The role of cuproptosis-related gene in the classification and prognosis of melanoma.

Background: Melanoma, as one of the most aggressive and malignant cancers, ranks first in the lethality rate of skin cancers. Cuproptosis has been shown to paly a role in tumorigenesis, However, the role of cuproptosis in melanoma metastasis are not clear. Studying the correlation beteen the molecular subtypes of cuproptosis-related genes (CRGs) and metastasis of melanoma may provide some guidance for the prognosis of melanoma. Methods: We collected 1085 melanoma samples in The Cancer Genome Atlas(TCGA) and Gene Expression Omnibus(GEO) databases, constructed CRGs molecular subtypes and gene subtypes according to clinical characteristics, and investigated the role of CRGs in melanoma metastasis. We randomly divide the samples into train set and validation set according to the ratio of 1:1. A prognostic model was constructed using data from the train set and then validated on the validation set. We performed tumor microenvironment analysis and drug sensitivity analyses for high and low risk groups based on the outcome of the prognostic model risk score. Finally, we established a metastatic model of melanoma. Results: According to the expression levels of 12 cuproptosis-related genes, we obtained three subtypes of A1, B1, and C1. Among them, C1 subtype had the best survival outcome. Based on the differentially expressed genes shared by A1, B1, and C1 genotypes, we obtained the results of three gene subtypes of A2, B2, and C2. Among them, the B2 group had the best survival outcome. Then, we constructed a prognostic model consisting of 6 key variable genes, which could more accurately predict the 1-, 3-, and 5-year overall survival rates of melanoma patients. Besides, 98 drugs were screened out. Finally, we explored the role of cuproptosis-related genes in melanoma metastasis and established a metastasis model using seven key genes. Conclusions: In conclusion, CRGs play a role in the metastasis and prognosis of melanoma, and also provide new insights into the underlying pathogenesis of melanoma.

Discovering common pathogenetic processes between COVID-19 and sepsis by bioinformatics and system biology approach.

Corona Virus Disease 2019 (COVID-19), an acute respiratory infectious disease caused by severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2), has spread rapidly worldwide, resulting in a pandemic with a high mortality rate. In clinical practice, we have noted that many critically ill or critically ill patients with COVID-19 present with typical sepsis-related clinical manifestations, including multiple organ dysfunction syndrome, coagulopathy, and septic shock. In addition, it has been demonstrated that severe COVID-19 has some pathological similarities with sepsis, such as cytokine storm, hypercoagulable state after blood balance is disrupted and neutrophil dysfunction. Considering the parallels between COVID-19 and non-SARS-CoV-2 induced sepsis (hereafter referred to as sepsis), the aim of this study was to analyze the underlying molecular mechanisms between these two diseases by bioinformatics and a systems biology approach, providing new insights into the pathogenesis of COVID-19 and the development of new treatments. Specifically, the gene expression profiles of COVID-19 and sepsis patients were obtained from the Gene Expression Omnibus (GEO) database and compared to extract common differentially expressed genes (DEGs). Subsequently, common DEGs were used to investigate the genetic links between COVID-19 and sepsis. Based on enrichment analysis of common DEGs, many pathways closely related to inflammatory response were observed, such as Cytokine-cytokine receptor interaction pathway and NF-kappa B signaling pathway. In addition, protein-protein interaction networks and gene regulatory networks of common DEGs were constructed, and the analysis results showed that ITGAM may be a potential key biomarker base on regulatory analysis. Furthermore, a disease diagnostic model and risk prediction nomogram for COVID-19 were constructed using machine learning methods. Finally, potential therapeutic agents, including progesterone and emetine, were screened through drug-protein interaction networks and molecular docking simulations. We hope to provide new strategies for future research and treatment related to COVID-19 by elucidating the pathogenesis and genetic mechanisms between COVID-19 and sepsis.

Advancing Prediction of Risk of Intraoperative Massive Blood Transfusion in Liver Transplantation With Machine Learning Models. A Multicenter Retrospective Study.

