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Colorectal cancer (CRC) is a frequent gastrointestinal malignancy with high rates of morbidity and mortality; 85% of these tumours are proficient mismatch repair (pMMR)‐microsatellite instability‐low (MSI‐L)/microsatellite stable (MSS) CRC known as 'cold' tumours that are resistant to immunosuppressive drugs. Monotherapy with programmed death 1 (PD‐1)/programmed death ligand 1 (PD‐L1) inhibitors is ineffective for treating MSS CRC, making immunotherapy for MSS CRC a bottleneck. Recent studies have found that the multi‐pathway regimens combined with PD‐1/PD‐L1 inhibitors can enhance the efficacy of anti‐PD‐1/PD‐L1 in MSS CRC by increasing the number of CD8+ T cells, upregulating PD‐L1 expression and improving the tumour microenvironment. This paper reviews the research progress of PD‐1/PD‐L1 inhibitors in combination with cytotoxic T‐lymphocyte–associated antigen 4 (CTLA‐4) inhibitors, oncolytic virus, intestinal flora, antiangiogenic agents, chemotherapy, radiotherapy and epigenetic drugs for the treatment of pMMR‐MSI‐L/MSS CRC. [ABSTRACT FROM AUTHOR]

Aim: To investigate the associations between ketone bodies (KB) and multiple adverse outcomes including cardiovascular disease (CVD), chronic kidney disease (CKD) and all‐cause mortality according to diabetes status. Methods: This prospective study included 222 824 participants free from CVD and CKD at baseline from the UK Biobank. Total KB including β‐hydroxybutyrate, acetoacetate and acetone were measured by nuclear magnetic resonance. Cox proportional hazards models were used to estimate hazard ratios (HRs) and 95% confidence intervals (CIs) for the association between KB and adverse outcomes among participants with normoglycaemia, prediabetes and type 2 diabetes, respectively. Results: During a mean follow‐up of 14.1 years, 24 088 incident CVD events (including 17 303 coronary heart disease events, 5172 stroke events and 5881 heart failure [HF] events), 8605 CKD events and 15 813 deaths, were documented. Higher total KB significantly increased the risk of HF among participants with normoglycaemia (HR, 1.32 [95% CI, 1.17‐1.49], per 10‐fold increase in total KB) and prediabetes (1.35 [1.04‐1.76]), and increased the risk of CKD among those with normoglycaemia (1.20 [1.09‐1.33]). Elevated KB levels were associated with an increased risk of all‐cause mortality across the glycaemic spectrum (1.32 [1.23‐1.42] for normoglycaemia, 1.45 [1.24‐1.71] for prediabetes and 1.47 [1.11‐1.94] for diabetes). Moreover, a significant additive interaction between KB and diabetes status was observed on the risk of death (P =.009), with 4.9% of deaths attributed to the interactive effects. Conclusions: Our study underscored the variation in association patterns between KB and adverse outcomes according to diabetes status and suggested that KB could interact with diabetes status in an additive manner to increase the risk of mortality. [ABSTRACT FROM AUTHOR]

Urgent detection of calculous pyonephrosis is crucial for surgical planning and preventing severe outcomes. This study aims to evaluate the performance of computed tomography (CT)-based radiomics and a three-dimensional convolutional neural network (3D-CNN) model, integrated with independent clinical factors, to identify patients with calculous pyonephrosis. We recruited 182 patients receiving either percutaneous nephrostomy tube placement or percutaneous nephrolithotomy for calculous hydronephrosis or pyonephrosis. The regions of interest were manually delineated on plain CT images and the CT attenuation value (HU) was measured. Radiomics analysis was performed using least absolute shrinkage and selection operator (LASSO). A 3D-CNN model was also developed. The better-performing machine-learning model was combined with independent clinical factors to build a comprehensive clinical machine-learning model. The performance of these models was assessed using receiver operating characteristic analysis and decision curve analysis. Fever, blood neutrophils, and urine leukocytes were independent risk factors for pyonephrosis. The radiomics model showed higher area under the curve (AUC) than the 3D-CNN model and HU (0.876 vs. 0.599, 0.578; p = 0.003, 0.002) in the testing cohort. The clinical machine-learning model surpassed the clinical model in both the training (0.975 vs. 0.904, p = 0.019) and testing (0.967 vs. 0.889, p = 0.045) cohorts. [ABSTRACT FROM AUTHOR]

Aim: To explore the relationship between proinflammatory diet, habitual salt intake and the onset of type 2 diabetes. Methods: This prospective study was conducted among 171 094 UK Biobank participants who completed at least one 24‐h dietary questionnaire and were free of diabetes at baseline. Participants were followed up until 1 March 2023 for type 2 diabetes incidence, with diagnosis information obtained from linked medical records. An Energy‐adjusted Diet Inflammatory Index (E‐DII) was calculated based on 28 food parameters. Habitual salt intake was determined through the self‐reported frequency of adding salt to foods. The associations between E‐DII, habitual salt intake and type 2 diabetes incidence were tested by the Cox proportional hazard regression model. Results: Over a median follow‐up period of 13.5 years, 6216 cases of type 2 diabetes were documented. Compared with participants with a low E‐DII (indicative of an anti‐inflammatory diet), participants with a high E‐DII (indicative of a proinflammatory diet) had an 18% heightened risk of developing type 2 diabetes. The association between E‐DII and type 2 diabetes tends to be linear after adjustment for major confounders. Participants with a proinflammatory diet and always adding salt to foods had the highest risk of type 2 diabetes incidence (hazard ratio 1.60, 95% confidence interval 1.32‐1.94). Conclusions: Our findings indicate that a proinflammatory diet and higher habitual salt intake were associated with an increased risk of type 2 diabetes. These results support the public health promotion of an anti‐inflammatory diet and reducing salt intake to prevent the onset of type 2 diabetes. [ABSTRACT FROM AUTHOR]

