Development of a PROMIS multidimensional cancer-related fatigue (mCRF) form using modern psychometric techniques.

PURPOSE: To develop a PRO assessment of multidimensional cancer-related fatigue based on the PROMIS fatigue assessments. METHOD: Cancer patients reporting fatigue were recruited from a comprehensive cancer care center and completed a survey including 39 items from the PROMIS Cancer Item Bank-Fatigue. Component and factor structures of the fatigue items were explored with Monte Carlo parallel factor and Mokken analyses, respectively. Psychometric properties were determined using item response theory, ensuring unidimensionality, scalability, and item independence. RESULTS: Fatigue scores from a sample of 333 fatigued cancer patients (mean age = 59.50, SD = 11.62, 67% women) were used in all scale development analyses. Psychometric analyses yielded 3 dimensions: motivational fatigue (15 items), cognitive fatigue (9 items), and physical fatigue (9 items). The subscales showed strong unidimensionality, were scalable, and were free of differential item function. Confirmatory factor analyses in a new sample of 182 patients confirmed the findings. CONCLUSION: The resulting 33-item PROMIS multidimensional cancer-related fatigue (mCRF) form provides a novel measure for the assessment of the different dimensions of cancer-related fatigue. It is the only multidimensional scale specific for cancer patients that has been developed using modern psychometric approaches. With its 3 dimensions (motivational, cognitive, and physical fatigue), this scale accurately captures the fatigue experienced by cancer patients, allowing clinicians to optimize fatigue management and improve patient care. The scale could also advance research on the nature and experience of cancer-related fatigue.

Cost-effectiveness analysis of different anesthesia strategies for transperineal MRI/US fusion prostate biopsy.

This study aims to conduct a cost-effectiveness analysis of three different anesthesia strategies, namely chatting while under local anesthesia (Chat-LA), total intravenous anesthesia (TIVA), and general anesthesia with laryngeal mask airway (GA-LMA), employed in transperineal magnetic resonance imaging (MRI)/ultrasound (US) fusion prostate biopsy (TP-MUF-PB). A retrospective study was conducted involving 1202 patients who underwent TP-MUF-PB from June 2016 to April 2023 at The First Affiliated Hospital of Soochow University (Suzhou, China). Clinical data and outcomes, including total costs, complications, and quality-adjusted life years (QALYs), were compared. Probability sensitivity and subgroup analyses were also performed. Chat-LA was found to be the most cost-effective option, outperforming both TIVA and GA-LMA. However, subgroup analyses revealed that in younger patients (under 65 years old) and those with smaller prostate volumes (<40 ml), TIVA emerged as a more cost-effective strategy. While Chat-LA may generally be the most cost-effective and safer anesthesia method for TP-MUF-PB, personalization of anesthesia strategies is crucial, considering specific patient demographics such as age and prostate volume.

Randomized phase 2 trial of tremelimumab and durvalumab in combination versus sequentially in recurrent platinum-resistant ovarian cancer.

BACKGROUND: Single-agent immune checkpoint inhibitors (ICIs) have demonstrated limited responses in recurrent ovarian cancer; however, 30%-40% of patients achieve stable disease. The primary objective was to estimate progression-free survival (PFS) after sequential versus combination cytotoxic T-lymphocyte antigen 4 and programmed death ligand 1 ICIs in patients with platinum-resistant high-grade serous ovarian cancer (HGSOC). METHODS: Patients were randomized to a sequential arm (tremelimumab followed by durvalumab on progression) or a combination arm (tremelimumab plus durvalumab, followed by durvalumab) via a Bayesian adaptive design that made it more likely for patients to be randomized to the more effective arm. The primary end point was immune-related PFS (irPFS). RESULTS: Sixty-one subjects were randomized to sequential (n = 38) or combination therapy (n = 23). Thirteen patients (34.2%) in the sequential arm received durvalumab. There was no difference in PFS in the sequential arm (1.84 months; 95% CI, 1.77-2.17 months) compared with the combination arm (1.87 months; 95% CI, 1.77-2.43 months) (p = .402). In the sequential arm, no responses were observed, although 12 patients (31.6%) demonstrated stable disease. In the combination arm, two patients (8.7%) had partial response, whereas one patient (4.4%) had stable disease. Adverse events were consistent with those previously reported for ICIs. Patient-reported outcomes were similar in both arms. CONCLUSIONS: There was no difference in irPFS for combination tremelimumab plus durvalumab compared to tremelimumab alone (administered as part of a sequential treatment strategy) in a heavily pretreated population of patients with platinum-resistant HGSOC. Response rates were comparable to prior reports, although the combination regimen did not add significant benefit, as has been previously described.

