(±)-Talapyrones A-F (1-6), six pairs of dimeric polyketide enantiomers featuring unusual 6/6/6 and 6/6/6/5 ring systems, were isolated from the fungus Talaromyces adpressus. Their structures were determined by spectroscopic analysis and HR-ESI-MS data, and their absolute configurations were elucidated using a modified Mosher's method and electronic circular dichroism (ECD) calculations. (±)-Talapyrones A-F (1-6) possess a 6/6/6 tricyclic skeleton, presumably formed through a Michael addition reaction between one molecule of α-pyrone derivative and one molecule of C₈ poly-β-keto chain. In addition, compounds 2/3 and 4/5 are two pairs of C-18 epimers, respectively. Putative biosynthetic pathways of 1-6 were discussed.
Polyketides/isolation & purification* ; Talaromyces/chemistry* ; Stereoisomerism ; Molecular Structure ; Circular Dichroism ; Pyrones/chemistry*

Objective To construct a dynamic prediction model for the risk of developing Alzheimer's disease(AD)based on baseline clinical data and longitudinal neuropsychological scores in patients with mild cognitive impairment(MCI).Methods A total of 380 MCI patients from the Alzheimer's Disease Neuroimaging Initiative 1 study from 2005 to 2011 were selected and were randomly divided into a training set and a test set in a 7∶3 randomization.The Alzheimer's disease assessment scale-cognitive 13 items(ADAS-Cog13),Rey auditory verbal learning test immediate score(RAVLT Immediate),functional activities questionnaire(FAQ),and mini-mental state examination(MMSE)were used as longitudinal neuropsychological score metrics.Random survival forests with multivariate longitudinal endogenous covariates were used to construct a dynamic prediction model of the risk of developing AD in patients with MCI in the training set.The predictive performance of the model was evaluated in the test set using time-dependent areas under the receiver operator characteristic curve(AUC)and Brier score(BS).Results For the prediction of the risk of developing AD in patients with MCI,longitudinal neuropsychological scores were more important predictors than baseline clinical data,with FAQ being the strongest predictor.The dynamic prediction model had high predictive performance in the test set,with AUC ranging from 0.7695 to 0.8987 and BS ranging from 0.1369 to 0.2184.Conclusion Random survival forests with multivariate longitudinal endogenous covariates can be used to combine multivariate longitudinal neuropsychological scores to construct a prediction model for the risk of developing AD in patients with MCI,with high predictive performance and the ability to achieve individual dynamic prediction.

Objective To investigate the drivers,stakeholders,core tasks,and differentiated development models of digital transformation in Chinese hospitals,develop a hospital digital transformation model,and propose advancement pathways.Methods Leveraging ROCCIPI theory,socio-technical systems theory,and social network theory,a multiple case study approach was employed to analyze four representative Chinese hospitals,examining the driving factors,social network relationship,and core tasks of digital transformation.Results Hospital digital transformation is a complex process driven by regulations,opportunities,and capabilities,requiring efficient collaboration among stakeholders focused on patient services,clinical operations,hospital management,and security.It identified three development models-ecology-oriented,regional integration,and grassroots enhancement—based on the distinct characteristics of the hospitals.A theoretical model for digital transformation in four Chinese hospitals was developed,along with proposed pathways and strategies.Conclusion It presents a digital transformation model and advancement pathways for hospitals through multiple case analyses,addressing the limited perspectives of existing research and providing a reference for practice.

ObjectiveTo assess the efficiency of a Sophora flavescens Ait (S. flavescens, Ku Shen)-soluble microneedle (SFA-MN) for improving skin lesion symptoms in mice with psoriasis.MethodsSFA-MNs were prepared using a two-mold molding process with 20% w/v polyvinylpyrrolidone and 15% w/v polyvinyl alcohol. The SFA-MNs were assessed for morphology, mechanical properties, in vitro dissolution, identification of components, and skin lesion improvement in imiquimod-induced psoriasis mice.ResultsThe SFA-MNs demonstrated good mechanical properties for efficiently penetrating the dermis, facilitating efficient drug delivery. Furthermore, they effectively inhibited mast cell levels in the dorsal lesion area of psoriasis mice and reduced the expression of the T-lymphocyte factor cluster of differentiation 3 and tumor necrosis factor-α. In addition, this system alleviated skin inflammation, splenic swelling, and thymic atrophy in the psoriasis-like mouse model. Seven major components were detected from SFA-MNs by comparison of the mass-to-nucleus ratios (m/z) of the secondary fragments N-methylcytisine, 5α, 9α-dihydroxymatrine, sophoramine, matrine, oxysophocarpine, oxymatrine, and kushenol O.ConclusionThe drug delivery strategy combining traditional herbal S. flavescens with soluble microneedle technology provides more targeted and effective immune regulation for treating psoriasis-like mice models, enabling enhanced therapeutic effects compared with the control group.

