Amide groups within polystyrene accelerates tetracycline removal in a continuous advanced microalgal treatment system.

Livestock effluents are challenging to be treated owing that antibiotics and microplastics are untargeted for most biological technologies. As far, microalgal wastewater treatment is recognized as an effective technique for dealing with. In this study, a continuous-flow system was conducted over 45 days to evaluate the effectiveness of Chlamydomonas sp. JSC4 in removing tetracycline (TCH) under the influence of polystyrene (PS). It shows that PS significantly enhanced the dissipation efficiency of TCH from livestock effluents, and 9.83 % TCH removal was increased under 5 mg/L of both TCH and PS exposure. Meanwhile, higher microalgal bioactivity was a significant factor in achieving desirable pollutants removal efficiency, as 87.14 % microalgal biomass was improved owing to reduction of oxidative stress and augmentation of photosynthesis. Importantly, the pivotal active sites, NH2 and CO, were rapidly covered via π-π interactions and hydrogen bonds during adsorption process between TCH and PS, accounting for mitigation of TCH-PS complexes toxicity and improvement of microalgal ribosome metabolism. Additionally, co-exposure to TCH and PS resulted in maximum lipids (0.57 g/L) and energy (20.79 kJ/L) production, further encouraging a fantastic vision for the tertiary process of livestock effluents via advanced microalgal treatment.

Effect of CNT Oxidation on the Processing and Properties of Superacid-Spun CNT Fibers.

Spinning fibers from carbon nanotube (CNT)/superacid dispersions has emerged as a promising strategy for industrial-scale production of high-performance CNT fibers (CNTFs). The oxygen content and types of functional groups on CNT surfaces significantly influence dispersion, assembly processes, and fiber properties. In this study, Tuball-SWCNTs were purified and oxidized at varying levels. The dispersion behavior of CNTs with different oxidation levels in chlorosulfonic acid was systematically observed, and the mechanical properties of fibers spun from these dispersions were compared. By adjusting the dispersion concentration, highly oriented CNTFs were produced with a specific strength of 1.03 N/tex, a tensile strength of 1.59 GPa, and an electrical conductivity of 3.58 MS/m. Further investigations indicated that oxygen-containing functional groups decrease the coagulation rate, increasing the maximum draw ratio during spinning and improving CNT alignment in the fibers. Molecular dynamics simulations demonstrated that these functional groups (-OH, -COOH) enhance load transfer between CNTs through hydrogen bonding. This specific strength is the highest achieved using Tuball-SWCNTs for superacid-spun fibers, surpassing previous works due to the oxidation-controlled coagulation rate, enhanced fiber orientation, and improved load transfer via hydrogen bonding. This study provides insights for designing and optimizing high-performance CNTFs.

Single nucleotide and copy number variants of cancer driver genes inform drug response in multiple cancers.

Due to the heterogeneity of cancer, precision medicine has been a major challenge for cancer treatment. Determining medication regimens based on patient genotypes has become a research hotspot in cancer genomics. In this study, we aim to identify key biomarkers for targeted therapies based on single nucleotide variants (SNVs) and copy number variants (CNVs) of genes. The experiment is carried out on 7 cancers on the Encyclopedia of Cancer Cell Lines (CCLE) dataset. Considering the high mutability of driver genes which result in abundant mutated samples, the effect of data sparsity can be eliminated to a large extent. Therefore, we focus on discovering the relationship between driver mutation patterns and three measures of drug response, namely area under the curve (AUC), half maximal effective concentration (EC50), and log2-fold change (LFC). First, multiple statistical methods are applied to assess the significance of difference in drug response between sample groups. Next, for each driver gene, we analyze the extent to which its mutations can affect drug response. Based on the results of multiple hypothesis tests and correlation analyses, our main findings include the validation of several known drug response biomarkers such as BRAF, NRAS, MAP2K1, MAP2K2, and CDKN2A, as well as genes with huge potential to infer drug responses. It is worth emphasizing that we identify a list of genes including SALL4, B2M, BAP1, CCDC6, ERBB4, FOXA1, GRIN2A, and PTPRT, whose impact on drug response spans multiple cancers and should be prioritized as key biomarkers for targeted therapies. Furthermore, based on the statistical p-values and correlation coefficients, we construct gene-drug sensitivity maps for cancer drug recommendation. In this work, we show that driver mutation patterns could be used to tailor therapeutics for precision medicine.

