The Methodological Quality of Observational Studies Examining the Risk of Pregnancy Drug Use on Congenital Malformations Needs Substantial Improvement: A Cross-Sectional Survey.

BACKGROUND AND OBJECTIVE: An increasing number of observational studies have investigated the risk of using drugs during pregnancy on congenital malformations. However, the credibility of the causal relationships drawn from these studies remains uncertain. This study aims to evaluate the potential methodological issues in existing observational studies. METHODS: We used a stepwise approach to investigate this issue. First, we identified observational studies published in 2020 that examined the risk of congenital malformations associated with medication use during pregnancy. We assessed the methodological characteristics for establishing causality, including study design, confounding control, and sensitivity analysis, and compared them between "core clinical journals" and "general journals." For studies reporting an increased risk of congenital malformations in core clinical journals, we searched for subsequent studies addressing the same research question published between January 2021 and May 2023 to assess the consistency of the literature. RESULTS: A total of 40 eligible studies were published in 2020, primarily focused on the safety of vitamin B12 and folic acid (n = 4), antidepressants (n = 4), and others (n = 32). Our findings suggest that only two (5.00%) studies used causal models to guide the identification of confounding, and only eight (20.00%) studies assessed the potential dose-response relationship. In all, 15 (37.50%) studies used propensity score analysis strategy to achieve "mimic-randomization." In addition, 22 studies (55.00%) performed sensitivity analyses, while 10 (45.45%) showed inconsistency with the primary outcome. Furthermore, 5 studies reported positive outcomes, whereas only 1 out of 11 studies demonstrated a positive correlation between drug usage during pregnancy and major malformations in subsequent studies. CONCLUSION: A significant portion of the studies has failed to sufficiently consider the essential methodological characteristics required to improve the credibility of causal inferences. The increased risk of congenital malformations documented in core clinical journal was not adequately replicated in subsequent studies.

The value of multiparametric MRI-based habitat imaging for differentiating uterine sarcomas from atypical leiomyomas: a multicentre study.

PURPOSE: To explore the feasibility of multiparametric MRI-based habitat imaging for distinguishing uterine sarcoma (US) from atypical leiomyoma (ALM). METHODS: This retrospective study included the clinical and preoperative MRI data of 69 patients with US and 225 patients with ALM from three hospitals. At both the individual and cohort levels, the K-means and Gaussian mixture model (GMM) algorithms were utilized to perform habitat imaging on MR images, respectively. Specifically, T2-weighted images (T2WI) and contrast-enhanced T1-weighted images (CE-T1WI) were clustered to generate structural habitats, while apparent diffusion coefficient (ADC) maps and CE-T1WI were clustered to create functional habitats. Parameters of each habitat subregion were extracted to construct distinct habitat models. The integrated models were constructed by combining habitat and clinical independent predictors. Model performance was assessed using the area under the curve (AUC). RESULTS: Abnormal vaginal bleeding, lactate dehydrogenase (LDH), and white blood cell (WBC) counts can serve as clinical independent predictors of US. The GMM-based functional habitat model at the cohort level had the highest mean AUC (0.766) in both the training and validation cohorts, followed by the GMM-based structural habitat model at the cohort level (AUC = 0.760). Within the integrated models, the K-means functional habitat model based on the cohort level achieved the highest mean AUC (0.905) in both the training and validation cohorts. CONCLUSION: Habitat imaging based on multiparametric MRI has the potential to distinguish US from ALM. The combination of clinical independent predictors with the habitat models can effectively improve the performance.

Identifying risk and prognostic factors for synchronous liver metastasis in small bowel adenocarcinoma: a predictive analysis using the SEER database.