Background: Liver transplantation surgery is often accompanied by massive blood loss and massive transfusion (MT), while MT can cause many serious complications related to high mortality. Therefore, there is an urgent need for a model that can predict the demand for MT to reduce the waste of blood resources and improve the prognosis of patients. Objective: To develop a model for predicting intraoperative massive blood transfusion in liver transplantation surgery based on machine learning algorithms. Methods: A total of 1,239 patients who underwent liver transplantation surgery in three large grade lll-A general hospitals of China from March 2014 to November 2021 were included and analyzed. A total of 1193 cases were randomly divided into the training set (70%) and test set (30%), and 46 cases were prospectively collected as a validation set. The outcome of this study was an intraoperative massive blood transfusion. A total of 27 candidate risk factors were collected, and recursive feature elimination (RFE) was used to select key features based on the Categorical Boosting (CatBoost) model. A total of ten machine learning models were built, among which the three best performing models and the traditional logistic regression (LR) method were prospectively verified in the validation set. The Area Under the Receiver Operating Characteristic Curve (AUROC) was used for model performance evaluation. The Shapley additive explanation value was applied to explain the complex ensemble learning models. Results: Fifteen key variables were screened out, including age, weight, hemoglobin, platelets, white blood cells count, activated partial thromboplastin time, prothrombin time, thrombin time, direct bilirubin, aspartate aminotransferase, total protein, albumin, globulin, creatinine, urea. Among all algorithms, the predictive performance of the CatBoost model (AUROC: 0.810) was the best. In the prospective validation cohort, LR performed far less well than other algorithms. Conclusion: A prediction model for massive blood transfusion in liver transplantation surgery was successfully established based on the CatBoost algorithm, and a certain degree of generalization verification is carried out in the validation set. The model may be superior to the traditional LR model and other algorithms, and it can more accurately predict the risk of massive blood transfusions and guide clinical decision-making.

Identification and Validation of the Pyroptosis-Related Molecular Subtypes of Lung Adenocarcinoma by Bioinformatics and Machine Learning.

Lung cancer remains the leading cause of cancer death globally, with lung adenocarcinoma (LUAD) being its most prevalent subtype. Due to the heterogeneity of LUAD, patients given the same treatment regimen may have different responses and clinical outcomes. Therefore, identifying new subtypes of LUAD is important for predicting prognosis and providing personalized treatment for patients. Pyroptosis-related genes play an essential role in anticancer, but there is limited research investigating pyroptosis in LUAD. In this study, 33 pyroptosis gene expression profiles and clinical information were collected from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases. By bioinformatics and machine learning analyses, we identified novel subtypes of LUAD based on 10 pyroptosis-related genes and further validated them in the GEO dataset, with machine learning models performing up to an AUC of 1 for classifying in GEO. A web-based tool was established for clinicians to use our clustering model (http://www.aimedicallab.com/tool/aiml-subphe-luad.html). LUAD patients were clustered into 3 subtypes (A, B, and C), and survival analysis showed that B had the best survival outcome and C had the worst survival outcome. The relationships between pyroptosis gene expression and clinical characteristics were further analyzed in the three molecular subtypes. Immune profiling revealed significant differences in immune cell infiltration among the three molecular subtypes. GO enrichment and KEGG pathway analyses were performed based on the differential genes of the three subtypes, indicating that differentially expressed genes (DEGs) were involved in multiple cellular and biological functions, including RNA catabolic process, mRNA catabolic process, and pathways of neurodegeneration-multiple diseases. Finally, we developed an 8-gene prognostic model that accurately predicted 1-, 3-, and 5-year overall survival. In conclusion, pyroptosis-related genes may play a critical role in LUAD, and provide new insights into the underlying mechanisms of LUAD.

A Novel Intercellular Communication-Associated Gene Signature for Prognostic Prediction and Clinical Value in Patients With Lung Adenocarcinoma.

BACKGROUND: Lung cancer remains the leading cause of cancer death globally, with lung adenocarcinoma (LUAD) being its most prevalent subtype. This study aimed to identify the key intercellular communication-associated genes (ICAGs) in LUAD. METHODS: Eight publicly available datasets were downloaded from the Gene Expression Omnibus (GEO) and The Cancer Genome Atlas (TCGA) databases. The prognosis-related ICAGs were identified and a risk score was developed by using survival analysis. Machine learning models were trained to predict LUAD recurrence based on the selected ICAGs and clinical information. Comprehensive analyses on ICAGs and tumor microenvironment were performed. A single-cell RNA-sequencing dataset was assessed to further elucidate aberrant changes in intercellular communication. RESULTS: Eight ICAGs with prognostic potential were identified in the present study, and a risk score was derived accordingly. The best machine-learning model to predict relapse was developed based on clinical information and the expression levels of these eight ICAGs. This model achieved a remarkable area under receiver operator characteristic curves of 0.841. Patients were divided into high- and low-risk groups according to their risk scores. DNA replication and cell cycle were significantly enriched by the differentially expressed genes between the high- and the low-risk groups. Infiltrating immune cells, immune functions were significantly related to ICAGs expressions and risk scores. Additionally, the changes of intercellular communication were modeled by analyzing the single-cell sequencing dataset. CONCLUSION: The present study identified eight key ICAGs in LUAD, which could contribute to patient stratification and act as novel therapeutic targets.