Trilogy of comprehensive treatment of kitchen waste by bacteria-microalgae-fungi combined system: Pretreatment, water purification and resource utilization, and biomass harvesting.

Given its profound disservice, a bacteria-microalgae-fungi combined system was designed to treat kitchen waste. Firstly, a new type of microbial agent homemade compound microorganisms (HCM) (composed of Serratia marcescens, Bacillus subtilis and other 11 strains) with relatively high bio-security were developed for pretreating kitchen waste, and HCM efficiently degraded 85.2 % cellulose, 94.3 % starch, and 59.0 % oil. HCM also accomplished brilliantly the initial nutrients purification and liquefaction conversion of kitchen waste. Under mono-culture mode (fungi and microalgae were inoculated separately in the pre - and post-stages) and co-culture mode (fungi and microalgae were inoculated simultaneously in the early stage), microalgae-fungi consortia were then applied for further water purification and resource utilization of kitchen waste liquefied liquid (KWLL) produced in the pretreatment stage. Two kinds of microalgae-fungi consortia (Chlorella sp. HQ and Chlorella sp. MHQ2 form consortia with pellet-forming fungi Aspergillus niger HW8-1, respectively) removed 79.5-83.0 % chemical oxygen demand (COD), 44.0-56.5 % total nitrogen (TN), 90.3-96.4 % total phosphorus (TP), and 64.9-71.0 % NH4+-N of KWLL. What's more, the microalgae-fungi consortia constructed in this study accumulated abundant high-value substances at the same time of efficiently purifying KWLL. Finally, in the biomass harvesting stage, pellet-forming fungi efficiently harvested 81.9-82.1 % of microalgal biomass in a low-cost manner through exopolysaccharides adhesion, surface proteins interaction and charge neutralization. Compared with conventional microalgae-bacteria symbiosis system, the constructed bacteria-microalgae-fungi new-type combined system achieves the triple purpose of efficient purification, resource utilization, and biomass recovery on raw kitchen waste through the trilogy strategy, providing momentous technical references and more treatment systems selection for future kitchen waste treatment.

Growth Stage-Dependent Variation in Soil Quality and Microbial Diversity of Ancient Gleditsia sinensis.

The environment monitoring of forest is vital for the ecosystem sustainable management, especially soil quality. Ancient Gleditsia sinensis is one of the most distributed ancient trees in Shaanxi. Comprehensive soil evaluate is important for the ancient tree protection. In this study, we selected the most distributed ancient tree Gleditsia sinensis and immature tree to compare the effect of growth stage to soil quality and soil bacteria. Most ancient tree soil nutrients were in good condition compared with immature tree. The bacterial community were composed with Proteobacteria (27.55%), Acidobacteriota (16.82%), Actinobacteriota (15.77%), Gemmatimonadota (6.82%), Crenarchaeota (4.61%), Bacteroidota (4.41%), Firmicutes (4.32%), Chloroflexi (4.28%), Planctomycetota (3.24%) and Verrucomicrobiota (3.04%). The level 2 ancient tree (300-400 years old) was different in bacterial community diversity. SOC and STN were important to level 2 (300-400 years old Gleditsia sinensis), and other levels were opposite. Our results suggested that the ancient tree management should not be lumped together.

High capacities of carbon capture and photosynthesis of a novel organic carbon-fixing microalgae in municipal wastewater: From mutagenesis, screening, ability evaluation to mechanism analysis.