Background: Small bowel adenocarcinoma (SBA) is a rare gastrointestinal malignancy with an increasing incidence and a high propensity for liver metastasis (LM). This study aimed to investigate the risk factors for synchronous LM and prognostic factors in patients with LM. Methods: Utilizing the Surveillance, Epidemiology, and End Results (SEER) database, this study analyzed data from 2,064 patients diagnosed with SBA between 2010 and 2020. Logistic regression was used to determine risk factors for synchronous LM. A nomogram was developed to predict the risk of LM in SBA patients, and its predictive performance was assessed through receiver operating characteristic (ROC) curves and calibration curves. Kaplan-Meier and Cox regression analyses were conducted to evaluate survival outcomes for SBA patients with LM. Results: Synchronous LM was present in 13.4% of SBA patients (n = 276). Six independent predictive factors for LM were identified, including tumor location, T stage, N stage, surgical intervention, retrieval of regional lymph nodes (RORLN), and chemotherapy. The nomogram demonstrated good discriminative ability, with an area under the curve (AUC) of 83.8%. Patients with LM had significantly lower survival rates than those without LM (P < 0.001). Survival analysis revealed that advanced age, tumor location in the duodenum, surgery, RORLN and chemotherapy were associated with cancer-specific survival (CSS) in patients with LM originating from SBA. Conclusions: This study highlights the significant impact of LM on the survival of SBA patients and identifies key risk factors for its occurrence. The developed nomogram aids in targeted screening and personalized treatment planning.

Comprehensive molecular characterization of collecting duct carcinoma for therapeutic vulnerability.

Collecting duct carcinoma (CDC) is an aggressive rare subtype of kidney cancer with unmet clinical needs. Little is known about its underlying molecular alterations and etiology, primarily due to its rarity, and lack of preclinical models. This study aims to comprehensively characterize molecular alterations in CDC and identify its therapeutic vulnerabilities. Through whole-exome and transcriptome sequencing, we identified KRAS hotspot mutations (G12A/D/V) in 3/13 (23%) of the patients, in addition to known TP53, NF2 mutations. 3/13 (23%) patients carried a mutational signature (SBS22) caused by aristolochic acid (AA) exposures, known to be more prevalent in Asia, highlighting a geologically specific disease etiology. We further discovered that cell cycle-related pathways were the most predominantly dysregulated pathways. Our drug screening with our newly established CDC preclinical models identified a CDK9 inhibitor LDC000067 that specifically inhibited CDC tumor growth and prolonged survival. Our study not only improved our understanding of oncogenic molecular alterations of Asian CDC, but also identified cell-cycle machinery as a therapeutic vulnerability, laying the foundation for clinical trials to treat patients with such aggressive cancer.

PASTICCINO2 interacting with Golgi Anti-Apoptotic Proteins resists endoplasmic reticulum stress dependent on very long-chain fatty acids.

The endoplasmic reticulum (ER) is crucial for maintaining cell homeostasis because it is the primary site for synthesizing secreted and transmembrane proteins and lipids. The unfolded protein response (UPR) is activated to restore ER homeostasis under ER stress. However, the relationship between lipids and the ER stress response in plants is not well understood. Arabidopsis Golgi anti-apoptotic proteins (GAAPs) are involved in resisting ER stress. To elucidate the function of GAAPs, PASTICCINO2 (PAS2), involved in very long-chain fatty acid (VLCFA) synthesis, was found to interact with GAAPs and IRE1. Single pas2 and gaap1/gaap2pas2 double mutants exhibited increased seedling damage and impaired UPR response under chronic ER stress. Site mutation combined with genetic analysis revealed that the role of PAS2 in resisting ER stress depended on its VLCFA synthesis domain. VLCFA contents were upregulated under ER stress, which required GAAPs. Exogenous VLCFAs partially restored the defect in UPR upregulation caused by PAS2 or GAAP mutations under chronic ER stress. These findings demonstrate that the association of PAS2 with GAAPs confers plant resistance to ER stress by regulating VLCFA synthesis and the UPR. This provides a basis for further studies on the connection between lipids and cell fate decisions under stress.

Effect of metabolic dysfunction-associated steatotic liver disease on BNT162b2 immunogenicity against the severe acute respiratory syndrome coronavirus 2 omicron variant.

