Randomized clinical trial on D2 lymphadenectomy versus D2 lymphadenectomy plus complete mesogastric excision in patients undergoing gastrectomy for cancer (DCGC01 study).

BACKGROUND: It is unknown whether D2 lymphadenectomy + complete mesogastric excision for gastric cancer improves survival compared with just D2 lymphadenectomy. METHODS: Between September 2014 and June 2018, patients with advanced gastric cancer were randomly assigned (1 : 1) to laparoscopic D2 lymphadenectomy or D2 lymphadenectomy + complete mesogastric excision gastrectomy. The modified intention-to-treat population was defined as patients who had pathologically confirmed gastric adenocarcinoma (pT1 N1-3 M0 and pT2-4 N0-3 M0). The primary endpoint was 3-year disease-free survival. Secondary endpoints were the recurrence pattern and overall survival. RESULTS: The median follow-up of patients in the D2 lymphadenectomy group (169 patients) and patients in the D2 lymphadenectomy +complete mesogastric excision group (169 patients) was 55 (interquartile range 37-60) months and 51 (interquartile range 40-60) months respectively. Recurrence occurred in 50 patients in the D2 lymphadenectomy group (29.6%) versus 33 patients in the D2 lymphadenectomy + complete mesogastric excision group (19.5%) (P = 0.032). The 3-year disease-free survival was 75.5% (95% c.i. 68.3% to 81.3%) in the D2 lymphadenectomy group versus 85.0% (95% c.i. 78.7% to 89.6%) in the D2 lymphadenectomy + complete mesogastric excision group (log rank P = 0.042). The HR for recurrence in the D2 lymphadenectomy + complete mesogastric excision group versus the D2 lymphadenectomy group was 0.64 (95% c.i. 0.41 to 0.99) by Cox regression (P = 0.045). The 3-year overall survival rate was 77.5% (95% c.i. 70.4% to 83.1%) in the D2 lymphadenectomy group versus 85.8% (95% c.i. 79.6% to 90.2%) in the D2 lymphadenectomy + complete mesogastric excision group (log rank P = 0.058). The HR for death in the D2 lymphadenectomy + complete mesogastric excision group versus the D2 lymphadenectomy group was 0.64 (95% c.i. 0.41 to 1.02) (P = 0.058). CONCLUSION: Compared with conventional D2 dissection, D2 lymphadenectomy + complete mesogastric excision is associated with better disease-free survival, but there is no statistically significant difference in overall survival. REGISTRATION NUMBER: NCT01978444 (http://www.clinicaltrials.gov).

Comparison of Different Fusion Radiomics for Predicting Benign and Malignant Sacral Tumors: A Pilot Study.

Differentiating between benign and malignant sacral tumors is crucial for determining appropriate treatment options. This study aims to develop two benchmark fusion models and a deep learning radiomic nomogram (DLRN) capable of distinguishing between benign and malignant sacral tumors using multiple imaging modalities. We reviewed axial T2-weighted imaging (T2WI) and non-contrast computed tomography (NCCT) of 134 patients pathologically confirmed as sacral tumors. The two benchmark fusion models were developed using fusion deep learning (DL) features and fusion classical machine learning (CML) features from multiple imaging modalities, employing logistic regression, K-nearest neighbor classification, and extremely randomized trees. The two benchmark models exhibiting the most robust predictive performance were merged with clinical data to formulate the DLRN. Performance assessment involved computing the area under the receiver operating characteristic curve (AUC), sensitivity, specificity, accuracy, negative predictive value (NPV), and positive predictive value (PPV). The DL benchmark fusion model demonstrated superior performance compared to the CML fusion model. The DLRN, identified as the optimal model, exhibited the highest predictive performance, achieving an accuracy of 0.889 and an AUC of 0.961 in the test sets. Calibration curves were utilized to evaluate the predictive capability of the models, and decision curve analysis (DCA) was conducted to assess the clinical net benefit of the DLR model. The DLRN could serve as a practical predictive tool, capable of distinguishing between benign and malignant sacral tumors, offering valuable information for risk counseling, and aiding in clinical treatment decisions.

