The efficacy of the combination of venetoclax and hypomethylating agents versus HAG agents in patients with acute myeloid leukemia: a retrospective study.

OBJECTIVES: The purpose of this study was to compare the effectiveness of the combination of venetoclax and hypomethylating agents with the HAG regimen. METHODS: We studied 52 cases of newly diagnosed AML and 26 cases of relapsed refractory AML, (including AML patients with treatment-related and ELN-adverse risk disease (n = 50)). These patients were treated with venetoclax and hypomethylating agents and HAG regimens, respectively. RESULTS: Twenty-nine patients newly diagnosed with acute myeloid leukemia were treated with VEN-HMA (venetoclax-hypomethylating agent), while 23 patients were treated with HAG. The median age of the VEN-HMA group was 70 years, while the HAG group had a median age of 69 years. The VEN-HMA group achieved a significantly higher rate of complete remission (82.7%) compared to the cohort treated with the HAG regimen (21.7%) (P < 0.001). At the same time, the VEN-HMA group exhibited a significant survival advantage compared to the HAG treatment group(HR = 0.328, 95%CI: 0.158-0.683, P = 0.003).In patients with relapsed and refractory acute myeloid leukaemia, 43.8% of patients in the VEN-HMA treatment group achieved complete remission, which was similar to the 50% in the HAG treatment group (P > 0.99). The median overall survival was similar between the VEN-HMA and HAG groups, with 4 and 3.67 months, respectively (P = 0.290). CONCLUSIONS: In conclusion, our analyses indicated that VEN-HMA resulted in better therapeutic outcomes compared to HAG for newly diagnosed AML patients, with higher rates of complete remission and overall survival. In relapsed/refractory AML patients, there was no significant difference in the efficacy of the two treatments and further studies with larger sample sizes are warranted.

Genome-wide analysis of in vivo-evolved Candida auris reveals multidrug-resistance mechanisms.

Candida auris, an emerging and multidrug-resistant fungal pathogen, has led to numerous outbreaks in China. While the resistance mechanisms against azole and amphotericin B have been studied, the development of drug resistance in this pathogen remains poorly understood, particularly in in vivo-generated drug-resistant strains. This study employed pathogen whole-genome sequencing to investigate the epidemiology and drug-resistance mutations of C. auris using 16 strains isolated from two patients. Identification was conducted through Matrix-assisted laser desorption/ionization time-of-flight mass spectrometry, and antimicrobial susceptibilities were assessed using broth microdilution and Sensititre YeastOne YO10. Whole-genome sequencing revealed that all isolates belonged to the South Asian lineage, displaying genetic heterogeneity. Despite low genetic variability among patient isolates, notable mutations were identified, including Y132F in ERG11 and A585S in TAC1b, likely linked to increased fluconazole resistance. Strains from patient B also carried F214L in TAC1b, resulting in a consistent voriconazole minimum inhibitory concentration of 4 µg/mL across all isolates. Furthermore, a novel frameshift mutation in the SNG1 gene was observed in amphotericin B-resistant isolates compared to susceptible ones. Our findings suggest the potential transmission of C. auris and emphasize the need to explore variations related to antifungal resistance. This involves analyzing genomic mutations and karyotypes, especially in vivo, to compare sensitive and resistant strains. Further monitoring and validation efforts are crucial for a comprehensive understanding of the mechanisms of drug resistance in C. auris.

MHESMMR: a multilevel model for predicting the regulation of miRNAs expression by small molecules.

According to the expression of miRNA in pathological processes, miRNAs can be divided into oncogenes or tumor suppressors. Prediction of the regulation relations between miRNAs and small molecules (SMs) becomes a vital goal for miRNA-target therapy. But traditional biological approaches are laborious and expensive. Thus, there is an urgent need to develop a computational model. In this study, we proposed a computational model to predict whether the regulatory relationship between miRNAs and SMs is up-regulated or down-regulated. Specifically, we first use the Large-scale Information Network Embedding (LINE) algorithm to construct the node features from the self-similarity networks, then use the General Attributed Multiplex Heterogeneous Network Embedding (GATNE) algorithm to extract the topological information from the attribute network, and finally utilize the Light Gradient Boosting Machine (LightGBM) algorithm to predict the regulatory relationship between miRNAs and SMs. In the fivefold cross-validation experiment, the average accuracies of the proposed model on the SM2miR dataset reached 79.59% and 80.37% for up-regulation pairs and down-regulation pairs, respectively. In addition, we compared our model with another published model. Moreover, in the case study for 5-FU, 7 of 10 candidate miRNAs are confirmed by related literature. Therefore, we believe that our model can promote the research of miRNA-targeted therapy.