A preoperative nomogram predicting risk of lymph node metastasis for early-stage cervical cancer.

OBJECTIVE: This study aimed to develop a preoperative nomogram based on clinical and pathological characteristics to provide a more individualized and accurate estimation of lymph node metastasis (LNM) in patients with early-stage cervical cancer. METHODS: A total of 7,349 early-stage cervical cancer patients with pathologically confirmed between 1988 and 2015 were obtained from the Surveillance, Epidemiology, and End Results (SEER) database. All the patients were divided into training (n = 5,500) and validation (n = 1,849) cohorts randomly. A cohort of 455 patients from multicenter was used for the external validation. We established a multivariate logistic regression model based on preoperative clinicopathological data, from which a nomogram was developed and validated. A predicted probability of LNM < 5% was defined as low risk. RESULTS: From multivariate logistic regression analysis, age at diagnosis, histologic subtype, tumor grade, tumor size and FIGO stage were identified as preoperative independent risk factors of LNM. The nomogram incorporating these factors demonstrated good discrimination and calibration (concordance index = 0.723; 95% confidence interval (CI), 0.707-0.738). In the validation cohort, the discrimination accuracy was 0.745 (95% CI, 0.720-0.770) and 0.747 (95% CI, 0.690-0.804), respectively. The nomogram was well calibrated with a high concordance probability. We also established an R-enabled Internet browser for LNM risk assessment, which tool may be convenient for physicians. CONCLUSIONS: We developed an effective preoperative nomogram based on clinical and pathological characteristics to predict LNM for early-stage cervical cancer. This model could improve clinical trial design and help physicians to decide whether to perform lymphadenectomy or not.

Physical well-being recovery trajectories by reconstruction modality in women undergoing mastectomy and breast reconstruction: Significant predictors and health-related quality of life outcomes.

OBJECTIVES: We sought to identify trajectories of patient-reported outcomes, specifically physical well-being of the chest (PWBC), in patients who underwent postmastectomy breast reconstruction, and further assessed its significant predictors, and its relationship with health-related quality of life (HRQOL). METHODS: We used data collected as part of the Mastectomy Reconstruction Outcomes Consortium study within a 2-year follow-up in 2012-2017, with 1422, 1218,1199, and 1417 repeated measures at assessment timepoints of 0,3,12, and 24 months, respectively. We performed latent class growth analysis (LCGA) in the implant group (IMPG) and autologous group (AUTOG) to identify longitudinal change trajectories, and then assessed its significant predictors, and its relationship with HRQOL by conducting multinomial logistic regression. RESULTS: Of the included 1424 patients, 843 were in IMPG, and 581 were in AUTOG. Both groups experienced reduced PWBC at follow-up. LCGA identified four distinct PWBC trajectories (χ2 = 1019.91, p<0.001): low vs medium high vs medium low vs high baseline PWBC that was restored vs. not-restored after 2 years. In 76.63%(n = 646) of patients in IMPG and 62.99% (n = 366) in AUTOG, PWBC was restored after two years. Patients in IMPG exhibited worse PWBC at 3 months post-surgery than that in AUTOG. Patients with low baseline PWBC that did not improve at 2-year follow up (n = 28, 4.82% for AUTOG) were characterized by radiation following reconstruction and non-white ethnicity. In IMPG, patients with medium low-restored trajectory were more likely to experience improved breast satisfaction, while patients developing high-restored trajectories were less likely to have worsened psychosocial well-being. CONCLUSION: Although more women in IMPG experienced restored PWBC after 2 years, those in AUTOG exhibited a more favorable postoperative trajectory of change in PWBC. This finding can inform clinical treatment decisions, help manage patient expectations for recovery, and develop rehabilitation interventions contributing to enhancing the postoperative quality of life for breast cancer patients.

