The bioactivity-guided isolation of potentially active natural products has been widely utilized in pharmaceutical discovery. In this study, by screening fungal extracts against coxsackievirus B3 (CVB3), three new aspochalasins, templichalasins A‒C (1‒3), along with six known aspochalasins (4‒9) were isolated from an active extract derived from the endophytic fungus Aspergillus templicola LHWf045. Compound 1 features a unique 5/6/5/7/5 pentacyclic ring system, while compounds 2 and 3 possess unusual 5/6/6/7 tetracyclic skeletons. Their structures were characterized through extensive spectroscopic analyses, electronic circular dichroism (ECD) calculations, and single-crystal X-ray diffraction analysis. Additionally, we demonstrated that compound 4 can be readily converted into compounds 1‒3 under mild acidic conditions and proposed a plausible mechanism for this conversion. Bioactivity evaluation of compounds 1‒9 against CVB3 revealed the inhibitory effects of all compounds against the virus. Notably, compound 9 exhibited superior antiviral activity, surpassing the commercial drug ribavirin in selectivity index (SI) value.
Antiviral Agents/isolation & purification* ; Aspergillus/chemistry* ; Molecular Structure ; Enterovirus B, Human/drug effects* ; Endophytes/chemistry* ; Cytochalasins/isolation & purification* ; Drug Discovery ; Humans

Objective To investigate the risk factors for acute respiratory failure in immunocompromised patients with Pneumocystis jirovecii pneumonia(PJP).Methods Clinical data of 123 immunocompromised patients complicated with PJP hospitalized at Mengchao Hepatobiliary Hospital of Fujian Medical University from January 2021 to December 2023 were retrospectively collected and analyzed.SPSS 22.0 statistical software package was used to perform multivariate binary logistic regression analysis to identify risk factors for acute respiratory failure in PJP patients.Results Among the 123 PJP patients,77 were HIV-positive,and 46 were HIV-negative.HIV-negative PJP patients were more likely to have comorbidities such as hypertension(P＜0.001),diabetes mellitus(P＜0.001),coronary heart disease(P=0.034),chronic kidney disease(P＜0.001),chronic liver disease(P=0.019),chronic lung disease(P=0.011),and malignant tumor(P＜0.001).They were also more prone to respiratory failure(P＜0.001)and ICU admission(P＜0.001).The HIV-positive patients had significantly lower CD4+T lymphocyte counts and albumin levels(P＜0.001).Forty patients developed acute respiratory failure,and six patients died.Multivariate analysis showed that high neutrophil-to-lymphocyte ratio(NLR)(P=0.031),non-HIV infection(P=0.002),and concomitant infections with other pathogens(P＜0.001)were independent risk factors for incidence of respiratory failure.ROC curve analysis revealed that the area under the curve(AUC)was 0.686(0.584,0.789)for non-HIV infection,0.731(0.637,0.826)for concomitant infections with other pathogens,0.648(0.546,0.750)for NLR.The predicted probability was 0.845(0.778,0.912).Conclusions Non-HIV infection,high NLR,and concomitant infections with other pathogens are independent risk factors for incidence of respiratory failure in PJP patients.The panel combining these factors provides a higher predictive value for respiratory failure.Timely assessment of patient condition and early treatment are vital for better outcomes.

