Shared genetic factors and causal association between chronic hepatitis C infection and diffuse large B cell lymphoma.

BACKGROUND: Epidemiological research and systematic meta-analyses indicate a higher risk of B-cell lymphomas in patients with chronic hepatitis C virus (HCV) compared to non-infected individuals. However, the genetic links between HCV and these lymphomas remain under-researched. METHODS: Mendelian randomization analysis was employed to explore the association between chronic hepatitis C (CHC) and B-cell lymphomas as well as chronic lymphocytic leukemia (CLL). Approximate Bayes Factor (ABF) localization analysis was conducted to find shared genetic variants that might connect CHC with B-cell lymphomas and chronic lymphocytic leukemia (CLL). Furthermore, The Variant Effect Predictor (VEP) was utilized to annotate the functional effects of the identified genetic variants. RESULTS: Mendelian randomization revealed a significant association between CHC and increased diffuse large B cell lymphoma (DLBCL) risk (OR: 1.34; 95% CI: 1.01-1.78; P = 0.0397). Subsequent colocalization analysis pinpointed two noteworthy variants, rs17208853 (chr6:32408583) and rs482759 (chr6:32227240) between these two traits. The annotation of these variants through the VEP revealed their respective associations with the butyrophilin-like protein 2 (BTNL2) and notch receptor 4 (NOTCH4) genes, along with the long non-coding RNA (lncRNA) TSBP1-AS1. CONCLUSION: This research provides a refined genetic understanding of the CHC-DLBCL connection, opening avenues for targeted therapeutic research and intervention.

A CT-based radiomics nomogram involving the cystic fluid area for differentiating appendiceal mucinous neoplasms from appendicitis with intraluminal fluid.

OBJECTIVE: To develop and validate a radiomics nomogram based on computed tomography (CT) to distinguish appendiceal mucinous neoplasms (AMNs) from appendicitis with intraluminal fluid (AWIF). METHOD: A total of 211 patients from two medical institutions were retrospectively analysed, of which 109 were pathologically confirmed as having appendicitis with concomitant CT signs of intraluminal fluid and 102 as having AMN. All patients were randomly assigned to a training (147 patients) or validation cohort (64 patients) at a 7:3 ratio. Radiomics features of the cystic fluid area of the appendiceal lesions were extracted from nonenhanced CT images using 3D Slicer software. Minimum redundancy maximum relevance and least absolute shrinkage and selection operator regression methods were employed to screen the radiomics features and develop a radiomics model. Combined radiomics nomogram and clinical-CT models were further developed based on the corresponding features selected after multivariate analysis. Lastly, receiver operating characteristic curves, and decision curve analysis (DCA) were used to assess the models' performances in the training and validation cohorts. RESULTS: A total of 851 radiomics features were acquired from the nonenhanced CT images. Subsequently, a radiomics model consisting of eight selected features was developed. The combined radiomics nomogram model comprised rad-score, age, and mural calcification, while the clinical-CT model contained age and mural calcification. The combined model achieved area under the curves (AUCs) of 0.945 (95% confidence interval [CI]: 0.895, 0.976) and 0.933 (95% CI: 0.841, 0.980) in the training and validation cohorts, respectively, which were larger than those obtained by the radiomics (training cohort: AUC, 0.915 [95% CI: 0.865, 0.964]; validation cohort: AUC, 0.912 [95% CI: 0.843, 0.981]) and clinical-CT models (training cohort: AUC, 0.884 [95% CI: 0.820, 0.931]; validation cohort: AUC, 0.767 [95% CI: 0.644, 0.863]). Finally, DCA showed that the clinical utility of the combined model was superior to that of the clinical CT and radiomics models. CONCLUSION: Our combined radiomics nomogram model constituting radiomics, clinical, and CT features exhibited good performance for differentiating AMN from AWIF, indicating its potential application in clinical decision-making.

Hub genes and associated drugs for multiple myeloma with 1q21+: identified by bioinformatic analysis.