Enhanced Surgical Decision-Making Tools in Breast Cancer: Predicting 2-Year Postoperative Physical, Sexual, and Psychosocial Well-Being following Mastectomy and Breast Reconstruction (INSPiRED 004).

BACKGROUND: We sought to predict clinically meaningful changes in physical, sexual, and psychosocial well-being for women undergoing cancer-related mastectomy and breast reconstruction 2 years after surgery using machine learning (ML) algorithms trained on clinical and patient-reported outcomes data. PATIENTS AND METHODS: We used data from women undergoing mastectomy and reconstruction at 11 study sites in North America to develop three distinct ML models. We used data of ten sites to predict clinically meaningful improvement or worsening by comparing pre-surgical scores with 2 year follow-up data measured by validated Breast-Q domains. We employed ten-fold cross-validation to train and test the algorithms, and then externally validated them using the 11th site's data. We considered area-under-the-receiver-operating-characteristics-curve (AUC) as the primary metric to evaluate performance. RESULTS: Overall, between 1454 and 1538 patients completed 2 year follow-up with data for physical, sexual, and psychosocial well-being. In the hold-out validation set, our ML algorithms were able to predict clinically significant changes in physical well-being (chest and upper body) (worsened: AUC range 0.69-0.70; improved: AUC range 0.81-0.82), sexual well-being (worsened: AUC range 0.76-0.77; improved: AUC range 0.74-0.76), and psychosocial well-being (worsened: AUC range 0.64-0.66; improved: AUC range 0.66-0.66). Baseline patient-reported outcome (PRO) variables showed the largest influence on model predictions. CONCLUSIONS: Machine learning can predict long-term individual PROs of patients undergoing postmastectomy breast reconstruction with acceptable accuracy. This may better help patients and clinicians make informed decisions regarding expected long-term effect of treatment, facilitate patient-centered care, and ultimately improve postoperative health-related quality of life.

Effects of Jatrorrhizine on inflammatory response induced by H2O2 in microglia by regulating the MAPK/NF-κB/NLRP3 signaling pathway.

Microglia-induced neuroinflammation is a contributing factor to neurodegenerative diseases. Jatrorrhizine (JAT), an alkaloid isolated from Huanglian, has been shown to have neuroprotective effects against various neurodegenerative diseases, but its impact on microglia-induced neuroinflammation remains unclear. In this study, we investigated the role of JAT in MAPK/NF-κB/NLRP3 signaling pathway in an H2O2-induced oxidative stress model using microglia (N9 cells). We divided cells into six groups, including control, JAT, H2O2, H2O2 + 5 µmol/L JAT, H2O2 + 10 µmol/L JAT, and H2O2 + 20 µmol/L minocycline groups. Cell viability was measured using MTT assay and TNF-α levels were detected with an ELISA Kit. Western blot was used to detect NLRP3, HMGB1, NF-κB, p-NF-κB, ERK, p-ERK, p38, p-p38, p-JNK, JNK, IL-1ß, and IL-18 expressions. Our results showed that JAT intervention improved H2O2-induced cytotoxicity in N9 cells and reduced the elevated expression of TNF-α, IL-1ß, IL-18, p-ERK/ERK, p-p38/p38, p-JNK/JNK, p-p65/p65, NLRP3, and HMGB1 in H2O2 group. Furthermore, treatment with ERK inhibitor SCH772984 specifically blocked ERK phosphorylation, resulting in decreased protein levels of p-NF-κB, NLRP3, IL-1ß, and IL-18 in H2O2 group. These results suggest that the MAPK/NF-κB signaling pathway may regulate the protein levels of NLRP3. Overall, our study indicates that JAT may have a protective effect on H2O2-treated microglia via inhibition the MAPK/NF-κB/NLRP3 pathway and could be a potential therapeutic approach for neurodegenerative diseases.