BACKGROUND AND AIM: We aimed to investigate the effect of metabolic dysfunction-associated steatotic liver disease (MASLD) on three-dose BNT162b2 immunogenicity to the omicron variant. METHODS: Adult recipients of three doses of BNT162b2 were prospectively recruited between May and December 2021. The serology of the neutralizing antibody by live virus microneutralization (vMN) to the omicron variant was measured at baseline, day 180, and day 360 after the first dose. The primary outcome was seroconversion (vMN titer ≥ 10) at day 360. Exposure of interest was MASLD, defined as hepatic steatosis (controlled attenuation parameter ≥ 248 dB/m on transient elastography) plus at least one of five cardiometabolic risk factors. Subjects with prior COVID-19 were excluded. A multivariable logistic regression model was used to derive the adjusted odds ratio of seroconversion with MASLD by adjusting for age, sex, antibiotic use, and proton pump inhibitor use. RESULTS: One hundred forty-eight BNT162b2 recipients (male: 48 [32.4%]; median age: 51.0 years [interquartile range, IQR: 44.5-57.3]) were recruited. The median time from the first dose to the third dose was 8.5 months (IQR: 7.9-8.9). MASLD subjects had a lower seroconversion rate than non-MASLD ones (89.6% vs 99.0%; P = 0.007). MASLD was the only independent risk factor for seroconversion (adjusted odds ratio: 0.051, 95% confidence interval: 0.002-0.440). Subgroup analysis of immunogenicity at 4 months after the third dose shows significantly lower vMN titer (13.06 [IQR: 7.69-22.20] vs 33.49 [IQR: 24.05-46.53]; P = 0.004) and seroconversion rate (76.9% vs 97.4%; P = 0.016) in MASLD than non-MASLD subjects, but not within 4 months from the third dose (vMN titer: 46.87 [IQR: 33.12-66.02] vs 41.86 [IQR: 34.47-50.91], P = 0.240; seroconversion rate: 94.3% vs 100%, P = 0.131). CONCLUSION: Metabolic dysfunction-associated steatotic liver disease was a risk factor for poorer immunogenicity to the omicron variant, with a more pronounced waning effect compared among three-dose BNT162b2 recipients.

The role of depth of general anesthesia in serum CGRP and SP level in diabetes patients.

BACKGROUND: Diabetes, which is associated with cardiovascular disease and related microvascular complications, affects life expectancy and decrease quality of life. A trial reports that the risk of patients with diabetes having cardiovascular disease is 2-4 times compared with that in patients without diabetes. OBJECTIVE: This study aims to investigate the relationship between depth of general anesthesia in patients with diabetes mellitus. METHODS: This clinical study totally includes 40 patients with diabetes mellitus, and these patients are divided into following two groups: diabetes mellitus deep anesthesia group and diabetes mellitus light anesthesia group, and then these patients receive general anesthesia combined with laparoscopic surgery. Preoperative patient general data and intraoperative patient general data are collected and analyzed. Calcitonin gene-related peptide (CGRP) and substance P (SP) level are determined by Enzyme-linked immunosorbent assay (ELISA). RESULTS: This study included a total of 40 patients. There were no significant differences in demographic and preoperative patient general data between the two groups. Measurements were taken for operative time, anesthesia time, recovery time after drug withdrawal, dwell time in the recovery room, intraoperative fluid volume, intraoperative blood loss, and intraoperative urine output between the two groups. Significant differences were observed in the recovery time after drug withdrawal between the two groups. CGRP and SP level in diabetes mellitus deep anesthesia group are evidently more than those in diabetes mellitus light anesthesia group. CONCLUSIONS: CGRP and SP level are involved in the diabetes mellitus and up-regulated CGRP and SP can prevent the development of diabetes mellitus. Our study extends the existing literature by addressing a gap in knowledge regarding the impact of anesthesia depth on neuropeptide levels in diabetes mellitus patients. By delineating this relationship, we aim to contribute to the advancement of perioperative care practices and ultimately improve outcomes for individuals with diabetes undergoing surgical procedures. Our study's findings provide valuable insights into the complex interactions between anesthesia, neuropeptides, and diabetes mellitus, offering the potential for personalized perioperative care, enhanced pain management, and improved surgical outcomes. These implications highlight the clinical relevance of our research and its potential to inform future advancements in perioperative care for diabetic patients undergoing surgery.

Transiently restricting individual amino acids protects Drosophila melanogaster against multiple stressors.