Real-world data on neoadjuvant chemotherapy with dual-anti HER2 therapy in HER2 positive breast cancer.

BACKGROUND: Neoadjuvant chemotherapy with dual-targeted therapy is the standard treatment for human epidermal growth factor 2 (HER2)-positive breast cancer. Although the dual-targeted therapy has significantly improved the pathological complete response (pCR) rate, further investigation is needed to identify biomarkers that predict the response to neoadjuvant therapy. METHODS: This retrospective study analyzed 353 patients with HER2-positive breast invasive ductal carcinoma. The correlation between clinicopathological factors and pCR rate was evaluated. A nomogram was constructed based on the results of the multivariate logistic regression analysis to predict the probability of pCR. RESULTS: The breast pCR (b-pCR) rate was 56.1% (198/353) and the total pCR (t-pCR) rate was 52.7% (186/353). Multivariate analysis identified ER status, PR status, HER2 status, Ki-67 index, and neoadjuvant chemotherapy regimens as independent indicators for both b-pCR and t-pCR. The nomogram had an area under the receiver operating characteristic curve (AUC) of 0.73 (95% CI: 0.68-0.78). According to the nomogram, the t- pCR rate was highest in the ER-PR- HER2-positive patients (131/208) and lowest in the ER + PR + HER2-positive patients (19/73). The subgroup analyses showed that there was no significant difference in pCR rate among the neoadjuvant chemotherapy regimens in ER positive, PR positive, HER2 IHC 2 + , Ki67 index < 30% population. However, for ER-PR-HER2-positive patients, the neoadjuvant chemotherapy regimen has a great influence on the pCR rates. CONCLUSIONS: Patients with ER-negative, PR-negative, HER2 3 + and high KI-67 index were more likely to achieve pCR. THP may be used as an alternative to AC-THP or TCbHP in selected HER2-positive patients.

Hydrogel Crosslinked with Nanoparticles for Prevention of Surgical Hemorrhage and Recurrence of Hepatocellular Carcinoma.

Hepatocellular carcinoma (HCC) is acknowledged as an immunosuppressive neoplasm, whereby the inactive microenvironment facilitates immune tolerance and evasion of HCC. Post-surgical resected liver cancer exhibits a proclivity for relapse, rendering prevention of recurrence challenging as it may transpire at any point subsequent to surgery. Among the various anti-recurrence interventions, the primary clinical approach involving the administration of regimens atezolizumab and bevacizumab (A+T) is deemed the most efficacious in reversing the tumor microenvironment, albeit still lacking in complete satisfaction. Therefore, the objective is to utilize a recently developed block copolymer as a protective carrier for two specific monoclonal antibody drugs. Subsequently, a modified hemostatic hydrogel will be synthesized for application during hepatic surgery. The immunotherapy impact of this approach is significantly prolonged and intensified due to the combined hemostasis properties and controlled release of the constituents within the synthesized nanocomposite hydrogel. Furthermore, these nanocomposite hydrogels exhibit remarkable efficacy in preventing postoperative wound bleeding and substantially enhancing the safety of liver cancer resection. This research on the anti-recurrence hydrogel system presents a novel therapeutic approach for addressing local recurrence of liver cancer, potentially offering a substantial contribution to the field of surgical treatment for liver cancer in the future.

Study on Cavitation, Warpage Deformation, and Moisture Diffusion of Sop-8 Devices during Molding Process.