Identification of a Ferroptosis-Related LncRNA Signature as a Novel Prognosis Model for Lung Adenocarcinoma.

Lung adenocarcinoma (LUAD) is a highly heterogeneous malignancy, which makes prognosis prediction of LUAD very challenging. Ferroptosis is an iron-dependent cell death mechanism that is important in the survival of tumor cells. Long non-coding RNAs (lncRNAs) are considered to be key regulators of LUAD development and are involved in ferroptosis of tumor cells, and ferroptosis-related lncRNAs have gradually emerged as new targets for LUAD treatment and prognosis. It is essential to determine the prognostic value of ferroptosis-related lncRNAs in LUAD. In this study, we obtained RNA sequencing (RNA-seq) data and corresponding clinical information of LUAD patients from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) database and ferroptosis-related lncRNAs by co-expression analysis. The best predictors associated with LUAD prognosis, including C5orf64, LINC01800, LINC00968, LINC01352, PGM5-AS1, LINC02097, DEPDC1-AS1, WWC2-AS2, SATB2-AS1, LINC00628, LINC01537, LMO7DN, were identified by Least Absolute Shrinkage and Selection Operator (LASSO) Cox regression analysis, and the LUAD risk prediction model was successfully constructed. Kaplan-Meier analysis, receiver operating characteristic (ROC) time curve analysis and univariate and multivariate Cox regression analysis and further demonstrated that the model has excellent robustness and predictive ability. Further, based on the risk prediction model, functional enrichment analysis revealed that 12 prognostic indicators involved a variety of cellular functions and signaling pathways, and the immune status was different in the high-risk and low-risk groups. In conclusion, a risk model of 12 ferroptosis related lncRNAs has important prognostic value for LUAD and may be ferroptosis-related therapeutic targets in the clinic.

Celastrol ameliorates vascular neointimal hyperplasia through Wnt5a-involved autophagy.

Neointimal hyperplasia caused by the excessive proliferation of vascular smooth muscle cells (VSMCs) is the pathological basis of restenosis. However, there are few effective strategies to prevent restenosis. Celastrol, a pentacyclic triterpene, has been recently documented to be beneficial to certain cardiovascular diseases. Based on its significant effect on autophagy, we proposed that celastrol could attenuate restenosis through enhancing autophagy of VSMCs. In the present study, we found that celastrol effectively inhibited the intimal hyperplasia and hyperproliferation of VSMCs by inducing autophagy. It was revealed that autophagy promoted by celastrol could induce the lysosomal degradation of c-MYC, which might be a possible mechanism contributing to the reduction of VSMCs proliferation. The Wnt5a/PKC/mTOR signaling pathway was found to be an underlying mechanism for celastrol to induce autophagy and inhibit the VSMCs proliferation. These observations indicate that celastrol may be a novel drug with a great potential to prevent restenosis.

Development and Validation of a Machine-Learning Model for Prediction of Extubation Failure in Intensive Care Units.