The development of wastewater treatment processes capable of reducing and fixing carbon is currently a hot topic in the wastewater treatment field. Microalgae possess a natural carbon-fixing advantage, and microalgae that can symbiotically coexist with indigenous bacteria in actual wastewater attract more significant attention. Ultraviolet (UV) mutagenesis and dissolved organic carbon (DOC) acclimation were applied to strengthen the carbon-fixing performance of microalgae in this study. The mechanisms associated with microalgal water purification ability, gene regulation at the molecular level and photosynthetic potential under different trophic modes resulting from carbon fixation and transformation were disclosed. The superior performance of Chlorella sp. MHQ2 was eventually screened out among a large number of mutants generated from 3 wild-type Chlorella strains. Results indicated that the dry cell weight of the optimal species Chlorella sp. HQ mutant MHQ2 was 1.91 times that of the wild strain in the pure algal system, more carbon from municipal wastewater (MW) were transferred to the microalgae and re-entered into the biological cycle through resource utilization. In addition, COD, NH3-N and TP removal efficiencies of MW by Chlorella sp. MHQ2 were found to increase to 95.8% (1.1-times), 96.4% (1.4-times), and 92.9% (1.2-times), respectively, under the extra DOC supply and the assistance of indigenous bacteria in the MW. In the transcriptome analysis of the logarithmic phase, the glycolytic pathway was inhibited, and the pentose phosphate pathway was mainly carried out for microalgal life activities, further promoting efficient energy utilization. Upon analysis of carbon capture capacity and photosynthetic potential in trophic mode, the addition of NaHCO3 increased the photosynthetic rate of Chlorella sp. MHQ2 in mixotrophy whereas it was attenuated in autotrophy. This study could provide a new perspective for the study of resource utilization and microalgae carbon- fixing mechanisms in the actual wastewater treatment process.

Ageing microenvironment mediates lymphocyte carcinogenesis and lymphoma drug resistance: From mechanisms to clinical therapy (Review).

Cellular senescence has a complex role in lymphocyte carcinogenesis and drug resistance of lymphomas. Senescent lymphoma cells combine with immunocytes to create an ageing environment that can be reprogrammed with a senescence­associated secretory phenotype, which gradually promotes therapeutic resistance. Certain signalling pathways, such as the NF­κB, Wnt and PI3K/AKT/mTOR pathways, regulate the tumour ageing microenvironment and induce the proliferation and progression of lymphoma cells. Therefore, targeting senescence­related enzymes or their signal transduction pathways may overcome radiotherapy or chemotherapy resistance and enhance the efficacy of relapsed/refractory lymphoma treatments. Mechanisms underlying drug resistance in lymphomas are complex. The ageing microenvironment is a novel factor that contributes to drug resistance in lymphomas. In terms of clinical translation, some senolytics have been used in clinical trials on patients with relapsed or refractory lymphoma. Combining immunotherapy with epigenetic drugs may achieve better therapeutic effects; however, senescent cells exhibit considerable heterogeneity and lymphoma has several subtypes. Extensive research is necessary to achieve the practical application of senolytics in relapsed or refractory lymphomas. This review summarises the mechanisms of senescence­associated drug resistance in lymphoma, as well as emerging strategies using senolytics, to overcome therapeutic resistance in lymphoma.

Pyrroloquinoline quinone production defines the ability of Devosia species to degrade deoxynivalenol.

