.11,P=0.030);however,there was no significant difference in the incidence of adverse reactions between the two groups(odds ratio=0.05,95%confidence interval:0.00 to 0.10,P=0.060).Funnel diagrams showed that the scattered points of each study were symmetrical and distributed in an inverted funnel shape;therefore,there was no publication bias.CONCLUSION Imipenem can cause mental disorders in patients.However,the low quality of the included literature may have affected the final results.Therefore,it is necessary to conduct a high-quality randomized controlled study with multiple samples to further confirm the mechanism of imipenem-induced mental disorders and provide effective guidance for clinical treatment.

Meta analysis of the efficacy of western medicine combined with Qiliqiangxin capsule versus western medicine alone in the treatment of chronic heart failure

Objective: To compare the clinical efficacy of conventional Western medicine combined with Qiliqiangxin capsule and western medicine alone in the treatment of chronic heart failure, and to prove that Qiliqiangxin capsule combined treatment has more advantages, providing reference for clinical decision-making in the treatment of chronic heart failure. Methods: Randomized controlled trials (RCTs) of conventional Western medicine treatment and Western medicine combined with Qiliqiangxin capsule in the treatment of chronic heart failure were searched in databases such as PubMed, Embase, Webofscience, CNKI, WanFang, VIP, and CBM. The bias risk assessment was conducted using the RCT tool recommended by Cochrane, and then the meta-analysis was performed using RevMan5.4 and Stata17 software. Compare the efficacy evaluation of cardiac function, left ventricular ejection fraction (LVEF), left ventricular end diastolic diameter (LVEDD), cardiac stroke output (SV), 6-minute walking test (6MWT), and N-terminal proBNP in the conventional western medicine combined with Qiliqiangxin capsule group (hereinafter referred to as the treatment group) and the conventional western medicine group (hereinafter referred to as the control group). Results: A total of 20 RCTs meeting the criteria were included, including 2953 patients, including 1508 in the treatment group and 1445 in the control group. The results of meta-analysis showed that the treatment group had significantly better cardiac function evaluation, LVEF, LVEDD, SV, 6MWT, and NT-proBNP improvement than the control group. Its central functional efficacy evaluation (OR=2.09,95% CI: 1.71-2.55, P<0.001), LVEF (WMD=7.05,95% CI: 5.30-8.79, P<0.00001), LVEDD (WMD=6.73, 95% CI: 3.18-10.29, P=0.0002), SV (WMD=6.73, 95% CI: 3.18-10.29, P=0.0002), 6MWT (SMD=0.70,95% CI: 0.54-0.87, P<0.00001), NT-proBNP (SMD=-1.95,95% CI: -2.5 2 to 1.38 (P<0.0001), with statistically significant differences. Conclusion: Conventional western medicine combined with Qiliqiangxin capsule can significantly improve the clinical efficacy of heart failure, improve LVEF, LVEDD, SV, and NT-proBNP index, and improve exercise tolerance. It is worth using for reference in the treatment.

A CSMA/CA based MAC protocol for hybrid Power-line/Visible-light communication networks:Design and analysis

Hybrid Power-line/Visible-light Communication(HPVC)network has been one of the most promising Cooperative Communication(CC)technologies for constructing Smart Home due to its superior communication reliability and hardware efficiency.Current research on HPVC networks focuses on the performance analysis and optimization of the Physical(PHY)layer,where the Power Line Communication(PLC)component only serves as the backbone to provide power to light Emitting Diode(LED)devices.So designing a Media Access Control(MAC)protocol remains a great challenge because it allows both PLC and Visible Light Communication(VLC)components to operate data transmission,i.e.,to achieve a true HPVC network CC.To solve this problem,we propose a new HPC network MAC protocol(HPVC MAC)based on Carrier Sense Multiple Access/Collision Avoidance(CSMA/CA)by combining IEEE 802.15.7 and IEEE 1901 standards.Firstly,we add an Additional Assistance(AA)layer to provide the channel selection strategies for sensor stations,so that they can complete data transmission on the selected channel via the specified CSMA/CA mechanism,respectively.Based on this,we give a detailed working principle of the HPVC MAC,followed by the construction of a joint analytical model for mathematicalmathematical validation of the HPVC MAC.In the modeling process,the impacts of PHY layer settings(including channel fading types and additive noise feature),CSMA/CA mechanisms of 802.15.7 and 1901,and practical configurations(such as traffic rate,transit buffer size)are comprehensively taken into consideration.Moreover,we prove the proposed analytical model has the solvability.Finally,through extensive simulations,we characterize the HPVC MAC performance under different system parameters and verify the correctness of the corresponding analytical model with an average error rate of 4.62%between the simulation and analytical results.