Background: Extubation failure (EF) can lead to an increased chance of ventilator-associated pneumonia, longer hospital stays, and a higher mortality rate. This study aimed to develop and validate an accurate machine-learning model to predict EF in intensive care units (ICUs). Methods: Patients who underwent extubation in the Medical Information Mart for Intensive Care (MIMIC)-IV database were included. EF was defined as the need for ventilatory support (non-invasive ventilation or reintubation) or death within 48 h following extubation. A machine-learning model called Categorical Boosting (CatBoost) was developed based on 89 clinical and laboratory variables. SHapley Additive exPlanations (SHAP) values were calculated to evaluate feature importance and the recursive feature elimination (RFE) algorithm was used to select key features. Hyperparameter optimization was conducted using an automated machine-learning toolkit (Neural Network Intelligence). The final model was trained based on key features and compared with 10 other models. The model was then prospectively validated in patients enrolled in the Cardiac Surgical ICU of Zhongshan Hospital, Fudan University. In addition, a web-based tool was developed to help clinicians use our model. Results: Of 16,189 patients included in the MIMIC-IV cohort, 2,756 (17.0%) had EF. Nineteen key features were selected using the RFE algorithm, including age, body mass index, stroke, heart rate, respiratory rate, mean arterial pressure, peripheral oxygen saturation, temperature, pH, central venous pressure, tidal volume, positive end-expiratory pressure, mean airway pressure, pressure support ventilation (PSV) level, mechanical ventilation (MV) durations, spontaneous breathing trial success times, urine output, crystalloid amount, and antibiotic types. After hyperparameter optimization, our model had the greatest area under the receiver operating characteristic (AUROC: 0.835) in internal validation. Significant differences in mortality, reintubation rates, and NIV rates were shown between patients with a high predicted risk and those with a low predicted risk. In the prospective validation, the superiority of our model was also observed (AUROC: 0.803). According to the SHAP values, MV duration and PSV level were the most important features for prediction. Conclusions: In conclusion, this study developed and prospectively validated a CatBoost model, which better predicted EF in ICUs than other models.

Celastrol Attenuates Lipid Accumulation and Stemness of Clear Cell Renal Cell Carcinoma via CAV-1/LOX-1 Pathway.

Clear cell renal cell carcinoma (ccRCC) is characterized by abnormal lipid accumulation. Celastrol is a pentacyclic triterpene extracted from Tripterygium wilfordii Hook F with anti-cancer activity. In the present study, the anticancer effects of celastrol on ccRCC and the underlying mechanisms were studied. Patients with reduced high density lipoprotein (HDL) and elevated levels of triglyceride (TG), total cholesterol (TC), low density lipoprotein (LDL) was found to have higher risk of ccRCC. In ccRCC clinical samples and cell lines, caveolin-1 (CAV-1) was highly expressed. CAV-1 was identified as a potential prognostic biomarker for ccRCC. Celastrol inhibited tumor growth and decreased lipid deposition promoted by high-fat diet in vivo. Celastrol reduced lipid accumulation and caveolae abundance, inhibited the binding of CAV-1 and lectin-like oxidized low-density lipoprotein receptor-1 (LOX-1) in ccRCC cells. Furthermore, celastrol attenuated stemness through blocking Wnt/ß-catenin pathway after knockdown of CAV-1 and LOX-1. Therefore, the findings suggest that celastrol may be a promising active ingredient from traditional Chinese medicine for anti-cancer therapy.

Machine Learning for the Prediction of Red Blood Cell Transfusion in Patients During or After Liver Transplantation Surgery.

Aim: This study aimed to use machine learning algorithms to identify critical preoperative variables and predict the red blood cell (RBC) transfusion during or after liver transplantation surgery. Study Design and Methods: A total of 1,193 patients undergoing liver transplantation in three large tertiary hospitals in China were examined. Twenty-four preoperative variables were collected, including essential population characteristics, diagnosis, symptoms, and laboratory parameters. The cohort was randomly split into a train set (70%) and a validation set (30%). The Recursive Feature Elimination and eXtreme Gradient Boosting algorithms (XGBOOST) were used to select variables and build machine learning prediction models, respectively. Besides, seven other machine learning models and logistic regression were developed. The area under the receiver operating characteristic (AUROC) was used to compare the prediction performance of different models. The SHapley Additive exPlanations package was applied to interpret the XGBOOST model. Data from 31 patients at one of the hospitals were prospectively collected for model validation. Results: In this study, 72.1% of patients in the training set and 73.2% in the validation set underwent RBC transfusion during or after the surgery. Nine vital preoperative variables were finally selected, including the presence of portal hypertension, age, hemoglobin, diagnosis, direct bilirubin, activated partial thromboplastin time, globulin, aspartate aminotransferase, and alanine aminotransferase. The XGBOOST model presented significantly better predictive performance (AUROC: 0.813) than other models and also performed well in the prospective dataset (accuracy: 76.9%). Discussion: A model for predicting RBC transfusion during or after liver transplantation was successfully developed using a machine learning algorithm based on nine preoperative variables, which could guide high-risk patients to take appropriate preventive measures.