Background: We aimed to examine whether current and lifetime night shift work is associated with accelerated biological ageing and the potential role of body mass index (BMI) in mediating the association., Methods: Data were sourced from the UK Biobank cohort. This study included participants who reported detailed information on their current work schedule and had complete data to calculate PhenoAge. The outcome of interest was biological ageing, measured by PhenoAge acceleration. Multivariable linear regression models were conducted to test the relationship between night shift work and biological ageing. Mediation analyses were performed., Results: Of the 182 064 participants included, the mean age was 52.6 years, and 51.1% were male. After adjustment for chronological age and sex, compared with day workers, shift workers without night shift, irregular night shift workers and permanent night shift workers were associated with 0.59-, 0.87- and 1.30-year increase in biological ageing, respectively (P for trend <.001). Considering the lifetime work schedule, participants who worked night shifts >10 years and participants who worked >8 night shifts each month showed increased biological age acceleration [>10 years: β = 0.54, 95% confidence interval (CI) 0.29-0.79; >8 times/month: β = 0.29, 95% CI 0.07-0.50]. The mediation analysis showed that BMI mediated the associations between night shift work and biological age acceleration by 36%-53%., Conclusions: We showed that night shift work was associated with accelerated biological ageing. Our findings highlight the interventions on appropriate shift work schedules and weight management in night shift workers, which may slow the biological ageing process and ultimately reduce the burden of age-related diseases., (© The Author(s) 2024. Published by Oxford University Press on behalf of the British Geriatrics Society. All rights reserved. For permissions, please email: journals.permissions@oup.com.)

Background: Identification of individuals with prediabetes who are at high risk of developing diabetes allows for precise interventions. We aimed to determine the role of nuclear magnetic resonance (NMR)-based metabolomic signature in predicting the progression from prediabetes to diabetes., Methods: This prospective study included 13,489 participants with prediabetes who had metabolomic data from the UK Biobank. Circulating metabolites were quantified via NMR spectroscopy. Cox proportional hazard (CPH) models were performed to estimate the associations between metabolites and diabetes risk. Supporting vector machine, random forest, and extreme gradient boosting were used to select the optimal metabolite panel for prediction. CPH and random survival forest (RSF) models were utilized to validate the predictive ability of the metabolites., Results: During a median follow-up of 13.6 years, 2525 participants developed diabetes. After adjusting for covariates, 94 of 168 metabolites were associated with risk of progression to diabetes. A panel of nine metabolites, selected by all three machine-learning algorithms, was found to significantly improve diabetes risk prediction beyond conventional risk factors in the CPH model (area under the receiver-operating characteristic curve, 1 year: 0.823 for risk factors + metabolites vs 0.759 for risk factors, 5 years: 0.830 vs 0.798, 10 years: 0.801 vs 0.776, all p < 0.05). Similar results were observed from the RSF model. Categorization of participants according to the predicted value thresholds revealed distinct cumulative risk of diabetes., Conclusions: Our study lends support for use of the metabolite markers to help determine individuals with prediabetes who are at high risk of progressing to diabetes and inform targeted and efficient interventions., Funding: Shanghai Municipal Health Commission (2022XD017). Innovative Research Team of High-level Local Universities in Shanghai (SHSMU-ZDCX20212501). Shanghai Municipal Human Resources and Social Security Bureau (2020074). Clinical Research Plan of Shanghai Hospital Development Center (SHDC2020CR4006). Science and Technology Commission of Shanghai Municipality (22015810500)., Competing Interests: JL, YY, YS, YF, WS, LC, XT, YC, NW, YL, BW No competing interests declared, (© 2024, Li, Yu et al.)

Objective: To evaluate the applicability of artificial intelligence-assisted compressed sensing (ACS) to anal fistula magnetic resonance imaging (MRI)., Methods: 51 patients were included in this study and underwent T2-weighted sequence of MRI examinations both with ACS and without ACS technology in a 3.0 T MR scanner. Subjective image quality scores, and objective image quality-related metrics including scanning time, signal-to-noise ratio (SNR), and contrast-to-noise ratio (CNR), were evaluated and statistically compared between the images collected with and without ACS., Results: No significant difference in the subjective image quality of lesion conspicuity was observed between the two groups. However, ACS MRI decreased the acquisition time with regard to control group (74.00 s vs. 156.00 s). Besides, SNR of perianal and muscle in the ACS group was significantly higher than that of the control group (164.07 ± 33.35 vs 130.81 ± 29.10, p < 0.001; 109.87 ± 22.01 vs 87.61 ± 17.95, p < 0.001; respectively). The CNR was significantly higher in the ACS group than in the control group (54.02 ± 23.98 vs 43.20 ± 21.00; p < 0.001). Moreover, the accuracy rate of the ACS groups in evaluating the direction and internal opening of the fistula was 88.89 %, exactly the same as that of the control group., Conclusion: We demonstrated the applicability of using ACS to accelerate MR of anal fistulas with improved SNR and CNR. Meanwhile, the accuracy rates of the ACS group and the control were equivalent in evaluating the direction and internal opening of the fistula, based on the results of surgical exploration., Competing Interests: The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (© 2023 The Authors.)