Deoxynivalenol (DON) is a prevalent mycotoxin that primarily contaminates cereal crops and animal feed, posing a significant risk to human and animal health. In recent years, an increasing number of Devosia strains have been identified as DON degradation bacteria, and significant efforts have been made to explore their potential applications in the food and animal feed industries. However, the characteristics and mechanisms of DON degradation in Devosia strains are still unclear. In this study, we identified a novel DON degrading bacterium, Devosia sp. D-G15 (D-G15), from soil samples. The major degradation products of DON in D-G15 were 3-keto-DON, 3-epi-DON and an unidentified product, compound C. The cell viability assay showed that the DON degradation product of D-G15 revealed significantly reduced toxicity to HEK293T cells compared to DON. Three enzymes for DON degradation were further identified, with G15-DDH converting DON to 3-keto-DON and G15-AKR1/G15-AKR6 reducing 3-keto-DON to 3-epi-DON. Interestingly, genome comparison of Devosia strains showed that the pyrroloquinoline quinone (PQQ) synthesis gene cluster is a unique feature of DON degradation strains. Subsequently, adding PQQ to the cultural media of Devosia strains without PQQ synthesis genes endowed them with DON degradation activity. Furthermore, a novel DON-degrading enzyme G13-DDH (<30% homology with known DON dehydrogenase) was identified from a Devosia strain that lacks PQQ synthesis ability. In summary, a novel DON degrading Devosia strain and its key enzymes were identified, and PQQ production was found as a distinct feature among Devosia strains with DON degradation activity, which is important for developing Devosia strain-based technology in DON detoxification.

A whole process study of dual microalgae cultivation coupled to domestic wastewater treatment and wheat growth.

The multiple microalgal collaborative treatment of domestic wastewater has been extensively investigated, but its whole life cycle tracking and consequent potential have not been fully explored. Herein, a dual microalgal system was employed for domestic wastewater treatment, tracking the variation in microalgal growth and pollutants removal from shake flask scale to 18 L photobioreactors scales. The results showed that Chlorella sp. HL and Scenedesmus sp. LX1 combination had superior growth and water purification performance, and the interspecies soluble algal products promoted their growth. Through microalgae mixing ratio and inoculum size optimized, the highest biomass yield (0.42 ± 0.03 g/L) and over 91 % N, P removal rates were achieved in 18 L photobioreactor. Harvested microalgae treated in different forms all promoted wheat growth and suppressed yellow leaf rate. This study provided data support for the whole process tracking of dual microalgal system in treating domestic wastewater and improving wheat growth.

Enhancing missense variant pathogenicity prediction with protein language models using VariPred.

Computational approaches for predicting the pathogenicity of genetic variants have advanced in recent years. These methods enable researchers to determine the possible clinical impact of rare and novel variants. Historically these prediction methods used hand-crafted features based on structural, evolutionary, or physiochemical properties of the variant. In this study we propose a novel framework that leverages the power of pre-trained protein language models to predict variant pathogenicity. We show that our approach VariPred (Variant impact Predictor) outperforms current state-of-the-art methods by using an end-to-end model that only requires the protein sequence as input. Using one of the best-performing protein language models (ESM-1b), we establish a robust classifier that requires no calculation of structural features or multiple sequence alignments. We compare the performance of VariPred with other representative models including 3Cnet, Polyphen-2, REVEL, MetaLR, FATHMM and ESM variant. VariPred performs as well as, or in most cases better than these other predictors using six variant impact prediction benchmarks despite requiring only sequence data and no pre-processing of the data.

Improved hypertensive stroke classification based on multi-scale feature fusion of head axial CT angiogram and multimodal learning.

PURPOSE: Strokes are severe cardiovascular and circulatory diseases with two main types: ischemic and hemorrhagic. Clinically, brain images such as computed tomography (CT) and computed tomography angiography (CTA) are widely used to recognize stroke types. However, few studies have combined imaging and clinical data to classify stroke or consider a factor as an Independent etiology. METHODS: In this work, we propose a classification model that automatically distinguishes stroke types with hypertension as an independent etiology based on brain imaging and clinical data. We first present a preprocessing workflow for head axial CT angiograms, including noise reduction and feature enhancement of the images, followed by an extraction of regions of interest. Next, we develop a multi-scale feature fusion model that combines the location information of position features and the semantic information of deep features. Furthermore, we integrate brain imaging with clinical information through a multimodal learning model to achieve more reliable results. RESULTS: Experimental results show our proposed models outperform state-of-the-art models on real imaging and clinical data, which reveals the potential of multimodal learning in brain disease diagnosis. CONCLUSION: The proposed methodologies can be extended to create AI-driven diagnostic assistance technology for categorizing strokes.