Molecular Modeling of Shockwave-Mediated Blood-Brain Barrier Opening for Targeted Drug Delivery.

Bubble-enhanced shock waves induce the transient opening of the blood-brain barrier (BBB) providing unique advantages for targeted drug delivery of brain tumor therapy, but little is known about the molecular details of this process. Based on our BBB model including 28 000 lipids and 280 tight junction proteins and coarse-grained dynamics simulations, we provided the molecular-level delivery mechanism of three typical drugs for the first time, including the lipophilic paclitaxel, hydrophilic gemcitabine, and siRNA encapsulated in liposome, across the BBB. The results show that the BBB is more difficult to be perforated by shock-induced jets than the human brain plasma membrane (PM), requiring higher shock wave speeds. For the pores formed, the BBB exhibits a greater ability to self-heal than PM. Hydrophobic paclitaxel can cross the BBB and be successfully absorbed, but the amount is only one-third of that of PM; however, the absorption of hydrophilic gemcitabine was almost negligible. Liposome-loaded siRNAs only stayed in the first layer of the BBB. The mechanism analysis shows that increasing the bubble size can promote drug absorption while reducing the risk of higher shock wave overpressure. An exponential function was proposed to describe the relation between bubble and overpressure, which can be extended to the experimental microbubble scale. The calculated overpressure is consistent with the experimental result. These molecular-scale details on shock-assisted BBB opening for targeted drug delivery would guide and assist experimental attempts to promote the application of this strategy in the clinical treatment of brain tumors.

Comparable prognosis of early gastric cancer between intestinal type and diffuse type in patients of age 75 and older: a SEER-based cohort study.

Background: The prognostic significance of Lauren's classification in elderly early gastric cancer (EGC) patients remains largely unknown. We aim to investigate the characteristics and clinical implications of Lauren's classification in elderly EGC patients. Methods: Patients were collected from the Surveillance, Epidemiology, and End Results (SEER) database based on the inclusion and exclusion criteria. Univariate and multivariate Cox regression, propensity score matching, inverse-probability-weighted analysis, and propensity-score adjustment were utilized to evaluate the association between Lauren's classification and cancer-specific survival (CSS) in elderly EGC patients. Stratification and interaction analyses were used to reveal the effects of confounding factors on the association between Lauren's classification and CSS. Results: The diffuse type (median, 41.0 months) showed a similar survival (37.0 months), and was mainly distributed in female group (62.5% vs. 42.2%) with poorly differentiated or undifferentiated components (89.1% vs. 27.0%) compared with intestinal type in elderly EGC patients. Analyses of univariate and multivariate Cox regression, propensity score matching, inverse-probability-weighted analysis, and propensity-score adjustment showed that Lauren's classification was not significantly CSS in elderly EGC patients (P>0.05). Subgroup and interaction analyses confirmed the stability of the results. Conclusions: Diffuse type was mainly distributed in female patients with more poorly differentiated/undifferentiated components and similar prognosis compared with intestinal type in age 75 and older EGC patients. No significant association was observed between diffuse type and CSS of the elderly EGC patients.

Hyperglycemia in pregnancy did not worsen the short-term outcomes of very preterm infants: a propensity score matching study.

Background: Hyperglycemia in pregnancy (HGP) has generally been considered a risk factor associated with adverse outcomes in offspring, but its impact on the short-term outcomes of very preterm infants remains unclear. Methods: A secondary analysis was performed based on clinical data collected prospectively from 28 hospitals in seven regions of China from September 2019 to December 2020. According to maternal HGP, all infants were divided into the HGP group or the non-HGP group. A propensity score matching analysis was used to adjust for confounding factors, including gestational age, twin or multiple births, sex, antenatal steroid administration, delivery mode and hypertensive disorders of pregnancy. The main complications and the short-term growth status during hospitalization were evaluated in the HGP and non-HGP groups. Results: A total of 2,514 infants were eligible for analysis. After matching, there were 437 infants in the HGP group and 874 infants in the non-HGP group. There was no significant difference between the two groups in main complications including respiratory distress syndrome, bronchopulmonary dysplasia, necrotizing enterocolitis, retinopathy of prematurity, patent ductus arteriosus, culture positive sepsis, intraventricular hemorrhage, periventricular leukomalacia, anemia, feeding intolerance, metabolic bone disease of prematurity, or parenteral nutrition-associated cholestasis. The incidences of extrauterine growth retardation and increased growth retardation for weight and head circumference in the non-HGP group were all higher than those in the HGP group after matching (P < 0.05). Conclusions: HGP did not worsen the short-term outcomes of the surviving very preterm infants, as it did not lead to a higher risk of the main neonatal complications, and the infants' growth improved during hospitalization.