Nutrition and resilience are linked, though it is not yet clear how diet confers stress resistance or the breadth of stressors that it can protect against. We have previously shown that transiently restricting an essential amino acid can protect Drosophila melanogaster against nicotine poisoning. Here, we sought to characterize the nature of this dietary-mediated protection and determine whether it was sex, amino acid and/or nicotine specific. When we compared between sexes, we found that isoleucine deprivation increases female, but not male, nicotine resistance. Surprisingly, we found that this protection afforded to females was not replicated by dietary protein restriction and was instead specific to individual amino acid restriction. To understand whether these beneficial effects of diet were specific to nicotine or were generalizable across stressors, we pre-treated flies with amino acid restriction diets and exposed them to other types of stress. We found that some of the diets that protected against nicotine also protected against oxidative and starvation stress, and improved survival following cold shock. Interestingly, we found that a diet lacking isoleucine was the only diet to protect against all these stressors. These data point to isoleucine as a critical determinant of robustness in the face of environmental challenges.

The EFNA4 gene is a potential prognostic biomarker in pancreatic cancer: a bioinformatics analysis.

Background: Pancreatic cancer is a highly aggressive malignancy with poor prognosis, and there is an urgent need to understand its molecular mechanisms for early diagnosis and treatment. Despite surgical resection being the only effective treatment, most patients are diagnosed at an advanced stage, missing the optimal window for therapy. Identifying novel biomarkers is crucial for prognostic assessment, treatment planning, and early intervention. Ephrin A4 (EFNA4), a member of the receptor tyrosine kinase family, is involved in vascular and epithelial development via regulation of cell migration and rejection. However, the role of EFNA4 in pancreatic cancer has not been reported. Therefore, our study aimed to clarify the role of EFNA4 in pancreatic cancer through bioinformatics analysis and vitro experiments. Methods: The expression of EFNA4 and its potential value as a diagnostic and prognostic biomarker in pancreatic cancer was analyzed using data from The Cancer Genome Atlas (TCGA) and the Gene Expression Profiling Interactive Analysis (GEPIA) database. According to the expression level of EFNA4, patients were divided into high expression group and low expression group, and the correlation between overall survival (OS) and disease-free survival (DFS) with different expression levels of EFNA4 and clinical parameters were analyzed. Subsequently, reverse-transcription quantitative polymerase chain reaction (RT-qPCR) was performed to detect EFNA4 expression. The proliferation, invasion, and cloning ability of the cells were detected via Cell Counting Kit 8 (CCK8), Transwell, and plate cloning assays, respectively. Results: EFNA4 is highly expressed in pancreatic cancer, and upregulation of EFNA4 is associated with poor prognosis. In this study, EFNA4 expression was correlated with T stage and TNM (tumor-node-metastasis) stage of pancreatic cancer, and the median survival time and progression-free survival (PFS) were worse in those with high EFNA4 expression (394 days) than in those with low expression (525 days) [hazard ratio (HR): 1.47, 95% confidence interval (CI): 1.00-2.16, P=0.047]. In addition, EFNA4 was also found to be involved in the regulation of signal pathways such as cell adhesion, cyclic AMP, insulin secretion, pancreatic secretion, and protein digestion and absorption. In vitro experiments demonstrated that EFNA4 knockdown significantly inhibited the proliferation, cloning ability, and invasiveness of the PANC-1 and SW1990 pancreatic cancer cell lines. Conclusions: The abnormal expression of EFNA4 in pancreatic cancer is associated with poor prognosis. Knockout of EFNA4 gene could significantly inhibit the proliferation and invasion of pancreatic cancer cells. Therefore, EFNA4 may be one of the molecular targets for poor prognosis of patients with pancreatic cancer.

Combining multimodal diffusion-weighted imaging and morphological parameters for detecting lymph node metastasis in cervical cancer.