Transrectal versus transperineal prostate biopsy for cancer detection in patients with gray-zone prostate-specific antigen: a multicenter, real-world study.

Knowledge about the effect of different prostate biopsy approaches on the prostate cancer detection rate (CDR) in patients with gray-zone prostate-specific antigen (PSA) is limited. We performed this study to compare the CDR among patients who underwent different biopsy approaches and had rising PSA levels in the gray zone. Two hundred and twenty-two patients who underwent transrectal prostate biopsy (TRB) and 216 patients who underwent transperineal prostate biopsy (TPB) between June 2016 and September 2022 were reviewed in this study. In addition, 110 patients who received additional targeted biopsies following the systematic TPB were identified. Clinical parameters, including age, PSA derivative, prostate volume (PV), and needle core count, were recorded. The data were fitted via propensity score matching (PSM), adjusting for potential confounders. TPB outperformed TRB in terms of the CDR (49.6% vs 28.3%, P = 0.001). The clinically significant prostate cancer (csPCa) detection rate was not significantly different between TPB and TRB (78.6% vs 68.8%, P = 0.306). In stratified analysis, TPB outperformed TRB in CDR when the age of patients was 65-75 years (59.0% vs 22.0%, P < 0.001), when PV was 25.00-50.00 ml (63.2% vs 28.3%, P < 0.001), and when needle core count was no more than 12 (58.5% vs 31.5%, P = 0.005). The CDR ( P = 0.712) and detection rate of csPCa ( P = 0.993) did not significantly differ among the systematic, targeted, and combined biopsies. TPB outperformed TRB in CDR for patients with gray-zone PSA. Moreover, performing target biopsy after systematic TPB provided no additional benefits in CDR.

UAV hyperspectral analysis of secondary salinization in arid oasis cotton fields: effects of FOD feature selection and SOA-RF.

Secondary salinization is a crucial constraint on agricultural progress in arid regions. The specific mulching irrigation technique not only exacerbates secondary salinization but also complicates field-scale soil salinity monitoring. UAV hyperspectral remote sensing offers a monitoring method that is high-precision, high-efficiency, and short-cycle. In this study, UAV hyperspectral images were used to derive one-dimensional, textural, and three-dimensional feature variables using Competitive adaptive reweighted sampling (CARS), Gray-Level Co-occurrence Matrix (GLCM), Boruta Feature Selection (Boruta), and Brightness-Color-Index (BCI) with Fractional-order differentiation (FOD) processing. Additionally, three modeling strategies were developed (Strategy 1 involves constructing the model solely with the 20 single-band variable inputs screened by the CARS algorithm. In Strategy 2, 25 texture features augment Strategy 1, resulting in 45 feature variables for model construction. Strategy 3, building upon Strategy 2, incorporates six triple-band indices, totaling 51 variables used in the model's construction) and integrated with the Seagull Optimization Algorithm for Random Forest (SOA-RF) models to predict soil electrical conductivity (EC) and delineate spatial distribution. The results demonstrated that fractional order differentiation highlights spectral features in noisy spectra, and different orders of differentiation reveal different hidden information. The correlation between soil EC and spectra varies with the order. 1.9th order differentiation is proved to be the best order for constructing one-dimensional indices; although the addition of texture features slightly improves the accuracy of the model, the integration of the three-waveband indices significantly improves the accuracy of the estimation, with an R2 of 0.9476. In contrast to the conventional RF model, the SOA-RF algorithm optimizes its parameters thereby significantly improving the accuracy and model stability. The optimal soil salinity prediction model proposed in this study can accurately, non-invasively and rapidly identify excessive salt accumulation in drip irrigation under membrane. It is of great significance to improve the growing conditions of cotton, increase the cotton yield, and promote the sustainable development of Xinjiang's agricultural economy, and also provides a reference for the prevention and control of regional soil salinization.