How Microbubble-Enhanced Shock Waves Promote the Delivery of Lipid-siRNA across Neuronal Plasma Membrane: A Computational Study.

In this computational study, we examine the potential of microbubble-enhanced shock waves to improve the delivery of lipid-siRNA nanoparticles across neuronal plasma membranes with the ultimate aim of enhancing brain tumor treatment. We critically evaluate several variables related to experiments, including the bubble size, the shock speed and action time, and the amount of siRNA encapsulated in the liposome. Our findings reveal that microbubble-enhanced shock waves are essential for the high delivery of small lipid vesicles (under 30 nm diameter); its corresponding variables significantly impact drug penetration and absorption rates and influence the overall efficacy of the drug delivery system. Long-time recovery simulations further provide valuable insights into the self-healing ability of the plasma membrane following shock wave exposure and the subsequent absorption dynamics of siRNA. This work provides the dynamic process of siRNA released from lipid vesicles with shock wave and nanobubbles, thereby serving as a molecular mechanism support for developing tunable delivery systems for RNA-based therapy in brain tumors.

MRI-Based Machine Learning Fusion Models to Distinguish Encephalitis and Gliomas.

This paper aims to compare the performance of the classical machine learning (CML) model and the deep learning (DL) model, and to assess the effectiveness of utilizing fusion radiomics from both CML and DL in distinguishing encephalitis from glioma in atypical cases. We analysed the axial FLAIR images of preoperative MRI in 116 patients pathologically confirmed as gliomas and clinically diagnosed with encephalitis. The 3 CML models (logistic regression (LR), support vector machine (SVM) and multi-layer perceptron (MLP)), 3 DL models (DenseNet 121, ResNet 50 and ResNet 18) and a deep learning radiomic (DLR) model were established, respectively. The area under the receiver operating curve (AUC) and sensitivity, specificity, accuracy, negative predictive value (NPV) and positive predictive value (PPV) were calculated for the training and validation sets. In addition, a deep learning radiomic nomogram (DLRN) and a web calculator were designed as a tool to aid clinical decision-making. The best DL model (ResNet50) consistently outperformed the best CML model (LR). The DLR model had the best predictive performance, with AUC, sensitivity, specificity, accuracy, NPV and PPV of 0.879, 0.929, 0.800, 0.875, 0.867 and 0.889 in the validation sets, respectively. Calibration curve of DLR model shows good agreement between prediction and observation, and the decision curve analysis (DCA) indicated that the DLR model had higher overall net benefit than the other two models (ResNet50 and LR). Meanwhile, the DLRN and web calculator can provide dynamic assessments. Machine learning (ML) models have the potential to non-invasively differentiate between encephalitis and glioma in atypical cases. Furthermore, combining DL and CML techniques could enhance the performance of the ML models.

Fusion Radiomics-Based Prediction of Response to Neoadjuvant Chemotherapy for Osteosarcoma.