Objective To investigate the predictive efficacy of complete blood count(CBC)parameters for the prognosis of acute pancreatitis(AP),and to construct a machine learning model.Methods The clinical data of 120 patients with AP admitted from January 2021 to December 2024 were retrospectively analyzed.Based on the prognostic outcomes within 28 d of treatment,they were divided into the poor prognosis group and the good prognosis group.On the first day of admission,CBC parameters[red blood cell count,hemoglobin,hematocrit,white blood cell count,neutrophil count,lymphocyte count,monocyte count,red blood cell distribution width(RDW),platelet count,mean platelet volume,platelet distribution width,neutrophil/lymphocyte ratio(NLR),platelet/lymphocyte ratio(PLR),lymphocyte/monocyte ratio(LMR)were detected in the two groups.The general data and CBC parameters of the two groups were compared.The clinical variables were screened using the Lasso regression equation.Four models,namely Support Vector Machine(SVM),logistic regression(LR),deep neural network(DNN),and Random Forest(RF),were constructed for external validation.The predictive efficacy of the four models for the prognosis of AP was evaluated by using the receiver operating characteristic(ROC)curve,the precision-recall(PR)curve,the calibration curve and the clinical decision curve.Results The APACHE Ⅱ score,bedside severity index score,proportion of vasoactive drug use,and proportion of severe hypoproteinemia in the poor prognosis group were all higher than those in the good prognosis group(P<0.01).The levels of RDW,NLR and PLR in the poor prognosis group were all higher than those in the good prognosis group,while the level of LMR was lower than that in the good prognosis group(P<0.01).The Lasso regression equation ultimately screened out four non-zero coefficient variables:NLR,RDW,APACHE Ⅱ score,and severe hypoproteinemia.Based on the above variables,SVM,LR,RF and DNN machine learning models were constructed.The RF model had the highest area under the ROC curve(AUC),PR AUC,accuracy rate and F1 score for predicting the prognosis of AP,and had the optimal comprehensive performance.The calibration curve showed that the curve of the RF model for predicting the prognosis of AP was relatively close to the ideal curve.The decision curve showed that when the threshold probability value of the RF model was 15%to 100%,it had a significance clinical net benefit rate.External validation also showed that the RF model had the optimal predictive efficacy.The calibration curve indicated that the predictive curve of the RF model for the prognosis of AP highly coincided with the actually observed curve.The decision curve showed that the RF model provided clinical net benefits to patients across the decision threshold range of 0 to 100%.Conclusion Machine learning models constructed based on CBC parameters can make relatively accurate predictions about the prognosis of AP.Among them,the RF model has the optimal predictive efficiency,which can be used as an auxiliary tool for clinical prediction of AP prognosis,and can also provide reference for clinical treatment.

Bacteroides uniformis(B.uniformis)is an obligate anaerobic Gram-negative bacteria,it belongs to Bacieroides spp..It is an important component of the normal gut microbiota in human body and rarely causes op-portunistic infection in clinical practice.Currently,there is no consensus on the clinical characteristics,diagnosis,treatment,and prognosis of infection caused by B.uniformis,and the resistance of anaerobic bacteria is difficult to be detected.This article reports a case of a lung tumor patient who developed diarrhea and bloodstream infection due to B.uniformis after chemotherapy.After empirical anti-infective treatment with cefoperazone/sulbactam,the pa-tient recovered and was discharged from hospital.This paper aims to improve clinical understanding on infection caused by opportunistic pathogens in the gut,and provide reference for timely diagnosis and effective treatment.

Bacteroides uniformis(B.uniformis)is an obligate anaerobic Gram-negative bacteria,it belongs to Bacieroides spp..It is an important component of the normal gut microbiota in human body and rarely causes op-portunistic infection in clinical practice.Currently,there is no consensus on the clinical characteristics,diagnosis,treatment,and prognosis of infection caused by B.uniformis,and the resistance of anaerobic bacteria is difficult to be detected.This article reports a case of a lung tumor patient who developed diarrhea and bloodstream infection due to B.uniformis after chemotherapy.After empirical anti-infective treatment with cefoperazone/sulbactam,the pa-tient recovered and was discharged from hospital.This paper aims to improve clinical understanding on infection caused by opportunistic pathogens in the gut,and provide reference for timely diagnosis and effective treatment.

Objective To investigate the risk factors for acute respiratory failure in immunocompromised patients with Pneumocystis jirovecii pneumonia(PJP).Methods Clinical data of 123 immunocompromised patients complicated with PJP hospitalized at Mengchao Hepatobiliary Hospital of Fujian Medical University from January 2021 to December 2023 were retrospectively collected and analyzed.SPSS 22.0 statistical software package was used to perform multivariate binary logistic regression analysis to identify risk factors for acute respiratory failure in PJP patients.Results Among the 123 PJP patients,77 were HIV-positive,and 46 were HIV-negative.HIV-negative PJP patients were more likely to have comorbidities such as hypertension(P＜0.001),diabetes mellitus(P＜0.001),coronary heart disease(P=0.034),chronic kidney disease(P＜0.001),chronic liver disease(P=0.019),chronic lung disease(P=0.011),and malignant tumor(P＜0.001).They were also more prone to respiratory failure(P＜0.001)and ICU admission(P＜0.001).The HIV-positive patients had significantly lower CD4+T lymphocyte counts and albumin levels(P＜0.001).Forty patients developed acute respiratory failure,and six patients died.Multivariate analysis showed that high neutrophil-to-lymphocyte ratio(NLR)(P=0.031),non-HIV infection(P=0.002),and concomitant infections with other pathogens(P＜0.001)were independent risk factors for incidence of respiratory failure.ROC curve analysis revealed that the area under the curve(AUC)was 0.686(0.584,0.789)for non-HIV infection,0.731(0.637,0.826)for concomitant infections with other pathogens,0.648(0.546,0.750)for NLR.The predicted probability was 0.845(0.778,0.912).Conclusions Non-HIV infection,high NLR,and concomitant infections with other pathogens are independent risk factors for incidence of respiratory failure in PJP patients.The panel combining these factors provides a higher predictive value for respiratory failure.Timely assessment of patient condition and early treatment are vital for better outcomes.