Efficacy of Chinese Herbal Medicines on Pregnancy Outcomes in Patients with Endometriosis in Long-Term Management: A Multicenter Retrospective Cohort Study.

OBJECTIVE: To analyze the factors related to pregnancy of endometriosis and whether Chinese herbal medicines (CHMs) can improve pregnancy outcomes in patients with endometriosis in long-term management. METHODS: This multicenter cohort study retrospectively analyzed the clinical data of endometriosis patients with fertility needs from January 2019 to November 2019. A total of 252 patients with endometriosis from 5 level-III Grade A hospitals in Beijing were included in this study. Univariate and multivariate logistic regression analysis were performed for the relevant factors. The propensity score matching (PSM) function of SPSS software was used to match the CHMs group with the non-CHMs group. The pregnancy rate and live birth rate were analyzed. RESULTS: The results of univariate analysis showed that age, disease course, presence of infertility, presence of adenomyosis, time after surgery or use of gonadotropin-releasing hormone agonist (GnRH-a), use of CHMs and follow-up time were influencing factors of pregnancy in endometriosis patients (P<0.05). The results of multivariate analysis showed that age, presence of adenomyosis, time after surgery or use of GnRH-a, use of CHMs and follow-up time were independent factors affecting pregnancy in endometriosis patients, among which, age ⩾35 years old, presence of adenomyosis and follow-up time >6 months were independent risk factors (OR=0.445, 0.348, 0.140, respectively, P<0.05), time after surgery or use of GnRH-a ⩽6 months and use of CHMs were independent protective factors (OR=3.839, 3.842, respectively, P<0.05). After PSM, 99 pairs of two groups were matched successfully. The pregnancy rate of the CHMs group was higher than that of the non-CHMs group [55.56% (55/99) vs. 36.36% (36/99), P<0.05]. The live birth rate of the CHMs group was higher than that of the non-CHMs group [49.49% (49/99) vs. 35.35% (35/99), P<0.05]. CONCLUSION: CHMs can effectively improve clinical pregnancy rate and live birth rate of patients with endometriosis in the chronic disease management.

Evaluation of the clinical efficacy of vaginal treatment options for persistent high-risk human papillomavirus infection after excisional treatment of cervical high-grade squamous intraepithelial lesions: a systematic review and Bayesian network meta-analysis.