Investigating the therapeutic efficacy of psilocybin in advanced cancer patients: A comprehensive review and meta-analysis

BACKGROUND Psilocybin,a naturally occurring psychedelic compound found in certain species of mushrooms,is known for its effects on anxiety and depression.It has recently gained increasing interest for its potential therapeutic effects,particularly in patients with advanced cancer.This systematic review and meta-analysis aim to evaluate the effects of psilocybin on adult patients with advanced cancer.AIM To investigate the therapeutic effect of psilocybin in patients with advanced cancer.METHODS A comprehensive search of electronic databases was conducted in PubMed,Cochrane Central Register of Controlled Trials,and Google Scholar for articles published up to February 2023.The reference lists of the included studies were also searched to retrieve possible additional studies.RESULTS A total of 7 studies met the inclusion criteria for the systematic review,comprising 132 participants.The results revealed significant improvements in quality of life,pain control,and anxiety relief following psilocybin-assisted therapy,specifically results on anxiety relief.Pooled effect sizes indicated statistically significant reductions in symptoms of anxiety at both 4 to 4.5 months[35.15(95%CI:32.28-38.01)]and 6 to 6.5 months[33.06(95%CI:28.73-37.40)].Post-administration compared to baseline assessments(P<0.05).Additionally,patients reported sustained improvements in psychological well-being and existential distress fo-llowing psilocybin therapy.CONCLUSION The findings provided compelling evidence for the potential benefits of psilocybin-assisted therapy in improving quality of life,pain control,and anxiety relief in patients with advanced cancer.

Application of Bayesian Analysis Based on Neural Network and Deep Learning in Data Visualization

This study aims to explore the application of Bayesian analysis based on neural networks and deep learning in data visualization.The research background is that with the increasing amount and complexity of data,traditional data analysis methods have been unable to meet the needs.Research methods include building neural networks and deep learning models,optimizing and improving them through Bayesian analysis,and applying them to the visualization of large-scale data sets.The results show that the neural network combined with Bayesian analysis and deep learning method can effectively improve the accuracy and efficiency of data visualization,and enhance the intuitiveness and depth of data interpretation.The significance of the research is that it provides a new solution for data visualization in the big data environment and helps to further promote the development and application of data science.

Mechanisms of Sophora flavescens in the treatment of cervical squamous cell carcinoma based on comprehensive biological analysis,network pharmacology,and experimental verification

Objective:This study used comprehensive bioinformatics analysis and network pharmacology analysis to investigate the potentially relevant mechanisms of Sophora flavescens against cervical squamous cell carcinoma.Methods:Consistently altered genes involved in cervical squamous cell cancerization were analyzed in the GEO database.The chemical ingredients and target genes of Sophora flavescens were explored using the TCMSP database.We obtained the potential therapeutic targets of Sophora flavescens by intersecting the above genesets and validated them in the GEPIA database.The interaction between Sophora flavescens and target genes was predicted by molecular docking.RT-qPCR was used to verify the changes of target genes in HeLa cells treated with Sophora flavescens.Single-gene GSEA functional analysis were performed to determine the molecular mechanisms.Results:Fifteen genes related to the transformation of cervical squamous cell carcinoma were identified,among which AR and ESR1 were confirmed as targets for kaempferol,wighteone,formononetin,and phaseolinon.These compounds are the active ingredients in Sophora flavescens.Low expressions of AR and ESR1 correlate with a poor prognosis,while Sophora flavescens treatment increases the expression of AR and ESR1 in HeLa.GSEA analysis showed that AR and ESR1 mainly participate in the epithelial-mesenchymal transition in cervical squamous cell carcinoma.Conclusion:Sophora flavescens exert anti-tumor effects by targeting AR and ESR1,which may regulate cancer metastasis.