RATIONALE AND OBJECTIVES: Neoadjuvant chemotherapy (NAC) is the most crucial prognostic factor for osteosarcoma (OS), it significantly prolongs progression-free survival and improves the quality of life. This study aims to develop a deep learning radiomics (DLR) model to accurately predict the response to NAC in patients diagnosed with OS using preoperative MR images. METHODS: We reviewed axial T2-weighted imaging (T2WI) and contrast-enhanced T1-weighted (T1CE) of 106 patients pathologically confirmed as OS. First, the Auto3DSeg framework was utilized for automated OS segmentation. Second, using three feature extraction methods, nine risk classification models were constructed based on three classifiers. The area under the receiver operating curve (AUC), sensitivity, specificity, accuracy, negative predictive value and positive predictive value were calculated for performance evaluation. Additionally, we developed a deep learning radiomics nomogram with clinical indicators. RESULTS: The model for OS automatic segmentation achieved a Dice coefficient of 0.868 across datasets. To predict the response to NAC, the DLR model achieved the highest prediction performance with an accuracy of 93.8% and an AUC of 0.961 in the test sets. We used calibration curves to assess the predictive ability of the models and performed decision curve analysis to evaluate the clinical net benefit of the DLR model. CONCLUSION: The DLR model can serve as a pragmatic prediction tool, capable of identifying patients with poor response to NAC, providing information for risk counseling, and assisting in making clinical treatment decisions. Poor responders are better advised to undergo immunotherapy and receive the best supportive care.

High-throughput sequencing reveals hub genes for human early embryonic development arrest in vitro fertilization: a pilot study.

Many clinical studies have shown that embryos of in vitro fertilization (IVF) are often prone to developmental arrest, which leads to recurrent failure of IVF treatment. Early embryonic arrest has always been an urgent clinical problem in assisted reproduction centers. However, the molecular mechanisms underlying early embryonic development arrest remain largely unknown. The objective of this study is to investigate potential candidate hub genes and key signaling pathways involved in early stages of embryonic development. RNA-seq analysis was performed on normal and arrest embryos to study the changes of gene expression during early embryonic development. A total of 520 genes exhibiting differential expression were identified, with 174 genes being upregulated and 346 genes being downregulated. Upregulated genes show enrichment in biosynthesis, cellular proliferation and differentiation, and epigenetic regulation. While downregulated genes exhibit enrichment in transcriptional activity, epigenetic regulation, cell cycle progression, cellular proliferation and ubiquitination. The STRING (search tool for the retravel of interacting genes/proteins) database was utilized to analyze protein-protein interactions among these genes, aiming to enhance comprehension of the potential role of these differentially expressed genes (DEGs). A total of 22 hub genes (highly connected genes) were identified among the DEGs using Cytoscape software. Of these, ERBB2 and VEGFA were upregulated, while the remaining 20 genes (CCNB1, CCNA2, DICER1, NOTCH1, UBE2B, UBE2N, PRMT5, UBE2D1, MAPK3, SOX9, UBE2C, UB2D2, EGF, ACTB, UBA52, SHH, KRAS, UBE2E1, ADAM17 and BRCA2) were downregulated. These hub genes are associated with crucial biological processes such as ubiquitination, cellular senescence, cell proliferation and differentiation, and cell cycle. Among these hub genes, CCNA2 and CCNB1 may be involved in controlling cell cycle, which are critical process in early embryonic development.

Peptidomic analysis of follicular fluid in patients with polycystic ovarian syndrome.

Objective: The aim of this study was to analyze and compare the differential expression of peptides within the follicular fluid of polycystic ovary syndrome (PCOS) patients versus normal women by using peptidomics techniques. The underlying mechanisms involved in PCOS pathogenesis will be explored, together with screening and identification of potential functional peptides via bioinformatics analysis. Materials and methods: A total of 12 patients who underwent in vitro fertilization and embryo transfer (IVF-ET) at the Reproductive Medicine Center of Shuguang Hospital Affiliated to Shanghai University of Traditional Chinese Medicine from 1 September 2022 to 1 November 2022 were included in this study. The follicular fluid of PCOS patients (n = 6) and normal women (n = 6) were collected. The presence and concentration differences of various peptides were detected by the LC-MS/MS method. GO and KEGG analysis were performed on the precursor proteins of the differentially-expressed peptides, and protein network interaction analysis was carried out to identify functionally-relevant peptides among the various peptides. Results: A variety of peptides within the follicular fluid of PCOS versus normal patients were detected by peptidomics techniques. Altogether, 843 upregulated peptides and 236 downregulated peptides were detected (absolute fold change ≥2 and p < 0.05). Of these, 718 (718 = 488 + 230) peptides were only detected in the PCOS group, while 205 (205 = 174 + 31) were only detected in the control group. Gene Ontology enrichment and pathway analysis were performed to characterize peptides through their precursor proteins. We identified 18 peptides from 7 precursor proteins associated with PCOS, and 4 peptide sequences were located in the functional domains of their corresponding precursor proteins. Conclusion: In this study, differences in the follicular development of PCOS versus normal patients were revealed from the polypeptidomics of follicular development, which thus provided new insights for future studies on the pathological mechanisms of PCOS development.