BACKGROUND: Accurate detection of lymph node metastasis (LNM) is crucial for determining the tumor stage, selecting optimal treatment, and estimating the prognosis for cervical cancer. This study aimed to assess the diagnostic efficacy of multimodal diffusion-weighted imaging (DWI) and morphological parameters alone or in combination, for detecting LNM in cervical cancer. METHODS: In this prospective study, we enrolled consecutive cervical cancer patients who received multimodal DWI (conventional DWI, intravoxel incoherent motion DWI, and diffusion kurtosis imaging) before treatment from June 2022 to June 2023. The largest lymph node (LN) observed on each side on imaging was matched with that detected on pathology to improve the accuracy of LN matching. Comparison of the diffusion and morphological parameters of LNs and the primary tumor between the positive and negative LN groups. A combined diagnostic model was constructed using multivariate logistic regression, and the diagnostic performance was evaluated using receiver operating characteristic curves. RESULTS: A total of 93 cervical cancer patients were enrolled: 35 with LNM (48 positive LNs were collected), and 58 without LNM (116 negative LNs were collected). The area under the curve (AUC) values for the apparent diffusion coefficient, diffusion coefficient, mean diffusivity, mean kurtosis, long-axis diameter, short-axis diameter of LNs, and the largest primary tumor diameter were 0.716, 0.720, 0.716, 0.723, 0.726, 0.798, and 0.744, respectively. Independent risk factors included the diffusion coefficient, mean kurtosis, short-axis diameter of LNs, and the largest primary tumor diameter. The AUC value of the combined model based on the independent risk factors was 0.920, superior to the AUC values of all the parameters mentioned above. CONCLUSION: Combining multimodal DWI and morphological parameters improved the diagnostic efficacy for detecting cervical cancer LNM than using either alone.

Knowledge, Attitudes and Practices Among Anesthesia and Thoracic Surgery Medical Staff Toward Ai-PCA.

Purpose: Artificial intelligence (AI) is increasingly influencing various medical fields, including anesthesiology. The Introduction of artificial intelligent patient-controlled analgesia (Ai-PCA) has been seen as a significant advancement in pain management. However, the adoption and practical application of Ai-PCA by medical staff, particularly in anesthesia and thoracic surgery, have not been extensively studied. This study aimed to investigate the knowledge, attitudes and practices (KAP) among anesthesia and thoracic surgery medical staff toward artificial intelligent patient-controlled analgesia (Ai-PCA). Participants and Methods: This web-based cross-sectional study was conducted between November 1, 2023 and November 15, 2023 at Jiangsu Cancer Hospital. A self-designed questionnaire was developed to collect demographic information of anesthesia and thoracic surgery medical staff, and to assess their knowledge, attitudes and practices toward Ai-PCA. Results: A total of 519 valid questionnaires were collected. Among the participants, 278 (53.56%) were female, 497 (95.76%) were employed in the field of anesthesiology, and 188 (36.22%) had participated in Ai-PCA training. The mean knowledge, attitude, and practice scores were 7.8±1.75 (possible range: 0-10), 37.43±4.16 (possible range: 9-45), and 28.38±9.27 (possible range: 9-45), respectively. Conclusion: The findings revealed that anesthesia and thoracic surgery medical staff have sufficient knowledge, active attitudes, but poor practices toward the Ai-PCA. Comprehensive training programs are needed to improve anesthesia and thoracic surgery medical staff's practices in this area.

Nursing students' perception of digital technology in clinical education among undergraduate programs: A qualitative systematic review.

BACKGROUND: Although past research has highlighted the importance of digital technology in clinical education among undergraduate nursing programs, facilitators and barriers to application still need to be systematically understood. AIM: The objective of this systematic review is to synthesize existing literature reporting on nursing students' experiences with digital technology in clinical education among undergraduate programs to identify gaps and design future educational programs. METHODS: We conducted a qualitative systematic review and reported the following Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) guidelines. PubMed, Embase, CINAHL, Web of Science databases were searched for studies published in English. Qualitative studies focusing on nursing students' experiences of digital technology in clinical education were included, and data were synthesized by extracting findings, developing categories, and developing synthesized findings. RESULTS: Six synthesized findings were concluded:1) nature of digital education, 2) facilitators, 3) barriers, 4) benefits to learning, 5) mixed experience, and 6) challenges. CONCLUSIONS: The systematic review revealed that the utilization of digital technology in clinical practice is beneficial to the learning of undergraduate nursing students. Facilitators and challenges of digital education, as well as barriers to digital education identified in this review can be used to design more effective digital education activities. Digital technology can be used to supplement traditional teaching, and it is worthwhile to explore more in the area of digital education to promote the cultivation of nursing students' competency.