Objective To investigate the predictive efficacy of complete blood count(CBC)parameters for the prognosis of acute pancreatitis(AP),and to construct a machine learning model.Methods The clinical data of 120 patients with AP admitted from January 2021 to December 2024 were retrospectively analyzed.Based on the prognostic outcomes within 28 d of treatment,they were divided into the poor prognosis group and the good prognosis group.On the first day of admission,CBC parameters[red blood cell count,hemoglobin,hematocrit,white blood cell count,neutrophil count,lymphocyte count,monocyte count,red blood cell distribution width(RDW),platelet count,mean platelet volume,platelet distribution width,neutrophil/lymphocyte ratio(NLR),platelet/lymphocyte ratio(PLR),lymphocyte/monocyte ratio(LMR)were detected in the two groups.The general data and CBC parameters of the two groups were compared.The clinical variables were screened using the Lasso regression equation.Four models,namely Support Vector Machine(SVM),logistic regression(LR),deep neural network(DNN),and Random Forest(RF),were constructed for external validation.The predictive efficacy of the four models for the prognosis of AP was evaluated by using the receiver operating characteristic(ROC)curve,the precision-recall(PR)curve,the calibration curve and the clinical decision curve.Results The APACHE Ⅱ score,bedside severity index score,proportion of vasoactive drug use,and proportion of severe hypoproteinemia in the poor prognosis group were all higher than those in the good prognosis group(P<0.01).The levels of RDW,NLR and PLR in the poor prognosis group were all higher than those in the good prognosis group,while the level of LMR was lower than that in the good prognosis group(P<0.01).The Lasso regression equation ultimately screened out four non-zero coefficient variables:NLR,RDW,APACHE Ⅱ score,and severe hypoproteinemia.Based on the above variables,SVM,LR,RF and DNN machine learning models were constructed.The RF model had the highest area under the ROC curve(AUC),PR AUC,accuracy rate and F1 score for predicting the prognosis of AP,and had the optimal comprehensive performance.The calibration curve showed that the curve of the RF model for predicting the prognosis of AP was relatively close to the ideal curve.The decision curve showed that when the threshold probability value of the RF model was 15%to 100%,it had a significance clinical net benefit rate.External validation also showed that the RF model had the optimal predictive efficacy.The calibration curve indicated that the predictive curve of the RF model for the prognosis of AP highly coincided with the actually observed curve.The decision curve showed that the RF model provided clinical net benefits to patients across the decision threshold range of 0 to 100%.Conclusion Machine learning models constructed based on CBC parameters can make relatively accurate predictions about the prognosis of AP.Among them,the RF model has the optimal predictive efficiency,which can be used as an auxiliary tool for clinical prediction of AP prognosis,and can also provide reference for clinical treatment.