Radiomics analysis based on CT for the prediction of pulmonary metastases in ewing sarcoma.

OBJECTIVES: This study aimed to develop and validate radiomics models on the basis of computed tomography (CT) and clinical features for the prediction of pulmonary metastases (MT) in patients with Ewing sarcoma (ES) within 2 years after diagnosis. MATERIALS AND METHODS: A total of 143 patients with a histopathological diagnosis of ES were enrolled in this study (114 in the training cohort and 29 in the validation cohort). The regions of interest (ROIs) were handcrafted along the boundary of each tumor on the CT and CT-enhanced (CTE) images, and radiomic features were extracted. Six different models were built, including three radiomics models (CT, CTE and ComB models) and three clinical-radiomics models (CT_clinical, CTE_clinical and ComB_clinical models). The area under the receiver operating characteristic curve (AUC), and accuracy were calculated to evaluate the different models, and DeLong test was used to compare the AUCs of the models. RESULTS: Among the clinical risk factors, the therapeutic method had significant differences between the MT and non-MT groups (P＜0.01). The six models performed well in predicting pulmonary metastases in patients with ES, and the ComB model (AUC: 0.866/0.852 in training/validation cohort) achieved the highest AUC among the six models. However, no statistically significant difference was observed between the AUC of the models. CONCLUSIONS: In patients with ES, clinical-radiomics model created using radiomics signature and clinical features provided favorable ability and accuracy for pulmonary metastases prediction.

Postoperative Relapse Prediction in Patients With Ewing Sarcoma Using Computed Tomography-Based Radiomics Models Covering Tumor Per Se and Peritumoral Signatures.

OBJECTIVE: We aimed to develop and validate a computed tomography (CT)-based radiomics model for early relapse prediction in patients with Ewing sarcoma (ES). METHODS: We recruited 104 patients in this study. Tumor areas and areas with a tumor expansion of 3 mm were used as regions of interest for radiomics analysis. Six different models were constructed: Pre-CT, CT enhancement (CTE), Pre-CT +3 mm , CTE +3 mm , Pre-CT and CTE combined (ComB), and Pre-CT +3 mm and CTE +3 mm combined (ComB +3 mm ). All 3 classifiers used a grid search with 5-fold cross-validation to identify their optimal parameters, followed by repeat 5-fold cross-validation to evaluate the model performance based on these parameters. The average performance of the 5-fold cross-validation and the best one-fold performance of each model were evaluated. The AUC (area under the receiver operating characteristic curve) and accuracy were calculated to evaluate the models. RESULTS: The 6 radiomics models performed well in predicting relapse in patients with ES using the 3 classifiers; the ComB and ComB +3 mm models performed better than the other models (AUC -best : 0.820-0.922/0.823-0.833 and 0.799-0.873/0.759-0.880 in the training and validation cohorts, respectively). Although the Pre-CT +3 mm , CTE +3 mm, and ComB +3 mm models covering tumor per se and peritumoral CT features preoperatively forecasted ES relapse, the model was not significantly improved. CONCLUSIONS: The radiomics model performed well for early recurrence prediction in patients with ES, and the ComB and ComB +3 mm models may be superior to the other models.

CD7 targeted "off-the-shelf" CAR-T demonstrates robust in vivo expansion and high efficacy in the treatment of patients with relapsed and refractory T cell malignancies.