While 1q21+ was common genetic alteration and found to have adverse effect on prognosis, the underlying genes remain unclear. Identification of related genes may provide additional help for rational intervention. The microarray dataset GSE2658 associated with MM was downloaded from the Gene Expression Omnibus (GEO) database. The differentially expressed genes (DEGs) were obtained, and Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analyses were performed to annotate their functions. The hub genes were derived from the combined results of up-regulated DEGs and weighted gene coexpression network analysis (WGCNA). The receiver operating characteristic (ROC) curves of hub genes were plotted to evaluate correlation with 1q21+. Survival analysis and drug-gene interaction of hub genes were performed separately to find the prognostic value and potential targeted drugs. A total of 55 DEGs were identified. GO and KEGG pathway analyses suggested that the DEGs were related to several pathways of cell proliferation. NVL, IL6R, DUSP23 were proven to be highly correlated with 1q21+ and have adverse effects on prognosis. IL6R, DUSP23 were matched to known interaction-drug. This study revealed potential roles of hub genes in the pathogenesis and progression of MM with 1q21+, further investigations are needed to elucidate the mechanisms.

Causal relationships between circulating inflammatory cytokines and diffuse large B cell lymphoma: a bidirectional Mendelian randomization study.

Diffuse large B cell lymphoma (DLBCL) is the most common type of non-Hodgkin lymphoma. Studies indicated that inflammatory cytokines involved in the occurrence and progression of DLBCL and it is challenging to discern causality from the effects due to the presence of feedback loops. We conducted a bidirectional Mendelian randomization (MR) study to investigate the potential causal relationship between DLBCL and inflammatory cytokines. The genetic variants associated with inflammatory cytokines were obtained from a genome-wide association study (GWAS) involving 8293 European participants, and the data on 1010 individuals with DLBCL were sourced from the FinnGen consortium. The primary method employed in this study was the inverse-variance weighted (IVW) method, with supplementary analyses conducted using the MR-Egger, weighted median, and MR-PRESSO approaches. Based on the IVW method, genetically predicted that increasing level of Monokine induced by interferon gamma (MIG/CXC chemokine ligand 9, CXCL9) [OR: 1.31; 95% CI: 1.05-1.62; P = 0.01] and interferon gamma-induced protein 10(IP-10/CXC chemokine ligand 10, CXCL10) [OR: 1.30; 95% CI: 1.02-1.66; P = 0.03] showed suggestive associations with DLBCL risk. DLBCL may increase the level of macrophage colony-stimulating factor (M-CSF) [OR: 1.12; 95% CI: 1.01-1.2; P = 0.03], tumor necrosis factor beta (TNF-ß) [OR: 1.16; 95% CI: 1.02-1.31; P = 0.02] and TNF-related apoptosis-inducing ligand (TRAIL) [OR: 1.07; 95% CI: 1.01-1.13; P = 0.02]. This study presents evidence supporting a causal relationship between inflammation cytokines and DLBCL. Specifically, MIG/CXCL9 and IP-10/CXCL10 were identified as indicators of upstream causes of DLBCL; while, DLBCL itself was found to elevate the levels of M-CSF, TNF-ß, and TRAIL. These findings suggest that targeting specific inflammatory factors through regulation and intervention could serve as a potential approach for the treatment and prevention of DLBCL.

A CT-based nomogram established for differentiating gastrointestinal heterotopic pancreas from gastrointestinal stromal tumor: compared with a machine-learning model.

OBJECTIVE: To identify CT features and establish a nomogram, compared with a machine learning-based model for distinguishing gastrointestinal heterotopic pancreas (HP) from gastrointestinal stromal tumor (GIST). MATERIALS AND METHODS: This retrospective study included 148 patients with pathologically confirmed HP (n = 48) and GIST (n = 100) in the stomach or small intestine that were less than 3 cm in size. Clinical information and CT characteristics were collected. A nomogram on account of lasso regression and multivariate logistic regression, and a RandomForest (RF) model based on significant variables in univariate analyses were established. Receiver operating characteristic (ROC) curve, mean area under the curve (AUC), calibration curve and decision curve analysis (DCA) were carried out to evaluate and compare the diagnostic ability of models. RESULTS: The nomogram identified five CT features as independent predictors of HP diagnosis: age, location, LD/SD ratio, duct-like structure, and HU lesion/pancreas A. Five features were included in RF model and ranked according to their relevance to the differential diagnosis: LD/SD ratio, HU lesion/pancreas A, location, peritumoral hypodensity line and age. The nomogram and RF model yielded AUC of 0.951 (95% CI: 0.842-0.993) and 0.894 (95% CI: 0.766-0.966), respectively. The DeLong test found no statistically significant difference in diagnostic performance (p > 0.05), but DCA revealed that the nomogram surpassed the RF model in clinical usefulness. CONCLUSION: Two diagnostic prediction models based on a nomogram as well as RF method were reliable and easy-to-use for distinguishing between HP and GIST, which might also assist treatment planning.