Plastic packaging has shown its advantages over ceramic packaging and metal packaging in lightweight, thin, and high-density electronic devices. In this paper, the reliability and moisture diffusion of Sop-8 (Small Out-Line Package-8) plastic packaging devices are studied, and we put forward a set of complete optimization methods. Firstly, we propose to improve the reliability of plastic packaging devices by reducing the amount of cavitation and warpage deformation. Structural and process factors were investigated in the injection molding process. An orthogonal experiment design was used to create 25 groups of simulation experiments, and Moldflow software was used to simulate the flow mode analysis. Then, the simulation results are subjected to range analysis and comprehensive weighted score analysis. Finally, different optimization methods are proposed according to different production conditions, and each optimization method can reduce cavitation or warpage by more than 9%. The moisture diffusion of the Sop-8 plastic packing devices was also investigated at the same time. It was determined that the contact surface between the lead frame and the plastic packaging material was more likely to exhibit delamination under the condition of MSL2 moisture diffusion because the humidity gradient was easily produced at the crucial points of different materials. The diffusion of moisture is related to the type of plastic packaging material and the diffusion path.

An efficient circRNA-miRNA interaction prediction model by combining biological text mining and wavelet diffusion-based sparse network structure embedding.

MOTIVATION: Accumulating clinical evidence shows that circular RNA (circRNA) plays an important regulatory role in the occurrence and development of human diseases, which is expected to provide a new perspective for the diagnosis and treatment of related diseases. Using computational methods can provide high probability preselection for wet experiments to save resources. However, due to the lack of neighborhood structure in sparse biological networks, the model based on network embedding and graph embedding is difficult to achieve ideal results. RESULTS: In this paper, we propose BioDGW-CMI, which combines biological text mining and wavelet diffusion-based sparse network structure embedding to predict circRNA-miRNA interaction (CMI). In detail, BioDGW-CMI first uses the Bidirectional Encoder Representations from Transformers (BERT) for biological text mining to mine hidden features in RNA sequences, then constructs a CMI network, obtains the topological structure embedding of nodes in the network through heat wavelet diffusion patterns. Next, the Denoising autoencoder organically combines the structural features and Gaussian kernel similarity, finally, the feature is sent to lightGBM for training and prediction. BioDGW-CMI achieves the highest prediction performance in all three datasets in the field of CMI prediction. In the case study, all the 8 pairs of CMI based on circ-ITCH were successfully predicted. AVAILABILITY: The data and source code can be found at https://github.com/1axin/BioDGW-CMI-model.

KS-CMI: A circRNA-miRNA interaction prediction method based on the signed graph neural network and denoising autoencoder.

Circular RNA (circRNA) plays an important role in the diagnosis, treatment, and prognosis of human diseases. The discovery of potential circRNA-miRNA interactions (CMI) is of guiding significance for subsequent biological experiments. Limited by the small amount of experimentally supported data and high randomness, existing models are difficult to accomplish the CMI prediction task based on real cases. In this paper, we propose KS-CMI, a novel method for effectively accomplishing CMI prediction in real cases. KS-CMI enriches the 'behavior relationships' of molecules by constructing circRNA-miRNA-cancer (CMCI) networks and extracts the behavior relationship attribute of molecules based on balance theory. Next, the denoising autoencoder (DAE) is used to enhance the feature representation of molecules. Finally, the CatBoost classifier was used for prediction. KS-CMI achieved the most reliable prediction results in real cases and achieved competitive performance in all datasets in the CMI prediction.

DeepCMI: a graph-based model for accurate prediction of circRNA-miRNA interactions with multiple information.

Recently, the role of competing endogenous RNAs in regulating gene expression through the interaction of microRNAs has been closely associated with the expression of circular RNAs (circRNAs) in various biological processes such as reproduction and apoptosis. While the number of confirmed circRNA-miRNA interactions (CMIs) continues to increase, the conventional in vitro approaches for discovery are expensive, labor intensive, and time consuming. Therefore, there is an urgent need for effective prediction of potential CMIs through appropriate data modeling and prediction based on known information. In this study, we proposed a novel model, called DeepCMI, that utilizes multi-source information on circRNA/miRNA to predict potential CMIs. Comprehensive evaluations on the CMI-9905 and CMI-9589 datasets demonstrated that DeepCMI successfully infers potential CMIs. Specifically, DeepCMI achieved AUC values of 90.54% and 94.8% on the CMI-9905 and CMI-9589 datasets, respectively. These results suggest that DeepCMI is an effective model for predicting potential CMIs and has the potential to significantly reduce the need for downstream in vitro studies. To facilitate the use of our trained model and data, we have constructed a computational platform, which is available at http://120.77.11.78/DeepCMI/. The source code and datasets used in this work are available at https://github.com/LiYuechao1998/DeepCMI.