Real-world analysis of apalutamide-associated skin rash in Chinese patients with prostate cancer.

PURPOSE: This study aimed to explore the clinical characteristics of apalutamide-associated skin rash and management of skin rash in real-world Chinese patients with prostate cancer. METHODS: We investigated 138 patients with prostate cancer who received apalutamide in the Second Hospital of Tianjin Medical University from January 2022 to March 2023. The primary end points were the incidence of skin rash and the time to skin rash. The second end points were the grade of skin rash, the time to remission, the rate of recurrence of skin rash, clinical risk factors and management of skin rash. RESULTS: One hundred patients were analyzed. Patients were a median of 73 years old (IQR 68-77.75). Thirty-two patients (32%) developed apalutamide­associated skin rash. The median time to incidence and remission of skin rash were 57.5 and 11.5 days, respectively. Of 32 skin rash, 27 patients had apalutamide therapy maintained after rash remission. There were seven patients having recurrence of skin rash. By multivariable logistic regression analysis, we revealed that hypertension history (OR 3.22, 95% CI 1.09-9.53, p = 0.035), bad life-styles (OR 3.29, 95% CI 1.11-9.8, p = 0.032), ECOG ≥ 1 (OR 3.92, 95% CI 1.33-11.55, p = 0.013), and high tumor burden (OR 3.13, 95% CI 1.07-9.14, p = 0.037) were independently associated with higher incidence of skin rash. CONCLUSION: Nearly one-third of Chinese patients experienced skin rash after taking apalutamide in our study. The poor health patients might have a higher incidence of apalutamide-associated skin rash.

Based on PI-RADS v2.1 combining PHI and ADC values to guide prostate biopsy in patients with PSA 4-20 ng/mL.

PURPOSE: The study aimed to investigate the diagnostic accuracy of prostate health index (PHI) and apparent diffusion coefficient (ADC) values in predicting prostate cancer (PCa) and construct a nomogram for the prediction of PCa and clinically significant PCa (CSPCa) in Prostate Imaging-Reporting and Data System (PI-RADS) three lesions cohort. METHODS: This study prospectively enrolled 301 patients who underwent multiparametric magnetic resonance (mpMRI) and were scheduled for prostate biopsy. The receiver operating characteristic curve (ROC) was performed to estimate the diagnostic accuracy of each predictor. Univariable and multivariable logistic regression analysis was conducted to ascertain hidden risk factors and constructed nomograms in PI-RADS three lesions cohort. RESULTS: In the whole cohort, the area under the ROC curve (AUC) of PHI is relatively high, which is 0.779. As radiographic parameters, the AUC of PI-RADS and ADC values was 0.702 and 0.756, respectively. The utilization of PHI and ADC values either individually or in combination significantly improved the diagnostic accuracy of the basic model. In PI-RADS three lesions cohort, the AUC for PCa was 0.817 in the training cohort and 0.904 in the validation cohort. The AUC for CSPCa was 0.856 in the training cohort and 0.871 in the validation cohort. When applying the nomogram for predicting PCa, 50.0% of biopsies could be saved, supplemented by 6.9% of CSPCa being missed. CONCLUSION: PHI and ADC values can be used as predictors of CSPCa. The nomogram included PHI, ADC values and other clinical predictors demonstrated an enhanced capability in detecting PCa and CSPCa within PI-RADS three lesions cohort.

Collagen Anchoring Protein-Nucleic Acid Chimeric Probe for In Situ In Vivo Mapping of a Tumor-Specific Protease.