Duodenal neuroendocrine neoplasms on enhanced CT: establishing a diagnostic model with duodenal gastrointestinal stromal tumors in the non-ampullary area and analyzing the value of predicting prognosis.

OBJECTIVE: To identify CT features and establish a diagnostic model for distinguishing non-ampullary duodenal neuroendocrine neoplasms (dNENs) from non-ampullary duodenal gastrointestinal stromal tumors (dGISTs) and to analyze overall survival outcomes of all dNENs patients. MATERIALS AND METHODS: This retrospective study included 98 patients with pathologically confirmed dNENs (n = 44) and dGISTs (n = 54). Clinical data and CT characteristics were collected. Univariate analyses and binary logistic regression analyses were performed to identify independent factors and establish a diagnostic model between non-ampullary dNENs (n = 22) and dGISTs (n = 54). The ROC curve was created to determine diagnostic ability. Cox proportional hazards models were created and Kaplan-Meier survival analyses were performed for survival analysis of dNENs (n = 44). RESULTS: Three CT features were identified as independent predictors of non-ampullary dNENs, including intraluminal growth pattern (OR 0.450; 95% CI 0.206-0.983), absence of intratumoral vessels (OR 0.207; 95% CI 0.053-0.807) and unenhanced lesion > 40.76 HU (OR 5.720; 95% CI 1.575-20.774). The AUC was 0.866 (95% CI 0.765-0.968), with a sensitivity of 90.91% (95% CI 70.8-98.9%), specificity of 77.78% (95% CI 64.4-88.0%), and total accuracy rate of 81.58%. Lymph node metastases (HR: 21.60), obstructive biliary and/or pancreatic duct dilation (HR: 5.82) and portal lesion enhancement ≤ 99.79 HU (HR: 3.02) were independent prognostic factors related to poor outcomes. CONCLUSION: We established a diagnostic model to differentiate non-ampullary dNENs from dGISTs. Besides, we found that imaging features on enhanced CT can predict OS of patients with dNENs.

Extracellular matrix modulating enzyme functionalized biomimetic Au nanoplatform-mediated enhanced tumor penetration and synergistic antitumor therapy for pancreatic cancer.

BACKGROUND: Excessive extracellular matrix (ECM) deposition in pancreatic ductal adenocarcinoma (PDAC) severely limits therapeutic drug penetration into tumors and is associated with poor prognosis. Collagen is the most abundant matrix protein in the tumor ECM, which is the main obstacle that severely hinders the diffusion of chemotherapeutic drugs or nanomedicines. METHODS: We designed a collagenase-functionalized biomimetic drug-loaded Au nanoplatform that combined ECM degradation, active targeting, immune evasion, near-infrared (NIR) light-triggered drug release, and synergistic antitumor therapy and diagnosis into one nanoplatform. PDAC tumor cell membranes were extracted and coated onto doxorubicin (Dox)-loaded Au nanocages, and then collagenase was added to functionalize the cell membrane through lipid insertion. We evaluated the physicochemical properties, in vitro and in vivo targeting, penetration and therapeutic efficacy of the nanoplatform. RESULTS: Upon intravenous injection, this nanoplatform efficiently targeted the tumor through the homologous targeting properties of the coated cell membrane. During penetration into the tumor tissue, the dense ECM in the PDAC tissues was gradually degraded by collagenase, leading to a looser ECM structure and deep penetration within the tumor parenchyma. Under NIR irradiation, both photothermal and photodynamic effects were produced and the encapsulated chemotherapeutic drugs were released effectively, exerting a strong synergistic antitumor effect. Moreover, this nanoplatform has X-ray attenuation properties that could serve to guide and monitor treatment by CT imaging. CONCLUSION: This work presented a unique and facile yet effective strategy to modulate ECM components in PDAC, enhance tumor penetration and tumor-killing effects and provide therapeutic guidance and monitoring.

Ultra-thin layered double hydroxide-mediated photothermal therapy combine with asynchronous blockade of PD-L1 and NR2F6 inhibit hepatocellular carcinoma.