Association of tumor morphology with long-term prognosis after liver resection for patients with a solitary huge hepatocellular carcinoma-a multicenter propensity score matching analysis.

Background: A solitary hepatocellular carcinoma (HCC) without macrovascular invasion and distant metastasis, regardless of tumor size, is currently classified as early-stage disease by the latest Barcelona Clinic Liver Cancer (BCLC) staging system. While the preferred treatment is surgical resection, the association of tumor morphology with long-term survival outcomes after liver resection for a solitary huge HCC of ≥10 cm has not been defined. Methods: Patients who underwent curative liver resection for a solitary huge HCC were identified from a multicenter database. Preoperative imaging findings were used to define spherical- or ellipsoidal-shaped lesions with smooth edges as balloon-shaped HCCs (BS-HCCs); out-of-shape lesions or lesions of any shape with matt edges were defined as non-balloon-shaped HCCs (NBS-HCCs). The two groups of patients with BS-HCCs and NBS-HCCs were matched in a 1:1 ratio using propensity score matching (PSM). Clinicopathologic characteristics, long-term overall survival (OS) and recurrence-free survival (RFS) were assessed. Results: Among patients with a solitary huge HCC, 74 pairs of patients with BS-HCC and NBS-HCC were matched. Tumor pathological features including proportions of microvascular invasion, satellite nodules, and incomplete tumor encapsulation in the BS-HCC group were lower than the NBS-HCC group. At a median follow-up of 50.7 months, median OS and RFS of all patients with a solitary huge HCC after PSM were 27.8 and 10.1 months, respectively. The BS-HCC group had better median OS and RFS than the NBS-HCC group (31.9 vs. 21.0 months, P=0.01; and 19.7 vs. 6.4 months, P=0.015). Multivariate analyses identified BS-HCC as independently associated with better OS (HR =0.592, P=0.009) and RFS (HR =0.633, P=0.013). Conclusions: For a solitary huge HCC, preoperative imaging on tumor morphology was associated with prognosis following resection. In particular, patients with BS-HCCs had better long-term survival following liver resection versus patients with large NBS-HCCs.

Predictors and long-term prognosis of early and late recurrence for patients undergoing hepatic resection of hepatocellular carcinoma: a large-scale multicenter study.

Background: Recurrence is common among patients undergoing hepatic resection for hepatocellular carcinoma (HCC), which greatly limits long-term survival. We aimed to identify predictors and long-term prognosis of early and late recurrence after HCC resection. Methods: Multicenter data of patients who underwent HCC resection between 2002 and 2016 were analyzed. Recurrence was divided into early (≤2 years) and late recurrence (>2 years after surgery). Predictors of early and late recurrence, and prognostic factors of post-recurrence survival (PRS) were identified by univariate and multivariate analyses. Results: Among 1,426 patients, 554 (38.8%) and 348 (24.4%) developed early and late recurrence, respectively. Independent predictors associated with early recurrence included preoperative alpha-fetoprotein level >400 µg/L, resection margin <1 cm, and tumor size >5.0 cm, multiplicity, macrovascular and microvascular invasion, and satellites of the initial tumor at the first diagnosis of HCC; independent predictors associated with late recurrence included male, cirrhosis, and tumor size >5.0 cm, multiplicity, macrovascular and microvascular invasion, and satellites of the initial tumor. Patients with early recurrence had a lower likelihood of undergoing potentially curative treatments for recurrence (37.2% vs. 48.0%, P<0.001) and a worse median PRS (13.5 vs. 36.6 months, P<0.001) vs. patients who had late recurrence. Multivariate analysis revealed that early recurrence and irregular postoperative surveillance were independently associated with worse PRS [hazard ratio (HR) =1.250, 95% CI: 1.016-1.538, P=0.035; and HR =1.983, 95% CI: 1.677-2.345, P<0.001]. Conclusions: Predictors associated with early and late recurrence after curative resection for patients with HCC were generally same, although several did differ. Patients with late recurrence had better long-term survival than patients with early recurrence.

A feature extraction method based on noise reduction for circRNA-miRNA interaction prediction combining multi-structure features in the association networks.