In situ analysis of biomarkers in the tumor microenvironment (TME) is important to reveal their potential roles in tumor progression and early diagnosis of tumors but remains a challenge. In this work, a bottom-up modular assembly strategy was proposed for a multifunctional protein-nucleic chimeric probe (PNCP) for in situ mapping of cancer-specific proteases. PNCP, containing a collagen anchoring module and a target proteolysis-responsive isothermal amplification sensor module, can be anchored in the collagen-rich TME and respond to the target protease in situ and generate amplified signals through rolling cycle amplification of tandem fluorescent RNAs. Taking matrix metalloproteinase 2 (MMP-2), a tumor-associated protease, as the model, the feasibility of PNCP was demonstrated for the in situ detection of MMP-2 activity in 3D tumor spheroids. Moreover, in situ in vivo mapping of MMP-2 activity was also achieved in a metastatic solid tumor model with high sensitivity, providing a useful tool for evaluating tumor metastasis and distinguishing highly aggressive forms of tumors.

A collagen-immobilized nanodevice for in situ ratiometric imaging of cancer biomarkers in the tumor microenvironment.

Monitoring the spatiotemporal dynamics of cancer biomarkers within the tumor microenvironment (TME) is critical to understanding their roles in tumorigenesis. Here, we reported a multifunctional fusion protein (collagen-binding domain and duck circovirus tag fused to mCherry, CBD-mCherry-DCV) capable of binding collagen with high affinity and covalently binding specific nucleic acids with exceptional efficiency. We then constructed a chimeric protein-nucleic acid nanodevice (CPNN) using CBD-mCherry-DCV and an aptamer-based sensing module to enable spatially controlled ratiometric imaging of cancer biomarkers in the TME. The collagen-anchoring module CBD-mCherry-DCV allowed specific immobilization of CPNN on 3D multicellular tumor spheroids, enabling the sensing module to achieve "off-on" fluorescence imaging of cancer biomarkers upon specific target recognition by an aptamer. Taking advantage of the constant fluorescence signal of mCherry and the activatable fluorescence response of Cy5 to specific cancer biomarkers, the detection sensitivity and reliability of CPNN were improved by self-calibrating the signal intensity. Specifically, CPNN enabled ratiometric fluorescence imaging of varying concentrations of exogenous PDGF-BB and ATP in tumor spheroids with a high signal-to-background ratio. Furthermore, it allowed the visual monitoring of endogenous PDGF-BB and ATP released from cells. Overall, this study demonstrates the potential of the nanodevice as a versatile approach for the visualization and imaging of cancer biomarkers in the TME.

Long-Term Prognosis of Different Subtypes of Left Ventricular Noncompaction Cardiomyopathy Patients: A Retrospective Study in China.

Left ventricular noncompaction (LVNC) is a heterogeneous cardiomyopathy that can be classified into different subtypes based on morphologic and functional features. However, the prognosis of the dilated and isolated subtypes of non-pediatric LVNC remains unknown. We retrospectively studied 101 patients with LVNC diagnosed at Peking Union Medical College Hospital from 2006 to 2022 using the Jenni criteria of transthoracic echocardiography. The patients were grouped into those with dilated LVNC (n = 64) or isolated LVNC (n = 37), and 88 patients (54 with dilated LVNC and 34 with isolated LVNC) were followed up successfully. The primary outcome was major adverse cardiovascular events (a composite of cardiovascular mortality, heart failure, severe ventricular arrhythmia, and systolic embolism). The median follow-up time was 5.24 years. The incidence of major adverse cardiovascular events was 43.2%; patients with dilated LVNC had a higher risk (adjusted hazard ratio, 4.43; 95% confidence interval, 1.24-15.81; p = 0.02) than those with isolated LVNC. None of the isolated LVNC patients had cardiovascular deaths or severe ventricular arrhythmias. The risk of systemic embolism was similar between patients with dilated and isolated LVNC. Our findings indicate that transthoracic echocardiography is a useful tool for classifying LVNC into subtypes with distinct clinical outcomes. Dilated LVNC is associated with a poor prognosis, while the isolated subtype is probably a physiological condition.