Expression profiling and function analysis identified new genes regulating cumulus expansion and cumulus cell apoptosis in mouse oocytes.

In brief: Genes expressed in cumulus cells might be used as markers for competent oocytes/embryos. This study identified and validated a new group of cumulus expansion and/or apoptosis-regulating genes, which may be used for selection of quality oocytes/embryos. Abstract: Studies on the mechanisms behind cumulus expansion and cumulus cell (CC) apoptosis are essential for understanding the mechanisms for oocyte maturation. Genes expressed in CCs might be used as markers for competent oocytes and/or embryos. In this study, both in vitro (IVT) and in vivo (IVO) mouse oocyte models with significant difference in cumulus expansion and CC apoptosis were used to identify and validate new genes regulating cumulus expansion and CC apoptosis of mouse oocytes. We first performed mRNA sequencing and bioinformatic analysis using the IVT oocyte model to identify candidate genes. We then analyzed functions of the candidate genes by RNAi or gene overexpression to select the candidate cumulus expansion and CC apoptosis-regulating genes. Finally, we validated the cumulus expansion and CC apoptosis-regulating genes using the IVO oocyte model. The results showed that while Spp1, Sdc1, Ldlr, Ezr and Mmp2 promoted, Bmp2, Angpt2, Edn1, Itgb8, Cxcl10 and Agt inhibited cumulus expansion. Furthermore, Spp1, Sdc1 and Ldlr inhibited CC apoptosis. In conclusion, by using both IVT and IVO oocyte models, we have identified and validated a new group of cumulus expansion and/or apoptosis-regulating genes, which may be used for selection of quality oocytes/embryos and for elucidating the molecular mechanisms behind oocyte maturation.

Insight into the mechanism of the aggregation behavior of wheat protein modulated by l-lysine under microwave irradiation.

This study explored the mechanism of l-lysine intervention in wheat gluten protein (WG) gel formation under a microwave (MW) field. The results showed that the MW treatment had higher ζ-potential values at the same heating rate. After adding l-lysine, the solution conductivity and dielectric loss were significantly increased. Moreover, the WG gel strength enhanced 4.40% under the MW treatment. The Fourier spectra showed that the α-helix content was decreased 13.78% with the addition of lysine. The ultraviolet absorption spectra and fluorescence spectra indicated that MW irradiation impacted the interactions between WG molecules more effectively than the water bath heating, promoting the denaturation and unfolding of the protein structure. In addition, scanning electron microscopy analysis showed that the incorporation of lysine promoted an ordered network structure formation of the protein, which enhanced the gel properties. This indicated that the zwitterion of l-lysine played a regulatory role in the aggregation of proteins in the MW field.

MEFFGRN: Matrix enhancement and feature fusion-based method for reconstructing the gene regulatory network of epithelioma papulosum cyprini cells by spring viremia of carp virus infection.

Gene regulatory networks (GRNs) are crucial for understanding organismal molecular mechanisms and processes. Construction of GRN in the epithelioma papulosum cyprini (EPC) cells of cyprinid fish by spring viremia of carp virus (SVCV) infection helps understand the immune regulatory mechanisms that enhance the survival capabilities of cyprinid fish. Although many computational methods have been used to infer GRNs, specialized approaches for predicting the GRN of EPC cells following SVCV infection are lacking. In addition, most existing methods focus primarily on gene expression features, neglecting the valuable network structural information in known GRNs. In this study, we propose a novel supervised deep neural network, named MEFFGRN (Matrix Enhancement- and Feature Fusion-based method for Gene Regulatory Network inference), to accurately predict the GRN of EPC cells following SVCV infection. MEFFGRN considers both gene expression data and network structure information of known GRN and introduces a matrix enhancement method to address the sparsity issue of known GRN, extracting richer network structure information. To optimize the benefits of CNN (Convolutional Neural Network) in image processing, gene expression and enhanced GRN data were transformed into histogram images for each gene pair respectively. Subsequently, these histograms were separately fed into CNNs for training to obtain the corresponding gene expression and network structural features. Furthermore, a feature fusion mechanism was introduced to comprehensively integrate the gene expression and network structural features. This integration considers the specificity of each feature and their interactive information, resulting in a more comprehensive and precise feature representation during the fusion process. Experimental results from both real-world and benchmark datasets demonstrate that MEFFGRN achieves competitive performance compared with state-of-the-art computational methods. Furthermore, study findings from SVCV-infected EPC cells suggest that MEFFGRN can predict novel gene regulatory relationships.