BACKGROUND: The efficacy of immune checkpoint blockade (ICB), in the treatment of hepatocellular carcinoma (HCC), is limited due to low levels of tumor-infiltrating T lymphocytes and deficient checkpoint blockade in this immunologically "cool" tumor. Thus, combination approaches are needed to increase the response rates of ICB and induce synergistic antitumor immunity. METHODS: Herein, we designed a pH-sensitive multifunctional nanoplatform based on layered double hydroxides (LDHs) loaded with siRNA to block the intracellular immune checkpoint NR2F6, together with the asynchronous blockade surface receptor PD-L1 to induce strong synergistic antitumor immunity. Moreover, photothermal therapy (PTT) generated by LDHs after laser irradiation modified an immunologically "cold" microenvironment to potentiate Nr2f6-siRNA and anti-PD-L1 immunotherapy. Flow cytometry was performed to assess the immune responses initiated by the multifunctional nanoplatform. RESULTS: Under the slightly acidic tumor extracellular environment, PEG detached and the re-exposed positively charged LDHs enhanced tumor accumulation and cell uptake. The accumulated siRNA suppressed the signal of dual protumor activity in both immune and H22 tumor cells by silencing the NR2F6 gene, which further reduced the tumor burden and enhanced systemic antitumor immunity. The responses include enhanced tumor infiltration by CD4+ helper T cells, CD8+ cytotoxic T cells, and mature dendritic cells; the significantly decreased level of immune suppressed regulator T cells. The therapeutic responses were also attributed to the production of IL-2, IFN-γ, and TNF-α. The prepared nanoparticles also exhibited potential magnetic resonance imaging (MRI) ability, which could serve to guide synergistic immunotherapy treatment. CONCLUSIONS: In summary, the three combinations of PTT, NR2F6 gene ablation and anti-PD-L1 can promote a synergistic immune response to inhibit the progression of primary HCC tumors and prevent metastasis. This study can be considered a proof-of-concept for the targeting of surface and intracellular immune checkpoints to supplement the existing HCC immunotherapy treatments.

Establishment of a prediction model for disease progression within one year in newly diagnosed multiple myeloma patients.

Multiple myeloma is still an incurable disease In the past decade, with the continuous progress of treatment methods, the progression-free survival of patients has been prolonged, but some patients still progress in the early stage of the disease. Our research analyses the clinical laboratory indicators of newly diagnosed multiple myeloma (NDMM) patients, to obtain the relevant factors of disease progression within one year in MM patients and to establish a prediction model.108 MM patients treated in our hospital from January 2015 to January 2020 were retrospectively analyzed. After univariate and multivariate logistic regression analyses, the related factors of disease progression within one year in NDMM patients were obtained, and a prediction model was established.Treatment regimen containing at least two targeted drugs (OR = 0.226, 95% CI 0.068-0.753), increased lactate dehydrogenase(LDH, OR = 3.452, 95% CI 1.101-10.826) and increased serum corrected calcium(OR = 4.466, 95% CI 1.346-14.811) were identified as potential predictors by statistical analysis. The prediction model was obtained: x = -2.042-1.489 × treatment regimen (including at least two targeted drug assignment as 1, otherwise 0) + 1.239 ×LDH (U/L, lactate dehydrogenase elevation assignment as 1, normal as 0) +1.496 × serum corrected calcium (mmol/L, serum corrected calcium elevation assignment as 1, normal as 0). Receiver operating characteristic curve analysis showed that the model has good predictive performance.The possibility of disease progression within one year can be predicted by the prediction model. The model can be used as a reference for clinicians to make individualized treatment plans for patients so that patients can obtain better treatment effects.

Machine Learning-Based Radiological Features and Diagnostic Predictive Model of Xanthogranulomatous Cholecystitis.