MOTIVATION: A large number of studies have shown that circular RNA (circRNA) affects biological processes by competitively binding miRNA, providing a new perspective for the diagnosis, and treatment of human diseases. Therefore, exploring the potential circRNA-miRNA interactions (CMIs) is an important and urgent task at present. Although some computational methods have been tried, their performance is limited by the incompleteness of feature extraction in sparse networks and the low computational efficiency of lengthy data. RESULTS: In this paper, we proposed JSNDCMI, which combines the multi-structure feature extraction framework and Denoising Autoencoder (DAE) to meet the challenge of CMI prediction in sparse networks. In detail, JSNDCMI integrates functional similarity and local topological structure similarity in the CMI network through the multi-structure feature extraction framework, then forces the neural network to learn the robust representation of features through DAE and finally uses the Gradient Boosting Decision Tree classifier to predict the potential CMIs. JSNDCMI produces the best performance in the 5-fold cross-validation of all data sets. In the case study, seven of the top 10 CMIs with the highest score were verified in PubMed. AVAILABILITY: The data and source code can be found at https://github.com/1axin/JSNDCMI.

Association of severity in the grading of microvascular invasion with long-term oncological prognosis after liver resection for early-stage hepatocellular carcinoma: a multicenter retrospective cohort study from a hepatitis B virus-endemic area.

BACKGROUND: The presence of microvascular invasion (MVI) is a significant malignant pathological feature related to recurrence and survival after liver resection for hepatocellular carcinoma (HCC). This study aimed to investigate the relationship between the severity in the grading of MVI and long-term oncological outcomes in patients with early-stage HCC. METHODS: A retrospective study was conducted on a prospectively maintained multicenter database on patients who underwent curative resection for Barcelona Clinic Liver Cancer stage 0/A HCC between 2017 and 2020. Patients were classified into three groups according to the severity in the grading of MVI: M0 (no MVI), M1 (1-5 sites of MVI occurring ≤1 cm away from the tumor), and M2 (>5 sites occurring ≤1 cm and/or any site occurring >1 cm away from the tumor). Recurrence-free survival (RFS) and overall survival (OS) were compared among the groups. RESULTS: Of 388 patients, M0, M1, and M2 of the MVI gradings were present in 223 (57.5%), 118 (30.4%), and 47 (12.1%) patients, respectively. The median OS and RFS in patients with M0, M1, and M2 were 61.1, 52.7, and 27.4 months; and 43.0, 29.1, and 13.1 months (both P <0.001), respectively. Multivariable analyses identified both M1 and M2 to be independent risk factors for OS [hazard ratio (HR): 1.682, P =0.003; and HR: 3.570, P <0.001] and RFS (HR: 1.550, P =0.037; and HR: 2.256, P <0.001). CONCLUSION: The severity in the grading of MVI was independently associated with recurrence and survival after HCC resection. Patients with the presence of MVI, especially those with a more severe MVI grading (M2), require more stringent recurrence surveillance and/or active adjuvant therapy against recurrence.

Associations between 24-h movement behaviours and BMI in Chinese primary- and middle- school students.

Purposes: This cross-sectional study aimed to examine the associations between the 24-h movement behaviours and body mass index (BMI) of students from China by using compositional data analysis. Methods: A total of 389 students aged 6-16 years participated in this study. Accelerometers were used to measure moderate-to-vigorous physical activity (MVPA), light-intensity physical activity (LPA), sedentary behaviour (SED), and sleep. Weight and height were objectively measured. The association between 24-h movement and BMI was analyzed by using compositional data analysis. Results: Time reallocation using minutes and proportions created major differences to the results. Reallocating 10 min from other movement behaviours to MVPA was associated with decreased BMI z-score of 1.372 to 0.158 among primary-school students. Reallocating 10 min from sleep and SED to MVPA, and from sleep and SED to LPA were associated with decreased BMI z-score of 0.505 to 0.017 among middle-school students. Reallocating 10% of time from all other components to SED and sleep were associated with a higher BMI z-score by 0.148 (95%CI: 0.020; 0.276) and 0.125 (95%CI: 0.046; 0.204), while reallocating time to MVPA was associated with a decrease in BMI z-scores of 0.132 (95%CI: -0.193; -0.070) among primary-school students. Reallocating 10% of time from all other components to SED was associated with a higher BMI z-score of 0.254 (95%CI: 0.165; 0.345), whereas reallocating time to MVPA and LPA was associated with a decrease in BMI z-scores of 0.039 (95%CI: -0.073; -0.005) and 0.093 (95%CI: -0.153; -0.033) among middle-school students. Conclusion: Research results of 10-min one-to-one reallocation may be treated cautiously due to uneven distribution of time in 24-h movement behaviours. Based on the results of 10% one-to-remaining reallocation, replacing SED with MVPA may be an appropriate target for adiposity intervention in primary-school students, while increasing MVPA or LPA at the expense of SED may be effective in controlling adiposity of middle-school students in China.