Carbocysteine as Adjuvant Therapy in Acute Respiratory Tract Infections in Patients without Underlying Chronic Conditions: Systematic Review and Meta-Analysis

Objective: This study aims to systematically examine the existing evidence regarding the clinical benefits of carbocysteine as an adjunctive treatment in acute bronchopulmonary and otorhinological processes. Design: Systematic review and meta-analysis. Data sources: An electronic search was conducted across PubMed, Cochrane Library, clinicaltrials.gov, and the European Clinical Trial Register, with the search dated to May 2023. Bibliographic references from other literature reviews and meta-analyses were also reviewed. The search was limited to randomized clinical trials published in any language and year. It was completed by cross-checking the references of the located articles. Methods: Inclusion criteria covered studies assessing systemic or inhaled carbocysteine, regardless of dosing regimen. Concomitant medication use was acceptable if balanced between intervention and control groups. Authors independently extracted data, resolving disagreements through consensus. Methodological quality assessment relied on critical reading of each study. Dichotomous variables were analyzed using odds ratio (OR), and a final effect size was calculated. Statistical significance was established when confidence intervals did not cross the neutral value. Heterogeneity was assessed via the X2 test and I2 index. Results: Out of 318 initially identified studies, 4 met inclusion criteria. The meta-analysis for poor general condition yielded an OR of 0.45 in favor of intervention, p = 0.013, with non-significant heterogeneity. Cough events showed a percentage of 15.8% for carbocysteine vs. 27.2% for placebo. On the seventh day, expectoration rates were 18.37% for carbocysteinevs 33.3% for placebo. Conclusions: The observed clinical benefits align with carbocysteine’s mucoactive and muco-regulatory properties, complemented by anti-inflammatory and antioxidant actions. Carbocysteine stands out among mucolytic agents. In the context of persistent infectious diseases, the study emphasizes the need for further exploration of carbocysteine’s therapeutic potential as an adjunctive treatment for acute respiratory infections. These findings underscore its significance in the evolving landscape of respiratory healthcare.

Prevalence of Latent Tuberculosis Infection [LTBI] in Prison Officers: A Systematic Review and Meta-Analysis

This study aimed to perform a systematic review and meta-analysis to determine the LTBI prevalence in prison officers worldwide. A systematic search was performed in PubMed, WoS, Embase, and BVS, including all articles related to LTBI prevalence and risk factors. After critical evaluation and qualitative synthesis of the identified articles, a meta-analysis was used. Five studies carried out between 2012 and 2022 were included, with a total sample size of 1718 prison officers. The overall LTBI prevalence was 50% [95% confidence interval [CI]: 48% - 52%;n = 816], with high heterogeneity between studies. Smoking [OR = 1.76;CI 95% = 1.26 - 2.46] and males [OR = 2.08;CI 95% = 1.31 - 3.31] were positively related to a higher LTBI prevalence among prison officers. Thus, preventive measures and the rapid and accurate diagnosis of new cases should be emphasized to ensure tuberculosis control, especially among risk groups such as prison officers.

Predicting the alloying element yield in a ladle furnace using principal component analysis and deep neural network

The composition control of molten steel is one of the main functions in the ladle furnace(LF)refining process.In this study,a feasible model was established to predict the alloying element yield using principal component analysis(PCA)and deep neural network(DNN).The PCA was used to eliminate collinearity and reduce the dimension of the input variables,and then the data processed by PCA were used to establish the DNN model.The prediction hit ratios for the Si element yield in the error ranges of±1%,±3%,and±5%are 54.0%,93.8%,and98.8%,respectively,whereas those of the Mn element yield in the error ranges of±1%,±2%,and±3%are 77.0%,96.3%,and 99.5%,respectively,in the PCA-DNN model.The results demonstrate that the PCA-DNN model performs better than the known models,such as the reference heat method,multiple linear regression,modified backpropagation,and DNN model.Meanwhile,the accurate prediction of the alloying element yield can greatly contribute to realizing a“narrow window”control of composition in molten steel.The construction of the prediction model for the element yield can also provide a reference for the development of an alloying control model in LF intelligent refining in the modern iron and steel industry.