Background: Xanthogranulomatous cholecystitis (XGC) is a rare benign chronic inflammatory disease of the gallbladder that is sometimes indistinguishable from gallbladder cancer (GBC), thereby affecting the decision of the choice of treatment. Thus, this study aimed to analyse the radiological characteristics of XGC and GBC to establish a diagnostic prediction model for differential diagnosis and clinical decision-making. Methods: We investigated radiological characteristics confirmed by the RandomForest and Logistic regression to establish computed tomography (CT), magnetic resonance imaging (MRI), CT/MRI models and diagnostic prediction model, and performed receiver operating characteristic curve (ROC) analysis to prove the effectiveness of the diagnostic prediction model. Results: Based on the optimal features confirmed by the RandomForest method, the mean area under the curve (AUC) of the ROC of the CT and MRI models was 0.817 (mean accuracy = 0.837) and 0.839 (mean accuracy = 0.842), respectively, whereas the CT/MRI model had a considerable predictive performance with the mean AUC of 0.897 (mean accuracy = 0.906). The diagnostic prediction model established for the convenience of clinical application was similar to the CT/MRI model with the mean AUC and accuracy of 0.888 and 0.898, respectively, indicating a preferable diagnostic efficiency in distinguishing XGC from GBC. Conclusions: The diagnostic prediction model showed good diagnostic accuracy for the preoperative discrimination of XGC and GBC, which might aid in clinical decision-making.

A radiologic diagnostic scoring model based on CT features for differentiating gastric schwannoma from gastric gastrointestinal stromal tumors.

We aimed to further explore the CT features of gastric schwannoma (GS), propose and validate a convenient diagnostic scoring system to distinguish GS from gastric gastrointestinal stromal tumors (GISTs) preoperatively. 170 patients with submucosal tumors pathologically confirmed (GS n=35; gastric GISTs n=135) from Hospital 1 were analyzed retrospectively as the training cohort, and 72 patients (GS=11; gastric GISTs=61) from Hospital 2 were enrolled as the validation cohort. We searched for significant CT imaging characteristics and constructed the scoring system via binary logistic regression and converted regression coefficients to weighted scores. The ROC curves, AUCs and calibration tests were carried out to evaluate the scoring models in both the training cohort and the validation cohort. For convenient assessment, the system was further divided into four score ranges and their diagnostic probability of GS was calculated respectively. Four CT imaging characteristics were ultimately enrolled in this scoring system, including transverse position (2 points), location (5 points), perilesional lymph nodes (6 points) and pattern of enhancement (2 points). The AUC of the scoring model in the training cohort were 0.873 (95% CI, 0.816-0.929) and the cutoff point was 6 points. In the validation cohort, the AUC was 0.898 (95% CI, 0.804-0.957) and the cutoff value was 5 points. Four score ranges were as follows: 0-3 points for very low probability of GS, 4-7 points for low probability; 8-9 points for middle probability; 10-15 points for very high probability. A convenient scoring model to preoperatively discriminate GS from gastric GISTs was finally proposed.

CB-LPD, MGUS, T-LGLL, and PRCA: A rare case report of 4 concomitant hematological disorders.

RATIONALE: Monoclonal gammopathy of undetermined significance (MGUS) is a clinically asymptomatic clonal plasma cell or lymphoplasmacytic proliferative disorder. Recently, some case reports have described the association of pure red cell aplasia (PRCA) with MGUS, even with a relatively low monoclonal immunoglobulin burden. T large granular lymphocyte leukemia (T-LGLL) is a chronic lymphoproliferative disorder characterized by clonal expansion of T large granular lymphocytes, which is rare in China. There are some reports about T-LGL leukemia in patients with B-cell lymphoma; however, it is very rare that T-LGLL coexists with MGUS and clonal B-cell lymphoproliferative disorders (CB-LPD). PATIENT CONCERNS: A 77-year-old man was hospitalized because of anemia. He was diagnosed with MGUS, CB-LPD, and PRCA. During the development of the disease, a group of abnormal T lymphocytes was detected by flow cytometry of peripheral blood. DIAGNOSIS: Combining clinical manifestations with the result of T cell receptor gene rearrangement and immunophenotype, it was consistent with the diagnosis of T large granular lymphocyte leukemia. INTERVENTIONS: The patient was treat with bortezomib and dexamethasone regimen, Rituximab and sirolimus. OUTCOMES: The patient was transfusion independent after therapies. LESSONS: We report a patient with 4 concomitant hematological disorders: T-LGLL, MGUS, CB-LPD, and PRCA, aiming to represent the clinical and flow cytometry characteristics of these concomitant diseases, analyze the mechanism between diseases, and provide a clinical reference.

Development and validation of a nomogram for steroid-resistance prediction in immune thrombocytopenia patients.