Triterpenoids from stems of Kadsura heteroclita.

In the present work, we reported the triterpenoids isolated from n-butanol fraction of Kadsura heteroclita which is a Tujia ethnomedicine with trivial name "Xuetong". This effort resulted in the isolation of six unpresented triterpenoids xuetongsu A-F (1-6), along with five known triterpenoids (7-11). The structures of the reported compounds were established on the 1D, and 2D NMR and HRESIMS spectra, along with CD spectroscopic analysis. Moreover, the absolute stereochemistry of compound 7 was determined by X-ray diffraction analysis. Antioxidant and cytotoxic activities were evaluated for all isolated compounds, compound 7 shown weak cytotoxic activity against HL-60 with IC50 value of 50.0 µM.

A Machine-Learning Approach for Dynamic Prediction of Sepsis-Induced Coagulopathy in Critically Ill Patients With Sepsis.

Background: Sepsis-induced coagulopathy (SIC) denotes an increased mortality rate and poorer prognosis in septic patients. Objectives: Our study aimed to develop and validate machine-learning models to dynamically predict the risk of SIC in critically ill patients with sepsis. Methods: Machine-learning models were developed and validated based on two public databases named Medical Information Mart for Intensive Care (MIMIC)-IV and the eICU Collaborative Research Database (eICU-CRD). Dynamic prediction of SIC involved an evaluation of the risk of SIC each day after the diagnosis of sepsis using 15 predictive models. The best model was selected based on its accuracy and area under the receiver operating characteristic curve (AUC), followed by fine-grained hyperparameter adjustment using the Bayesian Optimization Algorithm. A compact model was developed, based on 15 features selected according to their importance and clinical availability. These two models were compared with Logistic Regression and SIC scores in terms of SIC prediction. Results: Of 11,362 patients in MIMIC-IV included in the final cohort, a total of 6,744 (59%) patients developed SIC during sepsis. The model named Categorical Boosting (CatBoost) had the greatest AUC in our study (0.869; 95% CI: 0.850-0.886). Coagulation profile and renal function indicators were the most important features for predicting SIC. A compact model was developed with an AUC of 0.854 (95% CI: 0.832-0.872), while the AUCs of Logistic Regression and SIC scores were 0.746 (95% CI: 0.735-0.755) and 0.709 (95% CI: 0.687-0.733), respectively. A cohort of 35,252 septic patients in eICU-CRD was analyzed. The AUCs of the full and the compact models in the external validation were 0.842 (95% CI: 0.837-0.846) and 0.803 (95% CI: 0.798-0.809), respectively, which were still larger than those of Logistic Regression (0.660; 95% CI: 0.653-0.667) and SIC scores (0.752; 95% CI: 0.747-0.757). Prediction results were illustrated by SHapley Additive exPlanations (SHAP) values, which made our models clinically interpretable. Conclusions: We developed two models which were able to dynamically predict the risk of SIC in septic patients better than conventional Logistic Regression and SIC scores.

Development of near-zero water consumption cement materials via the geopolymerization of tektites and its implication for lunar construction.

The environment on the lunar surface poses some difficult challenges to building long-term lunar bases; therefore, scientists and engineers have proposed the creation of habitats using lunar building materials. These materials must meet the following conditions: be resistant to severe lunar temperature cycles, be stable in a vacuum environment, have minimal water requirements, and be sourced from local Moon materials. Therefore, the preparation of lunar building materials that use lunar resources is preferred. Here, we present a potential lunar cement material that was fabricated using tektite powder and a sodium hydroxide activator and is based on geopolymer technology. Geopolymer materials have the following properties: approximately zero water consumption, resistance to high- and low-temperature cycling, vacuum stability and good mechanical properties. Although the tektite powder is not equivalent to lunar soil, we speculate that the alkali activated activity of lunar soil will be higher than that of tektite because of its low Si/Al composition ratio. This assumption is based on the tektite geopolymerization research and associated references. In summary, this study provides a feasible approach for developing lunar cement materials using a possible water recycling system based on geopolymer technology.

Gold-catalyzed annulations of 2-alkynyl benzaldehydes with vinyl ethers: synthesis of dihydronaphthalene, isochromene, and bicyclo[2.2.2]octane derivatives.