Cancer stem cells (CSCs) are proposed to account for the progression, metastasis, and recurrence of diverse malignancies. However, the disorganized vasculars in tumors hinder the accumulation and penetration of nanomedicines, posing a challenge in eliminating CSCs located distantly from blood vessels. Herein, a pair of twin-like small-sized nanoparticles, sunitinib (St)-loaded ROS responsive micelles (RM@St) and salinomycin (SAL)-loaded GSH responsive micelles (GM@SAL), are developed to normalize disordered tumor vessels and eradicate CSCs. RM@St releases sunitinib in response to the abundant ROS in the tumor extracellular microenvironment for tumor vessel normalization, which improved intratumor accumulation and homogeneous distribution of small-sized GM@SAL. Sequentially, GM@SAL effectively accesses CSCs and achieves reduction-responsive drug release at high GSH concentrations within CSCs. More importantly, RM@St significantly extends the window of vessel normalization and enhances vessel integrity compared to free sunitinib, thus further amplifying the anti-tumor effect of GM@SAL. The combination therapy of RM@St plus GM@SAL produces considerable depression of tumor growth, drastically reducing CSCs fractions to 5.6% and resulting in 78.4% inhibition of lung metastasis. This study offers novel insights into rational nanomedicines designed for superior therapeutic effects by vascular normalization and anti-CSCs therapy.

Non-small cell lung cancer (NSCLC), a leading cause of cancer-related deaths worldwide, remains a significant clinical challenge despite advances in immune checkpoint inhibitors therapy, with drug resistance persisting as a major obstacle. Palmitoylation, a critical post-translational modification (PTM) primarily catalyzed by palmitoyltransferases of the zinc finger DHHC-type (ZDHHC), has recently demonstrated important implications in NSCLC. This review aims to elucidate the mechanisms and clinical potential of ZDHHC-mediated protein palmitoylation in NSCLC progression and immune escape.
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Humans ; Lipoylation ; Lung Neoplasms/pathology* ; Acyltransferases/genetics* ; Carcinoma, Non-Small-Cell Lung/pathology* ; Animals

Pinellia ternata， belonging to the Pinellia genus within the Araceae family， is a medicinal plant due to its tubers. There are severe issues with unclear germplasm and mixed varieties in its cultivation， necessitating urgent new variety protection efforts. The distinctness， uniformity， and stability （DUS） testing of the plant variety is the basis for protecting new plant varieties， and the DUS test guidelines are the technical basis for DUS testing. To develop the DUS test guidelines for P. ternata， agronomic traits of 229 germplasm of P. ternata were observed and measured during its two growth stages over the years， and each character was graded and described. A total of 38 traits were selected as the test traits of the DUS test guideline for P. ternata. There were three plant traits， 19 leaf traits， six flower traits， two fruit traits， two tuber traits， five bulbil traits， and one ploidy trait. These traits could be divided into 22 quality characters， 12 quantitative characters， and four pseudo-quantitative characters， as well as seven groups， including plants， leaves， flowers， fruit， tubers， bulbils， and ploidy. By searching for standard traits， 10 standard varieties were ultimately determined. Preparing these guidelines will have great significance for reviewing and protecting P. ternata varieties， safeguarding breeders' rights， and promoting the development of the P. ternata industry.

Objective To construct a dynamic prediction model for the risk of developing Alzheimer's disease(AD)based on baseline clinical data and longitudinal neuropsychological scores in patients with mild cognitive impairment(MCI).Methods A total of 380 MCI patients from the Alzheimer's Disease Neuroimaging Initiative 1 study from 2005 to 2011 were selected and were randomly divided into a training set and a test set in a 7∶3 randomization.The Alzheimer's disease assessment scale-cognitive 13 items(ADAS-Cog13),Rey auditory verbal learning test immediate score(RAVLT Immediate),functional activities questionnaire(FAQ),and mini-mental state examination(MMSE)were used as longitudinal neuropsychological score metrics.Random survival forests with multivariate longitudinal endogenous covariates were used to construct a dynamic prediction model of the risk of developing AD in patients with MCI in the training set.The predictive performance of the model was evaluated in the test set using time-dependent areas under the receiver operator characteristic curve(AUC)and Brier score(BS).Results For the prediction of the risk of developing AD in patients with MCI,longitudinal neuropsychological scores were more important predictors than baseline clinical data,with FAQ being the strongest predictor.The dynamic prediction model had high predictive performance in the test set,with AUC ranging from 0.7695 to 0.8987 and BS ranging from 0.1369 to 0.2184.Conclusion Random survival forests with multivariate longitudinal endogenous covariates can be used to combine multivariate longitudinal neuropsychological scores to construct a prediction model for the risk of developing AD in patients with MCI,with high predictive performance and the ability to achieve individual dynamic prediction.