BACKGROUND: To evaluate the clinical efficacy of different vaginal administration on cervical persistent high-risk human papillomavirus (HR-HPV) infection after excisional treatment for high-grade squamous intraepithelial lesions (HSIL). METHODS: Six databases (PubMed, EmBase, Cochrane Central, China Knowledge Network database, China Biomedical Literature Service, and WanFang database) were searched to collect randomized controlled trials (RCTs) of various types of vaginal administration compared to no treatment on persistent HR-HPV infection after HSIL excisional treatment, and comprehensive analysis of the clearance of different drugs on HR-HPV was performed using Bayesian reticulation meta-analysis. RESULTS: The study analyzed the efficacy of eight interventions, including Interferon, Baofukang, Paiteling, Bletilla striata Sanhuang Powder, Lactobacilli vaginal capsules, Fuanning + Interferon, Interferon + Lactobacilli vaginal capsules, and Interferon + Baofukang, on the clearance of HR-HPV after excisional treatment through pooling and analyzing data from 52 RCTs. The results of the study demonstrated that Interferon + Lactobacilli vaginal capsules [OR 16.0 (95% CIs 8.1-32.0)], Interferon + Fuanning [OR 16.0 (95% CIs 1.1-52.0)], and Interferon + Baofukang [OR 14.0 (95% CIs 6.8-28.0)] were all found to significantly improve postoperative HR-HPV clearance rates when compared to no treatment. Furthermore, when studies with high-risk bias were excluded, Interferon + Lactobacilli vaginal capsules [OR 8.6 (95% CIs 4.7-19.0)] and Interferon + Baofukang [OR 22.0 (95% CIs 8.7-59.0)] were still found to be positively associated with increased postoperative HR-HPV clearance rate. Additionally, the study´s results also indicate that Interferon + Baofukang was effective in enhancing the postoperative HR-HPV clearance rates, mainly when the studies were restricted to a follow-up period of at least 12 months [OR 9.6 (95% CIs 2.9-34.0)]. However, it is important to note that the majority of the trials (29 out of 52, 51.6%) were rated as moderate to high risk of bias, and the certainty of the evidence was moderate to very low. CONCLUSION: The application of various forms of vaginal administration, except for individual use of Lactobacilli vaginal capsules, is more efficacious than no treatment in patients with cervical persistent HR-HPV infection after excisional treatment. However, all of the estimates of the effect size for change in the efficiency of HR-HPV clearance are uncertain. Our confidence in effect estimates and ranking of treatments is low, which needs larger, more rigorous, and longer follow-up RCTs to resolve.

Machine learning models for 180-day mortality prediction of patients with advanced cancer using patient-reported symptom data.

PURPOSE: The objective of the current study was to develop and test the performances of different ML algorithms which were trained using patient-reported symptom severity data to predict mortality within 180 days for patients with advanced cancer. METHODS: We randomly selected 630 of 689 patients with advanced cancer at our institution who completed symptom PRO measures as part of routine care between 2009 and 2020. Using clinical, demographic, and PRO data, we trained and tested four ML algorithms: generalized regression with elastic net regularization (GLM), extreme gradient boosting (XGBoost) trees, support vector machines (SVM), and a single hidden layer neural network (NNET). We assessed the performance of algorithms individually as well as part of an unweighted voting ensemble on the hold-out testing sample. Performance was assessed using area under the receiver-operating characteristic curve (AUROC), sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV). RESULTS: The starting cohort of 630 patients was randomly partitioned into training (n = 504) and testing (n = 126) samples. Of the four ML models, the XGBoost algorithm demonstrated the best performance for 180-day mortality prediction in testing data (AUROC = 0.69, sensitivity = 0.68, specificity = 0.62, PPV = 0.66, NPV = 0.64). Ensemble of all algorithms performed worst (AUROC = 0.65, sensitivity = 0.65, specificity = 0.62, PPV = 0.65, NPV = 0.62). Of individual PRO symptoms, shortness of breath emerged as the variable of highest impact on the XGBoost 180-mortality prediction (1-AUROC = 0.30). CONCLUSION: Our findings support ML models driven by patient-reported symptom severity as accurate predictors of short-term mortality in patients with advanced cancer, highlighting the opportunity to integrate these models prospectively into future studies of goal-concordant care.

Intelligent multi-modal shear wave elastography to reduce unnecessary biopsies in breast cancer diagnosis (INSPiRED 002): a retrospective, international, multicentre analysis.