LPIH2V: LncRNA-protein interactions prediction using HIN2Vec based on heterogeneous networks model.

LncRNA-protein interaction plays an important role in the development and treatment of many human diseases. As the experimental approaches to determine lncRNA-protein interactions are expensive and time-consuming, considering that there are few calculation methods, therefore, it is urgent to develop efficient and accurate methods to predict lncRNA-protein interactions. In this work, a model for heterogeneous network embedding based on meta-path, namely LPIH2V, is proposed. The heterogeneous network is composed of lncRNA similarity networks, protein similarity networks, and known lncRNA-protein interaction networks. The behavioral features are extracted in a heterogeneous network using the HIN2Vec method of network embedding. The results showed that LPIH2V obtains an AUC of 0.97 and ACC of 0.95 in the 5-fold cross-validation test. The model successfully showed superiority and good generalization ability. Compared to other models, LPIH2V not only extracts attribute characteristics by similarity, but also acquires behavior properties by meta-path wandering in heterogeneous networks. LPIH2V would be beneficial in forecasting interactions between lncRNA and protein.

Age-dependent changes in the dynamic functional organization of the brain at rest: a cross-cultural replication approach.

Age-associated changes in brain function play an important role in the development of neurodegenerative diseases. Although previous work has examined age-related changes in static functional connectivity, accumulating evidence suggests that advancing age is especially associated with alterations in the dynamic interactions and transitions between different brain states, which hitherto have received less attention. Conclusions of previous studies in this domain are moreover limited by suboptimal replicability of resting-state functional magnetic resonance imaging (fMRI) and culturally homogenous cohorts. Here, we investigate the robustness of age-associated changes in dynamic functional connectivity (dFC) by capitalizing on the availability of fMRI cohorts from two cultures (Western European and Chinese). In both the LEMON (Western European) and SALD (Chinese) cohorts, we consistently identify two distinct states: a more frequent segregated within-network connectivity state (state I) and a less frequent integrated between-network connectivity state (state II). Moreover, in both these cohorts, older (55-80 years) compared to younger participants (20-35 years) exhibited lower occurrence of and spent less time in state I. Older participants also tended to exhibit more transitions between networks and greater variance in global efficiency. Overall, our cross-cultural replication of age-associated changes in dFC metrics implies that advancing age is robustly associated with a reorganization of dynamic brain activation that favors the use of less functionally specific networks.

Opposing and emotion-specific associations between frontal activation with depression and anxiety symptoms during facial emotion processing in generalized anxiety and depression.

Major depression (MDD) and generalized anxiety disorder (GAD) have become one of the leading global causes of disability and both are characterized by marked interpersonal and social impairments. However, despite high comorbidity and overlapping social-emotional deficits, it remains unclear whether MDD and GAD share a common neural basis during interpersonal processing. In the present study, we combined an emotional face processing paradigm with fMRI and dimensional and categorical analyses in a sample of unmedicated MDD and GAD patients (N = 72) as well as healthy controls (N = 35). No group differences were found in categorical analyses. However, the dimensional analyses revealed that dorsolateral prefrontal cortex (dlPFC) reactivity to sad facial expressions was positively associated with depression symptom load, yet negatively associated with anxiety symptom load in the entire sample. On the network level depression symptom load was positively associated with functional connectivity between the bilateral amygdala and a widespread network including the anterior cingulate and insular cortex. Together, these findings suggest that the dlPFC - engaged in cognitive and emotional processing - exhibits symptom- and emotion-specific alteration during interpersonal processing. Dysregulated communication between the amygdala and core regions of the salience network may represent depression-specific neural dysregulations.