OBJECTIVES: Corticosteroid is first-line therapy in immune thrombocytopenia. However, nearly 30% of patients appear in steroid-resistance. Our research analyses the relevant indicators of patients and develops a risk prediction model to predict the poor response to steroid-therapy in ITP patients. METHODS: We collected data from 111 ITP patients admitted to Xiamen University Zhongshan Hospital from 2013 to 2019 as the training cohort and 65 ITP patients during 2019-2020 as the external validation cohort. Screening significant factors(P < 0.05) in univariate analysis, and further identified to be independent variables in multivariable logistic regression analysis. Incorporated the significant risk factors in and presented them with a nomogram based on independent risk predictors. The nomogram was assessed by receiver operating characteristics curves and decision curve analysis. RESULTS: We constructed a steroid-resistance prediction model based on the potential predictors including age, serum ferritin and expression of HBsAg. As a result, based on the area under the ROC curves, the training cohort (AUC: 0.718, 95% CI: 0.615-0.821) and the external validation cohort (AUC:0.799,95%CI:0.692-0.905), which displayed good discrimination. The decision curve showed that predicting the steroid-refractory risk in ITP patients using this nomogram with a range of the threshold probability between >16% and <70%. The nomogram appears good performance in predicting steroid-refractory ITP patients. CONCLUSION: Prediction model shows that elder patients with a high level of ferritin and positive expression of HBsAg may appear a high possibility of steroid-resistance. For these patients, TPO-RAs can be considered to help patients to get better treatment effects and develop a better health-related quality of life.

Diagnostic Value of CT- and MRI-Based Texture Analysis and Imaging Findings for Grading Cartilaginous Tumors in Long Bones.

OBJECTIVE: To confirm the diagnostic performance of computed tomography (CT)-based texture analysis (CTTA) and magnetic resonance imaging (MRI)-based texture analysis for grading cartilaginous tumors in long bones and to compare these findings to radiological features. MATERIALS AND METHODS: Twenty-nine patients with enchondromas, 20 with low-grade chondrosarcomas and 16 with high-grade chondrosarcomas were included retrospectively. Clinical and radiological information and 9 histogram features extracted from CT, T1WI, and T2WI were evaluated. Binary logistic regression analysis was performed to determine predictive factors for grading cartilaginous tumors and to establish diagnostic models. Another 26 patients were included to validate each model. Receiver operating characteristic (ROC) curves were generated, and accuracy rate, sensitivity, specificity and positive/negative predictive values (PPV/NPV) were calculated. RESULTS: On imaging, endosteal scalloping, cortical destruction and calcification shape were predictive for grading cartilaginous tumors. For texture analysis, variance, mean, perc.01%, perc.10%, perc.99% and kurtosis were extracted after multivariate analysis. To differentiate benign cartilaginous tumors from low-grade chondrosarcomas, the imaging features model reached the highest accuracy rate (83.7%) and AUC (0.841), with a sensitivity of 75% and specificity of 93.1%. The CTTA feature model best distinguished low-grade and high-grade chondrosarcomas, with accuracies of 71.9%, and 80% in the training and validation groups, respectively; T1-TA and T2-TA could not distinguish them well. We found that the imaging feature model best differentiated benign and malignant cartilaginous tumors, with an accuracy rate of 89.2%, followed by the T1-TA feature model (80.4%). CONCLUSIONS: The imaging feature model and CTTA- or MRI-based texture analysis have the potential to differentiate cartilaginous tumors in long bones by grade. MRI-based texture analysis failed to grade chondrosarcomas.

Self-amplification of oxidative stress with tumour microenvironment-activatable iron-doped nanoplatform for targeting hepatocellular carcinoma synergistic cascade therapy and diagnosis.