Purpose To explore factors influencing vaginal delivery during the first stage of labor using intrapartum ultrasound and to construct predictive models for delivery decision-making.Materials and Methods A total of 473 nulliparous women admitted to Heping Hospital,Northern Theater General Hospital from July to December 2021 were prospectively enrolled as the training set.Clinical data on admission and fetal biometric parameters(biparietal diameter,femur length,head circumference and abdominal circumference)measured within one week before delivery were collected.Ultrasound assessments of fetal position,angle of progression(AOP)and head-perineum distance(HPD)were performed during the first stage of labor.The latent phase group(n=255)was subdivided into vaginal delivery group(n=186)and cesarean section group(n=69);the active phase group(n=218)was divided into vaginal delivery group(n=168)and cesarean section group(n=50).The associations between fetal position,AOP,HPD and vaginal delivery were analyzed,and predictive models were constructed for the latent phase(model 1)and active phase(model 2).A validation set of 547 women from January to September 2022 was used to evaluate model performance via area under the curve(AUC),calibration curves and decision curve analysis.Results In the latent phase,multivariate regression identified maternal height(OR=3.970,P=0.002),pre-pregnancy body mass index(OR=0.893,P=0.036),labor onset type(OR=2.415,P=0.045),neonatal birth weight(OR=3.728,P=0.002),AOP(OR=11.649,P<0.001)and HPD(OR=4.240,P=0.004)as significant predictors.The training and validation sets showed AUCs of 0.917 and 0.869,respectively.Goodness-of-fit tests indicated excellent model calibration(χ2=3.437,P=0.904;χ2=10.877,P=0.209).Decision curve analysis demonstrated strong clinical utility.For the active phase,significant predictors included maternal height(OR=6.532,P<0.001),neonatal birth weight(OR=11.890,P<0.001),fetal position(OR=4.600,P=0.003),AOP(OR=7.229,P<0.001)and HPD(OR=4.722,P=0.005).AUCs were 0.943(training)and 0.906(validation),with good calibration(χ2=4.340,P=0.740;χ2=9.836,P=0.277)and clinical applicability.Conclusion First stage ultrasound assessment of fetal position,AOP and HPD correlates with delivery outcomes.The developed nomogram models combining these parameters with clinical factors provide valuable guidance for delivery decision-making.

OBJECTIVE To explore the risk factors for postoperative surgical site infection(SSI)in the neurosur-gery department patients undergoing craniocerebral surgeries and establish Nomogram prediction model and verify it.METHODS A total of 1 265 patients who underwent craniocerebral surgeries in neurosurgery department of the First Hospital of Quanzhou City from Jan.2021 to Dec.2022 were recruited as the research subjects.The risk factors for the postoperative SSI were explored by logistic regression model.The Nomogram prediction model was established based on the independent risk factors that were screened by logistic regression analysis,and the model was verified.RESULTS Among 1 265 patients who underwent the craniocerebral surgeries,68 had SSI,with the infection rate of 5.38％.Diabetes mellitus,NNIS score no less than 2 points,NRS2002 score no less than 3 points,operation duration no less than 4.33 hours and drainage tube indwelling time more than 3 days were the independent risk factors for the postoperative SSI in the patients undergoing craniocerebral surgeries(P＜0.05).The area under the receiver operating characteristic(ROC)curve(AUC)of the established Nomogram pre-diction model was 0.842 in the training group,0.863 in the verification group.the calibration curves were drawn,the goodness of fit of the established Nomogram risk prediction model was assessed by means of Hosmer-Leme-show test;the predicted probability of SSI was highly consistent with the actual probability of infection,with the modeling group(P=0.851),the validation group(P=0.893).CONCLUSIONS The postoperative SSI in the neurosurgery department patients undergoing craniocerebral surgeries is closely associated with the diabe-tes mellitus,NNIS score no less than 2 points,NRS2002 score no less than 3 points,operation duration no less than 4.33 hours and drainage tube indwelling time more than 3 days.The established Nomogram prediction model has high prediction capability and can accurately assess the risk of SSI in the patients.