The suboptimal clinical applicability of prognostic prediction models for severe postpartum hemorrhage: a meta-epidemiological study.

OBJECTIVES: To systematically investigate clinical applicability of the current prognostic prediction models for severe postpartum hemorrhage (SPPH). STUDY DESIGN AND SETTING: A meta-epidemiological study of prognostic prediction models was conducted for SPPH. A pre-designed structured questionnaire was adopted to extract the study characteristics, predictors and the outcome, modeling methods, predictive performance, the classification ability for high-risk individuals, and clinical use scenarios. The risk of bias among studies was assessed by the Prediction model Risk Of Bias ASsessment Tool (PROBAST). RESULTS: Twenty-two studies containing 27 prediction models were included. The number of predictors in the final models varied from 3 to 53. However, one-third of the models (11) did not clearly specify the timing of predictor measurement. Calibration was found to be lacking in 10 (37.0%) models. Among the 20 models with an incidence rate of predicted outcomes below 15.0%, none of the models estimated the area under the precision-recall curve, and all reported positive predictive values were below 40.0%. Only two (7.4%) models specified the target clinical setting, while seven (25.9%) models clarified the intended timing of model use. Lastly, all 22 studies were deemed to be at high risk of bias. CONCLUSION: Current SPPH prediction models have limited clinical applicability due to methodological flaws, including unclear predictor measurement, inadequate calibration assessment, and insufficient evaluation of classification ability. Additionally, there is a lack of clarity regarding the timing for model use, target users, and clinical settings. These limitations raise concerns about the reliability and usefulness of these models in real-world clinical practice.

An evidence-based breathing exercise intervention for chronic pain management in breast cancer survivors: A phase II randomized controlled trial.

OBJECTIVE: Explore the preliminary effects of a breathing exercise (BE) intervention on chronic pain among breast cancer survivors. METHODS: This two-parallel-arm, open-label pilot randomized controlled trial recruited 72 breast cancer survivors who were randomly allocated to either the control or intervention group (n = 36 each). Both groups received usual care and a pain information booklet, while the intervention group received 4 weeks of additional BE. The primary clinical outcome was measured using the Brief Pain Inventory (BPI), with secondary clinical outcomes measured by the Hospital Anxiety and Depression Scale (HADS), Quality of Life Patient/Cancer Survivor Version in Chinese (QOLCSV-C), and Functional Assessment of Cancer Therapy- Breast (FACT-B) immediately post-intervention and at 4-week follow-up. Both adjusted and unadjusted Generalized Estimating Equation models were utilized to assess the BE's potential effects, with safety assessed through participant self-report. RESULTS: Sixty-eight participants completed the study. Statistical significance was observed in BPI in both adjusted and unadjusted models at post-intervention and follow-up (p < 0.05). BE demonstrated positive effects on anxiety, depression and quality of life improvement across all measures and timepoints in both adjusted and unadjusted models (p < 0.05). The effect sizes were smaller in the adjusted model. Three mild transient discomforts were reported associated with BE practice including dizziness, tiredness and yawning, without requirement of medical treatment. No severe adverse events occurred. CONCLUSION: This BE intervention appears effective in alleviating chronic pain, anxiety and depression, and improving quality of life for breast cancer survivors. Fully powered large-scale studies are required to confirm its effects.

CDK4/6 inhibition augments anti-tumor efficacy of XPO1 inhibitor selinexor in natural killer/T-cell lymphoma.