Sentiment Analysis of Low-Resource Language Literature Using Data Processing and Deep Learning

Sentiment analysis, a crucial task in discerning emotional tones within the text, plays a pivotal role in understandingpublic opinion and user sentiment across diverse languages.While numerous scholars conduct sentiment analysisin widely spoken languages such as English, Chinese, Arabic, Roman Arabic, and more, we come to grapplingwith resource-poor languages like Urdu literature which becomes a challenge. Urdu is a uniquely crafted language,characterized by a script that amalgamates elements from diverse languages, including Arabic, Parsi, Pashtu,Turkish, Punjabi, Saraiki, and more. As Urdu literature, characterized by distinct character sets and linguisticfeatures, presents an additional hurdle due to the lack of accessible datasets, rendering sentiment analysis aformidable undertaking. The limited availability of resources has fueled increased interest among researchers,prompting a deeper exploration into Urdu sentiment analysis. This research is dedicated to Urdu languagesentiment analysis, employing sophisticated deep learning models on an extensive dataset categorized into fivelabels: Positive, Negative, Neutral, Mixed, and Ambiguous. The primary objective is to discern sentiments andemotions within the Urdu language, despite the absence of well-curated datasets. To tackle this challenge, theinitial step involves the creation of a comprehensive Urdu dataset by aggregating data from various sources such asnewspapers, articles, and socialmedia comments. Subsequent to this data collection, a thorough process of cleaningand preprocessing is implemented to ensure the quality of the data. The study leverages two well-known deeplearningmodels, namely Convolutional Neural Networks (CNN) and Recurrent Neural Networks (RNN), for bothtraining and evaluating sentiment analysis performance. Additionally, the study explores hyperparameter tuning tooptimize the models’ efficacy. Evaluation metrics such as precision, recall, and the F1-score are employed to assessthe effectiveness of the models. The research findings reveal that RNN surpasses CNN in Urdu sentiment analysis,gaining a significantly higher accuracy rate of 91%. This result accentuates the exceptional performance of RNN,solidifying its status as a compelling option for conducting sentiment analysis tasks in the Urdu language.

Trends and hotspots in gastrointestinal neoplasms risk assessment: A bibliometric analysis from 1984 to 2022

BACKGROUND Gastrointestinal neoplasm(GN)significantly impact the global cancer burden and mortality,necessitating early detection and treatment.Understanding the evolution and current state of research in this field is vital.AIM To conducts a comprehensive bibliometric analysis of publications from 1984 to 2022 to elucidate the trends and hotspots in the GN risk assessment research,focusing on key contributors,institutions,and thematic evolution.METHODS This study conducted a bibliometric analysis of data from the Web of Science Core Collection database using the"bibliometrix"R package,VOSviewer,and CiteSpace.The analysis focused on the distribution of publications,contributions by institutions and countries,and trends in keywords.The methods included data synthesis,network analysis,and visualization of international collaboration networks.RESULTS This analysis of 1371 articles on GN risk assessment revealed a notable evolution in terms of research focus and collaboration.It highlights the United States'critical role in advancing this field,with significant contributions from institutions such as Brigham and Women's Hospital and the National Cancer Institute.The last five years,substantial advancements have been made,representing nearly 45%of the examined literature.Publication rates have dramatically increased,from 20 articles in 2002 to 112 in 2022,reflecting intensified research efforts.This study underscores a growing trend toward interdisciplinary and international collaboration,with the Journal of Clinical Oncology standing out as a key publication outlet.This shift toward more comprehensive and collaborative research methods marks a significant step in addressing GN risks.CONCLUSION This study underscores advancements in GN risk assessment through genetic analyses and machine learning and reveals significant geographical disparities in research emphasis.This calls for enhanced global collaboration and integration of artificial intelligence to improve cancer prevention and treatment accuracy,ultimately enhancing worldwide patient care.