BACKGROUND: Breast ultrasound identifies additional carcinomas not detected in mammography but has a higher rate of false-positive findings. We evaluated whether use of intelligent multi-modal shear wave elastography (SWE) can reduce the number of unnecessary biopsies without impairing the breast cancer detection rate. METHODS: We trained, tested, and validated machine learning algorithms using SWE, clinical, and patient information to classify breast masses. We used data from 857 women who underwent B-mode breast ultrasound, SWE, and subsequent histopathologic evaluation at 12 study sites in seven countries from 2016 to 2019. Algorithms were trained and tested on data from 11 of the 12 sites and externally validated using the additional site's data. We compared findings to the histopathologic evaluation and compared the diagnostic performance between B-mode breast ultrasound, traditional SWE, and intelligent multi-modal SWE. RESULTS: In the external validation set (n = 285), intelligent multi-modal SWE showed a sensitivity of 100% (95% CI, 97.1-100%, 126 of 126), a specificity of 50.3% (95% CI, 42.3-58.3%, 80 of 159), and an area under the curve of 0.93 (95% CI, 0.90-0.96). Diagnostic performance was significantly higher compared to traditional SWE and B-mode breast ultrasound (P < 0.001). Unlike traditional SWE, positive-predictive values of intelligent multi-modal SWE were significantly higher compared to B-mode breast ultrasound. Unnecessary biopsies were reduced by 50.3% (79 versus 159, P < 0.001) without missing cancer compared to B-mode ultrasound. CONCLUSION: The majority of unnecessary breast biopsies might be safely avoided by using intelligent multi-modal SWE. These results may be helpful to reduce diagnostic burden for patients, providers, and healthcare systems.

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The imbalance of regional development is one of the important obstacles for the implementation of regio-nal coordinated development strategy. Based on the panel data of 41 cities in the Yangtze River Delta from 2010 to 2019, the regional coordinated development index system with five subsystems was constructed, including economic development, science and education, infrastructure, people's life, and resource and environment. With the help of GeoDa and ArcGIS software, we used measurement model of regional coordinated development and method of exploratory spatial data analysis to analyze the temporal and spatial variations and internal correlation of various elements of regional coordinated development in the Yangtze River Delta. The results showed that, from the perspective of regional development, the coordination of regional development in the Yangtze River Delta had increased annually from 2010 to 2019. The level of economic development and science and education in Shanghai and Suzhou was ahead of other cities, while the development coordination of Northwest Anhui, Zhoushan and Huangshan was weaker than other cities. The order of average autocorrelation degree of each subsystem from high to low in the Yangtze River Delta from 2010 to 2019 was people's life, economic development, resource and environment, science and education, and infrastructure. Among them, the global Moran's index (Moran I) of economic development and science and education subsystem showed a downward trend, while science and education subsystem showed no significant correlation. Moran I of infrastructure subsystem was mostly at the low level with a great fluctuation in different years. People's life had obvious spatial characteristics of high-high and low-low agglomeration. The global Moran I of resources and environment showed a pattern of "V" distribution. Economic development and science and education were the two factors most closely related to regional coordinated development.

Chondroprotective Effects of Gubitong Recipe via Inhibiting Excessive Mitophagy of Chondrocytes.

Objective: Osteoarthritis (OA) is the most common degenerative joint disorder and a leading cause of disability. A previous randomized controlled trial has shown that Gubitong (GBT) recipe can improve OA-related symptoms and articular function without noticeable side effects. However, the underlying mechanisms remain unclear. This study aims to explore the therapeutic mechanisms of the GBT recipe for OA through in vivo and in vitro experiments. Methods: Rats of the OA model were established by Hulth surgery and intervened with the GBT recipe and then were subjected to pathological assessment of the cartilage. Matrix metalloproteinase 13 (MMP-13) expression in cartilage tissues was assessed by immunohistochemical staining. Chondrocytes were isolated from sucking rats and stimulated with LPS to establish an in vitro model. After intervened by water extraction of the GBT recipe, the fluorescent signal of Mtphagy Dye and mitochondrial membrane potential (Δψm) were detected to determine the states of mitophagy and mitochondrial dynamics of chondrocytes in vitro, respectively. Western blot test was used to detect levels of proteins related to catabolism of the cartilage matrix, mitophagy, and PI3K/AKT pathway. Results: In in vivo experiments, the GBT recipe can effectively inhibit the cartilage degeneration of chondrocytes in OA rats, as well as markedly suppress the expression of MMP-13. In vitro experiments on LPS-induced chondrocytes exhibited increase in mitochondrial depolarization and excessive mitophagy, and the GBT recipe can alleviate these changes. LPS-stimulated chondrocytes showed increases in MMP-13, PINK1, and Parkin in cell lysates and LC3II/LC3I ratio in the mitochondrial fraction, and the GBT recipe can inhibit these increases in a dose-dependent manner. Moreover, the GBT recipe can attenuate the abnormal activation of PI3K/AKT pathway induced by LPS. Conclusion: The GBT recipe exhibits chondroprotective effects through inhibiting excessive mitophagy of chondrocytes, which may be associated with its inhibitory effect on the abnormal activation of PI3K/AKT pathway.