With the suitable selection of a gold catalyst as well as the appropriate control of the reaction conditions, various new gold-catalyzed cyclizations of 2-alkynyl benzaldehyde with acyclic or cyclic vinyl ethers have been developed. Acetal-tethered dihydronaphthalene and isochromenes were obtained from the reactions of 2-alkynyl benzaldehydes with acyclic vinyl ethers under mild conditions. And, more interestingly, the gold-catalyzed reactions of 2-alkynyl benzaldehyde with a cyclic vinyl ether afforded the bicyclo[2.2.2]octane derivative involving two molecules of cyclic vinyl ethers. These products contain interesting substructures that have been found in many biologically active molecules and natural products. In addition, a gold-catalyzed homo-dimerization of 2-phenylethynyl benzaldehyde 1a was observed when the reaction was carried out in the absence of vinyl ether, affording a set of separable diastereomeric products. Plausible mechanisms for these transformations are discussed; a gold-containing benzopyrylium was regarded as the crucial intermediate by which a number of these new transformations took place.

Recent advances in the isolation and reactivity of organogold complexes.

Homogeneous gold catalysis has been one of the most active research fields in organic chemistry for more than a decade, and it has been also among the most efficient methodologies for forming carbon-carbon or carbon-heteroatom bonds. Recently, a number of organogold intermediates were isolated from stoichiometric reactions, which helps to better understand the mechanisms. Meanwhile, the reactivity of organogold compounds has been attracting the attention of organic chemists in the field. This tutorial review collects the most recent advances in the isolation and reactivity of organogold compounds that may help to open new directions in homogenous gold catalysis.

Gold-catalyzed, intramolecular, oxygen-transfer reactions of 2-alkynyl-1,5-diketones or 2-alkynyl-5-ketoesters: scope, expansion, and mechanistic investigations on a new [4+2] cycloaddition.

The gold-catalyzed intramolecular oxygen-transfer reactions of 2-alkynyl-1,5-diketones or 2-alkynyl-5-ketoesters-obtained from tetra-n-butylammonium fluoride mediated Michael addition of activated allenes to electron-deficient olefins-furnished cyclopentenyl ketones under very mild conditions. These reactions proceeded much easier and faster than similar reactions reported in literature, and the corresponding products were obtained in very good yields. Mechanistic investigations on the cycloisomerization were carried out by means of both (18) O isotopic experiments and quantum chemical calculations. The results from both, the designed isotopic experiments and theoretical calculations, satisfactorily supported the novel proposed intramolecular [4+2] cycloaddition of a gold-containing furanium intermediate to a carbonyl group, instead of the previous well-accepted [2+2] pathway.

Gold-catalyzed cascade annulations of 2-(ynol)aryl aldehydes: facile synthesis of benzochromanes and benzobicyclo[n.3.1]acetals.

Gold-catalyzed reactions of 2-(ynol)aryl aldehydes were investigated. Benzochromanes were obtained from the reaction when AuCl(3) was employed as the catalyst, whereas benzobicyclo[n.3.1]acetals were produced when triazole-gold was employed as the catalyst. Plausible mechanisms are discussed.

Gold(I)-catalyzed tandem C-H and C-C activation (cleavage).

(Cyclopropylidenecyclohexyl)benzene derivatives, a kind of methylenecyclopropane (MCP) containing a cyclohexyl group, can undergo an interesting tandem intramolecular C-H and C-C bond activation through dehydrogenated rearrangement in the presence of AuPPh(3)Cl/AgOTf by transferring three hydrogen atoms from cyclohexane to cyclopropane, affording the corresponding biaryl derivatives in moderate to good yields. The reaction mechanism has also been carefully investigated by deuterium labeling experiments and DFT calculations.

Highly efficient and tunable synthesis of dioxabicyclo[4.2.1] ketals and tetrahydropyrans via gold-catalyzed cycloisomerization of 2-alkynyl-1,5-diols.

A highly efficient gold(I) chloride catalyzed cycloisomerization of 2-alkynyl-1,5-diol (1) to dioxabicyclo[4.2.1] ketal (2) and its further transformation to tetrahydropyran (3) are reported. The diol is readily obtained by the reduction of 2-alkynyl-substituted glutarates, isolated from the Michael addition of allenoates to methyl acrylate. These reactions proceeded smoothly under very mild conditions. A plausible mechanism for the formation of the said tetrahydropyran from the corresponding ketal is proposed.