T-cell acute lymphoblastic leukemia (T-ALL) represents an area of highly unmet medical needs. Once relapsed, patients have limited treatment options and poor prognosis. T-ALL antigens such as CD7 is extensively expressed in normal T cells and natural killer (NK) cells, and extending the success of CAR-T therapy to T cell malignancies was challenged by CAR-T cell fratricide, high production cost, and potential product contaminations. GC027 is an "off-the-shelf" allogeneic CD7 targeted CAR-T therapeutic product for T cell malignancies. It demonstrated superior cell expansion and antileukemia efficacy in mouse xenograft model. In our previous study, we observed promising efficacy results in the first two relapsed and refractory(R/R) T-ALL patients treated with GC027. In the expanded study, 11 out of 12 patients had rapid eradication of T-lymphoblasts and reached complete response within 1-month after GC027 infusion. GC027 cells expanded quickly beginning at infusion and reached to peak around 5-10 days after infusion. For most patients with a response(9/11), GC027 could not be detected via flow cytometry or qPCR 4 weeks after infusion. One patient had progression free survival of >3 years. With manageable toxicity profile, GC027 demonstrated superior clinical efficacy to standard chemotherapy regimens in (R/R) T cell malignancies.

Discovery of neddylation E2s inhibitors with therapeutic activity.

Neddylation is the writing of monomers or polymers of neural precursor cells expressed developmentally down-regulated 8 (NEDD8) to substrate. For neddylation to occur, three enzymes are required: activators (E1), conjugators (E2), and ligators (E3). However, the central role is played by the ubiquitin-conjugating enzymes E2M (UBE2M) and E2F (UBE2F), which are part of the E2 enzyme family. Recent understanding of the structure and mechanism of these two proteins provides insight into their physiological effects on apoptosis, cell cycle arrest and genome stability. To treat cancer, it is therefore appealing to develop novel inhibitors against UBE2M or UBE2F interactions with either E1 or E3. In this evaluation, we summarized the existing understanding of E2 interaction with E1 and E3 and reviewed the prospective of using neddylation E2 as a pharmacological target for evolving new anti-cancer remedies.

Giant paratesticular dedifferentiated liposarcoma with intraabdominal extension: a case report.

Giant paratesticular liposarcoma is a rare presentation of paratesticular tumor. We present a case of the largest paratesticular liposarcoma described to date with a weight of 4,100 g and measuring 460 × 210 × 130 mm. It was initially mistaken as an inguinoscrotal hernia until a contrast-enhanced computed tomography (CECT) scan of the abdomen and pelvis revealed a huge left paratesticular tumor extending from the scrotum to the mid-abdomen. The challenge was to achieve a tumor-free margin orchidectomy due to the poor fat plane of the tumor to the external iliac artery, psoas muscle, descending colon, and anterior abdominal wall. The surgery was started with laparoscopic dissection for the intraabdominal part of tumor from the vital structure, then followed by inguinal radical orchidectomy and inguinal mesh repair. Postoperative histopathological report revealed a paratesticular dedifferentiated liposarcoma with rhabdomyosarcomatous differentiation with clear margin. The patient had good recovery post operation.

Abnormal Pap smear among pregnant women - Feasibility of opportunistic cervical screening.

Objective: The uptake of cervical cancer screening is poor, especially in developing countries. Thus, pregnancy represents a good opportunity to have the test done. The aim of this study is to determine the prevalence of abnormal Pap smear among pregnant women during their antenatal check-ups. Study design: A prospective study involving five hundred and ninety-six women was recruited over a 1-year duration from 15th January 2018 until 14th January 2019 in a tertiary referral center, in Malaysia. Pap smears were performed on all consented pregnant women using liquid-based cytology and the results were obtained to evaluate the prevalence of abnormal Pap smear during pregnancy. Maternal risk factors associated with abnormal Pap smear were identified and the outcomes of abnormal Pap smear were followed up. Results: A total of 670 participants were approached and 596 participants agreed to participate, giving a response rate of 89.0 %. Therefore, 587 participants were available for analysis. There were nine unsatisfactory smears (1.5 %). The prevalence of premalignant lesions reported on p % ap smear was 0.8 %. Three respondents had atypical squamous cells of undetermined significance (ASCUS) (0.5 %) and two respondents had low-grade squamous intraepithelial lesions (LSIL) (0.3 %). Almost one-third (30.3 %) of respondents had an infection and 24 (4.1 %) smears were reported as reactive changes associated with inflammation. Respondents between the age of 20-30 years old had a significant association with an abnormal pre-cancerous smear (p = 0.000) as well as nulliparity (p = 0.0.40). There was no significant association between height, weight, BMI, sexual partner, age of first intercourse, smoking habit, history of sexually transmitted disease and history of abnormal Pap smear. Conclusion: The prevalence of abnormal pre-cancerous smears during pregnancy is low. However, it is desirable to perform cervical screening as it provides an opportunity to no screening at all.