Short-form adaptive measure of financial toxicity from the Economic Strain and Resilience in Cancer (ENRICh) study: Derivation using modern psychometric techniques.

OBJECTIVES: This study sought to evaluate advanced psychometric properties of the 15-item Economic Strain and Resilience in Cancer (ENRICh) measure of financial toxicity for cancer patients. METHODS: We surveyed 515 cancer patients in the greater Houston metropolitan area using ENRICh from March 2019 to March 2020. We conducted a series of factor analyses alongside parametric and non-parametric item response theory (IRT) assessments using Mokken analysis and the graded response model (GRM). We utilized parameters derived from the GRM to run a simulated computerized adaptive test (CAT) assessment. RESULTS: Among participants, mean age was 58.49 years and 278 (54%) were female. The initial round factor analysis results suggested a one-factor scale structure. Negligible levels of differential item functioning (DIF) were evident between eight items. Three items were removed due to local interdependence (Q3>+0.4). The original 11-point numerical rating scale did not function well, and a new 3-point scoring system was implemented. The final 12-item ENRICh had acceptable fit to the GRM (p<0.001; TLI = 0.94; CFI = 0.95; RMSEA = 0.09; RMSR = 0.06) as well as good scalability and dimensionality. We observed high correlation between CAT version scores and the 12-item measure (r = 0.98). During CAT, items 2 (money you owe) and 4 (stress level about finances) were most frequently administered, followed by items 1 (money in savings) and 5 (ability to pay bills). Scores from these four items alone were strongly correlated with that of the 12-item ENRICh (r = 0.96). CONCLUSION: These CAT and 4-item versions provide options for quick screening in clinical practice and low-burden assessment in research.

Artificial intelligence: Emerging player in the diagnosis and treatment of digestive disease.

Given the breakthroughs in key technologies, such as image recognition, deep learning and neural networks, artificial intelligence (AI) continues to be increasingly developed, leading to closer and deeper integration with an increasingly data-, knowledge- and brain labor-intensive medical industry. As society continues to advance and individuals become more aware of their health needs, the problems associated with the aging of the population are receiving increasing attention, and there is an urgent demand for improving medical technology, prolonging human life and enhancing health. Digestive system diseases are the most common clinical diseases and are characterized by complex clinical manifestations and a general lack of obvious symptoms in the early stage. Such diseases are very difficult to diagnose and treat. In recent years, the incidence of diseases of the digestive system has increased. As AI applications in the field of health care continue to be developed, AI has begun playing an important role in the diagnosis and treatment of diseases of the digestive system. In this paper, the application of AI in assisted diagnosis and the application and prospects of AI in malignant and benign digestive system diseases are reviewed.

Vagus nerve stimulated by microbiota-derived hydrogen sulfide mediates the regulation of berberine on microglia in transient middle cerebral artery occlusion rats.