BACKGROUND: Hepatocellular carcinoma is insensitive to many chemotherapeutic agents. Ferroptosis is a form of programmed cell death with a Fenton reaction mechanism. It converts endogenous hydrogen peroxide into highly toxic hydroxyl radicals, which inhibit hepatocellular carcinoma progression. METHODS: The morphology, elemental composition, and tumour microenvironment responses of various organic/inorganic nanoplatforms were characterised by different analytical methods. Their in vivo and in vitro tumour-targeting efficacy and imaging capability were analysed by magnetic resonance imaging. Confocal microscopy, flow cytometry, and western blotting were used to investigate the therapeutic efficacy and mechanisms of complementary ferroptosis/apoptosis mediated by the nanoplatforms. RESULTS: The nanoplatform consisted of a silica shell doped with iron and disulphide bonds and an etched core loaded with doxorubicin that generates hydrogen peroxide in situ and enhances ferroptosis. It relied upon transferrin for targeted drug delivery and could be activated by the tumour microenvironment. Glutathione-responsive biodegradability could operate synergistically with the therapeutic interaction between doxorubicin and iron and induce tumour cell death through complementary ferroptosis and apoptosis. The nanoplatform also has a superparamagnetic framework that could serve to guide and monitor treatment under T2-weighted magnetic resonance imaging. CONCLUSION: This rationally designed nanoplatform is expected to integrate cancer diagnosis, treatment, and monitoring and provide a novel clinical antitumour therapeutic strategy.

Relapsed/refractory acute promyelocytic leukemia with RARA-LBD region mutation was salvaged by venetoclax: A case report.

RATIONALE: Acute promyelocytic leukemia (APL) is one of the most curable cancers. However, relapse of the disease is a difficult issue in clinical practice and it remains a great challenge that patients have a poor effect of conventional treatment in the clinic. Therefore, new and more effective therapeutic measures are urgently needed. Herein, we report a case of relapsed and refractory APL harboring a RARA-LBD region mutation successfully treated with venetoclax (VEN). PATIENT CONCERNS: A 37-years-old woman was admitted to our hospital with worsening spontaneous gingival bleeding and skin ecchymosis. Physical examination revealed multiple petechiae and ecchymosis in the extremities. DIAGNOSES: The patient was diagnosed with L-type PML-RARα-positive APL, harboring a RARA-LBD region mutation, low-risk, based on bone marrow cytology, immunophenotypic analysis by flow cytometry, karyotype analysis, and molecular analysis. INTERVENTIONS: Complete remission was achieved after the first induction therapy of all-trans retinoic acid (ATRA) combined with arsenic trioxide, but relapse was observed only after 11 months. Reinduction with ATRA and arsenic trioxide combined with anthracycline failed. Therefore, we tried to provide a new treatment with the Bcl-2 inhibitor VEN orally (100 mg d1, 200 mg d2 to d18, followed by 300 mg daily continuously). OUTCOMES: Clinical symptoms and laboratory indicators improved rapidly with VEN treatment. A complete hematologic response was achieved with VEN-based therapy. LESSONS: Related drug resistance gene monitoring should be performed canonically in relapsed and refractory APL. Some relapsed and refractory APL that failed to respond to conventional treatment were at risk of death. Bcl-2 inhibitors are expected to be an effective salvage therapy for patients with resistance to ATRA, which is worthy of further discussion.

Successful treatment of disseminated fusariosis in a patient with acute lymphoblastic leukemia: A case report and literature review.

RATIONALE: Fusarium is the second most common cause of fungi infections in the immunocompromised patients with the mortality rate over 80%. Early identification and appropriate selection of antifungal drugs is the key to successful treatment. PATIENT CONCERNS: A 31-year-old female was diagnosed with acute lymphocytic leukemia (pro-B ALL). She developed a high fever and presented with typical painful purple nodules with central necrosis formed on the upper and lower limbs during the induction chemotherapy. DIAGNOSIS: Combining clinical manifestations with results of blood culture testing and sequencing methods, it was consistent with the diagnosis of disseminated fusariosis. INTERVENTIONS: The patient was treated with the combination of tigecycline and antifungal agents (Liposomal Amphotericin B and Voriconazole), OUTCOMES:: The skin lesions generally healed with some scar left after treating with antifungal agents for 6 weeks. The final date of follow-up was 1.5 years later, and the patient was alive with no diseases. LESSONS: This case highlights the importance of the typical cutaneous lesions for early diagnosis and proper treatment to decrease the mortality rate of this severe infection. This patient was successfully treated with the combination of tigecycline and antifungal agents, which may be the first clinical confirmation of tigecycline that improved the effectiveness of antifungal agents against fusariosis, but it requires more studies to verify. We reviewed 62 cases from literature and analyzed using logistic regression and recognized the high-risk factor for fusariosis mortality in patients with acute leukemia was non-remission of underlying disease.