Objective:To analyze the clinical characteristics of children with 2019 novel coronavirus (2019-nCoV) infection in Putian City, and to provide a reference for the diagnosis and treatment of children with 2019-nCoV infection.Methods:Clinical characteristics, laboratory examination, pulmonary compated tomography findings, treatment, and clinical outcomes of 78 children with 2019-nCoV infection who were admitted to Putian University Affiliated Hospital Medical Group Putian City Children′s Hospital from September 10 to October 20, 2021 were retrospectively collected and analyzed.Results:Of the 78 children included in the analysis, two cases (2.6%) were asymptomatic infection, 36 cases (46.2%) were mild and 40 cases (51.3%) were ordinary. Five children were vaccinated against 2019-nCoV. The main symptoms were fever (24 cases), cough (13 cases), and fatigue (nine cases). A total of 34 cases (43.6%) had neutropenia, 29 cases (37.2%) had lymphopenia, 36 cases (46.2%) had D-dimer increase, 38 cases (48.7%) had hypokalemia, 27 cases (34.6%) had hypoglycemia and 11 cases (14.1%) had elevated creatine kinase isoenzyme. The neutropenia mostly occurred two to four days after admission. Fifty-six cases (71.8%) showed pulmonary computed tomography abnormalities. The cycle threshold of virus open reading frame ( ORF)1 ab was 20.90±7.15 and the cycle threshold of N gene was 20.29±7.78 in the first nucleic acid detection of 78 children after admission. The time of nucleic acid negative conversion of the 78 children was (20.73±6.94) days. IgM antibody titer in five vaccinated children was 0.36 (0.34, 4.89) and IgG antibody was 10.42 (0.50, 19.42). IgM antibody titer was 1.82 (1.66, 8.12) and IgG antibody was 76.63 (16.92, 79.84) in cases with disease duration ≥10 days. Nine children (11.5%) had resurgence of virus and were sent to the isolation site. All the other children were cured and discharged from hospital. Conclusions:Children with 2019-nCoV infection have mild clinical symptoms, and some children have lymphopenia, neutropenia, and D-dimer elevation during the course of the disease. The overall prognosis is good. The children vaccinated against 2019-nCoV have higher antibody levels.

Objective To establish a new method for rapid detection of β-thalassemia by investigating six clinical common mutation types.Methods Fifty cases of clinical wild-type samples and 42 cases ofβ-thalassemia samples were collected, and β-globin gene was amplified by PCR.Uniform ligation probe ( ULP) specific probes were designed for hybridization reaction to increase the reaction specificity and real-time PCR was performed to increase the sensitivity.After that, PCR products were verified by agarose electrophoresis.After examining the specificity and sensitivity, Kappa test between LDR-ULP method and reverse dot blot( RDB) method was conducted.Results Hybridization efficiency was improved 2.53 times by LDR-ULP hybridization.Each mutant type showed a significant amplification curve, whereas the wild-type had no significant curve within 40 cycles.The limit of determination of this method was 5 pg.The results of 92 cases of peripheral blood samples detected by the method of LDR-ULP and RDB were completely consistent.Conclusion In this study, a simple, inexpensive, rapid new method to detect β-thalassemia were established.

BACKGROUNDLivin is a novel inhibitor of apoptosis protein(IAP),recent studies showed that it overexpressed in many carcinomas including lung cancer and contributed much to the cancerous development.The objective of this study is to explore the expression of the two isoforms of livin in lung cancer tissues and their relationship with histological types and chemotherapy,and to explore their relationship to the expression of caspase-3 as well.

METHODSExpression of livin α,livin β and caspase-3 mRNAs were detected by reverse transcription polymerase chain reaction(RT-PCR) assay in lung cancer tissues as well as in controls.

RESULTSLivin α and livin β were expressed in 12 of 27 and 19 of 27 lung cancer tissues respectively,much higher than those in lung para-cancerous(0/6,0/6) or benign disease lung tissues(0/12,1/12)(both P < 0.01).Moreover,the positive rate was 7/14 and 9/14 in lung adenocarcinoma and 4/12 and 9/12 in squamous and large cell carcinoma respectively,and both of them were detected in one small cell carcinoma.The levels of these two isoforms in lung cancer were significantly higher than those in controls by Gel Imaging System(both P < 0.05),the level of livin α was remarkably higher in adenocarcinoma than that in squamous cell carcinoma(P < 0.05),while the level of livin β was similar in both carcinomas(P > 0.05).Meanwhile,the level of caspase-3 in lung cancer was significantly lower than that in controls,the levels of either each of two isoforms or their sum were negatively associated with that of caspase-3(P < 0.05,P < 0.01,P < 0.01).Two isoforms of livin mRNA expression seemed to increase after chemotherapy but not related to clinical stages(P > 0.05).

CONCLUSIONSTwo isoforms of livin are differently expressed in different histological types of lung cancer and may contribute to corresponding cancerous development;the levels of livin are negatively associated with those of caspase-3,this may due to the fact that livin could resist against apoptosis;high expression of livin seems to be related to chemotherapy but not clinical stage.