Amelioration of neuroinflammation via modulating microglia is a promising approach for cerebral ischemia therapy. The aim of the present study was to explore gut-brain axis signals in berberine-modulating microglia polarization following cerebral ischemia. The potential pathway was determined through analyzing the activation of the vagus nerve, hydrogen sulfide (H2 S) metabolism, and cysteine persulfides of transient receptor potential vanilloid 1 (TRPV1) receptor. The cerebral microenvironment feature was explored with a metabolomics assay. The data indicated that berberine ameliorated behavioral deficiency in transient middle cerebral artery occlusion rats through modulating microglia polarization and neuroinflammation depending on microbiota. Enhanced vagus nerve activity following berberine treatment was blocked by antibiotic cocktails, capsazepine, or sodium molybdate, respectively. Berberine-induced H2 S production was responsible for vagus nerve stimulation achieved through assimilatory and dissimilatory sulfate reduction with increased synthetic enzymes. Sulfation of the TRPV1 receptor resulted in vagus nerve activation and promoted the c-fos and ChAT in the nucleus tractus solitaries with berberine. Sphingolipid metabolism is the primary metabolic characteristic with berberine in the cerebral cortex, hippocampus, and cerebral spinal fluid disrupted by antibiotics. Berberine, in conclusion, modulates microglia polarization in a microbiota-dependent manner. H2 S stimulates the vagus nerve through TRPV1 is responsible for the berberine-induced gut-brain axis signal transmission. Sphingolipid metabolism might mediate the neuroinflammation amelioration following vagus afferent fiber activation.

Functionalized quinolizinium-based fluorescent reagents for modification of cysteine-containing peptides and proteins.

A series of quinolizinium-based fluorescent reagents were prepared by visible light-mediated gold-catalyzed cis-difunctionalization between quinolinium diazonium salts and electron-deficient alkyne-linked phenylethynyl trimethylsilanes. The electron-deficient alkynyl group of the quinolizinium-based fluorescent reagents underwent nucleophilic addition reaction with the sulfhydryl group on cysteine-containing peptides and proteins. The quinolizinium-based fluorescent reagents were found to function as highly selective reagents for the modification of cysteine-containing peptides and proteins with good to excellent conversions (up to 99%). Moreover, the modified BCArg mutants bearing cationic quinolizinium compounds 1b, 1d, 1e and 1h exhibit comparable activity in enzymatic and cytotoxicity assays to the unmodified one.

Machine Learning-Based Short-Term Mortality Prediction Models for Patients With Cancer Using Electronic Health Record Data: Systematic Review and Critical Appraisal.

BACKGROUND: In the United States, national guidelines suggest that aggressive cancer care should be avoided in the final months of life. However, guideline compliance currently requires clinicians to make judgments based on their experience as to when a patient is nearing the end of their life. Machine learning (ML) algorithms may facilitate improved end-of-life care provision for patients with cancer by identifying patients at risk of short-term mortality. OBJECTIVE: This study aims to summarize the evidence for applying ML in ≤1-year cancer mortality prediction to assist with the transition to end-of-life care for patients with cancer. METHODS: We searched MEDLINE, Embase, Scopus, Web of Science, and IEEE to identify relevant articles. We included studies describing ML algorithms predicting ≤1-year mortality in patients of oncology. We used the prediction model risk of bias assessment tool to assess the quality of the included studies. RESULTS: We included 15 articles involving 110,058 patients in the final synthesis. Of the 15 studies, 12 (80%) had a high or unclear risk of bias. The model performance was good: the area under the receiver operating characteristic curve ranged from 0.72 to 0.92. We identified common issues leading to biased models, including using a single performance metric, incomplete reporting of or inappropriate modeling practice, and small sample size. CONCLUSIONS: We found encouraging signs of ML performance in predicting short-term cancer mortality. Nevertheless, no included ML algorithms are suitable for clinical practice at the current stage because of the high risk of bias and uncertainty regarding real-world performance. Further research is needed to develop ML models using the modern standards of algorithm development and reporting.