A multi-modal intervention for managing the fatigue-sleep disturbance-depressed mood symptom cluster in breast cancer patients undergoing chemotherapy: A pilot study.

Objective: To examine the feasibility and acceptability of a multi-modal intervention for managing the cancer-related fatigue-sleep disturbance-depressed mood (F-S-D) symptom cluster in patients with breast cancer (BC) and receiving chemotherapy in Hong Kong, and the preliminary effects of such intervention on the occurrence of the F-S-D symptom cluster in these patients. Methods: This study was a single-blind randomized controlled trial. Patients with BC scheduled for chemotherapy were recruited. Intervention participants received a weekly nurse-led multi-modal intervention lasting 7 weeks. The feasibility parameters and adverse events were assessed using logbook records. Acceptability was evaluated using a program evaluation questionnaire. F-S-D symptoms and quality of life (QOL) were measured at baseline (T0), upon intervention completion (T1), and 3 months after intervention completion (T2). Generalized estimating equation analyses were used. Results: Fifty participants were enrolled. The eligibility and enrollment rates were 11% and 87.7%, respectively. The rate of adherence to the intervention was 96%. No adverse events were reported. All participants were satisfied with the intervention, which had significant effects in terms of reducing the occurrence of the F-S-D symptom cluster at T2 (P â€‹= â€‹0.035) and improving QOL at T1 and T2 (T1: P â€‹= â€‹0.035; T2: P â€‹= â€‹0.012). Conclusions: The multi-modal intervention is a feasible, acceptable, and safe intervention that demonstrated preliminary positive effects in managing the F-S-D symptom cluster and improving QOL in patients with BC and receiving chemotherapy in Hong Kong. This study provides key insights into F-S-D symptom cluster management in patients with BC. Trial registration: ChiCTR2100047819 (Chinese Clinical Trial Register).

A marginal structural model analysis for the effect modification by education on the association between cancer diagnosis history and major depressive symptoms: Findings from Midlife Development in the U.S. (MIDUS).

BACKGROUND: Limited research has employed a longitudinal approach to investigate the role of education level as an effect modifier on the relationship between cancer diagnosis history and the experience of major depressive disorder (MDD) with a nationally representative sample. METHODS: We harnessed data from three installments of the MIDUS Longitudinal study (n = 7108). A Marginal Structural Model facilitated the investigation of associations between a history of cancer diagnosis, MDD, and potential modifying effects of education level. Inverse probability weighting helped manage confounding factors. RESULTS: Findings indicated that a cancer diagnosis made one year prior was linked with 3.741 times greater odds of experiencing MDD (95 % CI: 1.411-9.918, p < 0.01). This connection was absent for diagnoses made two years earlier. Among individuals with education up to high school, a recent cancer diagnosis significantly increased the likelihood of MDD in the subsequent wave by 3.45 times (95 % CI: 1.31-9.08, p < 0.05). This pattern was not apparent among better-educated individuals. LIMITATIONS: As the exposure variable was dependent on self-reported questionnaires, recall bias could be a potential limitation. Moreover, unaccounted variables like genetic factors could introduce confounding. CONCLUSIONS: A recent cancer diagnosis, particularly among less educated individuals, correlated with an increased probability of MDD, while the impact was not observed for older diagnoses. These findings emphasize that the timing of a cancer diagnosis and education level need consideration in the mental health assessment of cancer survivors.