XPO1 is an attractive and promising therapeutic target frequently overexpressed in multiple hematological malignancies. The clinical use of XPO1 inhibitors in natural killer/T-cell lymphoma (NKTL) is not well documented. Here, we demonstrated that XPO1 overexpression is an indicator of poor prognosis in patients with NKTL. The compassionate use of the XPO1 inhibitor selinexor in combination with chemotherapy showed favorable clinical outcomes in three refractory/relapsed (R/R) NKTL patients. Selinexor induced complete tumor regression and prolonged survival in sensitive xenografts but not in resistant xenografts. Transcriptomic profiling analysis indicated that sensitivity to selinexor was correlated with deregulation of the cell cycle machinery, as selinexor significantly suppressed the expression of cell cycle-related genes. CDK4/6 inhibitors were identified as sensitizers that reversed selinexor resistance. Mechanistically, targeting CDK4/6 could enhance the anti-tumor efficacy of selinexor via the suppression of CDK4/6-pRb-E2F-c-Myc pathway in resistant cells, while selinexor alone could dramatically block this pathway in sensitive cells. Overall, our study provids a preclinical proof-of-concept for the use of selinexor alone or in combination with CDK4/6 inhibitors as a novel therapeutic strategy for patients with R/R NKTL.

Development and validation of a nomogram for predicting cancer-specific survival in small-bowel adenocarcinoma patients using the SEER database.

BACKGROUND: Small bowel adenocarcinoma (SBA) is a rare gastrointestinal malignancy forwhich survival is hampered by late diagnosis, complex responses to treatment, and poor prognosis. Accurate prognostic tools are crucial for optimizing treatment strategies and improving patient outcomes. This study aimed to develop and validate a nomogram based on the Surveillance, Epidemiology, and End Results (SEER) database to predict cancer-specific survival (CSS) in patients with SBA and compare it to traditional American Joint Committee on Cancer (AJCC) staging. METHODS: We analyzed data from 2,064 patients diagnosed with SBA between 2010 and 2020 from the SEER database. Patients were randomly assigned to training and validation cohorts (7:3 ratio). Kaplan‒Meier survival analysis, Cox multivariate regression, and nomograms were constructed for analysis of 3-year and 5-year CSS. The performance of the nomograms was evaluated using Harrell's concordance index (C-index), the area under the receiver operating characteristic (ROC) curve, calibration curves, decision curve analysis (DCA), net reclassification improvement (NRI), and integrated discrimination improvement (IDI). RESULTS: Multivariate Cox regression identified sex, age at diagnosis, marital status, tumor site, pathological grade, T stage, N stage, M stage, surgery, retrieval of regional lymph nodes (RORLN), and chemotherapy as independent covariates associated with CSS. In both the training and validation cohorts, the developed nomograms demonstrated superior performance to that of the AJCC staging system, with C-indices of 0.764 and 0.759, respectively. The area under the curve (AUC) values obtained by ROC analysis for 3-year and 5-year CSS prediction significantly surpassed those of the AJCC model. The nomograms were validated using calibration and decision curves, confirming their clinical utility and superior predictive accuracy. The NRI and IDI indicated the enhanced predictive capability of the nomogram model. CONCLUSION: The SEER-based nomogram offers a significantly superior ability to predict CSS in SBA patients, supporting its potential application in clinical decision-making and personalized approaches to managing SBA to improve survival outcomes.

Correlative Super-resolution Optical and Electron Microscopic Imaging of Intracellular Ribosomal RNA by a Terpyridine Iridium(III) Complex.

Ribosomal RNA (rRNA) plays a vital role in binding amino acids together, which dictates the primary structure of a protein. Visualization of its intracellular distribution and dynamics during protein synthesis enables a better understanding of the correlated biological essence. However, appropriate tools targeting live cell rRNA that are capable of multimodal imaging at the nanoscale are still lacking. Here, we rationally designed a series of terpyridine ammonium iridium(III) complexes, one of which is capable of selectively labeling rRNA in living cells. Its metal core and photostable nature allow further super-resolution STED imaging of rRNA found on the rough endoplasmic reticulum at a ∼40 nm resolution that is well correlated under correlative light and electron microscopy (CLEM). Interestingly, the Ir(III) complex demonstrated rRNA dynamics in living cells while boosting protein synthesis at the nanoscale. Our work offers a versatile tool to visualize rRNA synchronously under optical and electron microscopy, which provides a better understanding of rRNA evolution in living systems.