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Background: The incidence of allergic disease remains high, and many studies have focused on the association between food diversity in infancy and allergic disease later in life, but their conclusions are still controversial. Objective: We aimed to synthesize the literature on the association between childhood diet diversity and atopic disease. Methods: We searched the PubMed, Cochrane Library, Embase, Scopus, VPCS, and Wanfang databases for studies about food diversity and atopic disease. Seventeen high-quality studies, 14 cohort studies, and 1 case–control study were included from 5244 studies with sample sizes ranging from 100 to 5225. Results: All high-quality cohort studies showed that increasing food diversity in infancy can effectively prevent the occurrence of food allergies (5/5). Moderate evidence showed that increased food diversity reduced the risk of asthma (4/6), food sensitization (3/5), and atopic dermatitis (3/5). However, its effect on eczema (5), allergic rhinitis (4), and other diseases remains controversial. Conclusions: Increasing food diversity during infancy is a potential method for preventing food allergy, asthma, atopic dermatitis, and food sensitization later in life. There is little or no comparative evidence about the protective effect of food diversity on other atopic diseases.

Chronic urticaria (CU) is a debilitating skin disease that lasts for more than 6 weeks with wheals and/or angioedema, including chronic spontaneous urticaria (CSU) and chronic inducible urticaria (CIndU). In China, the prevalence of this disease is high, more than 1%, and on the rise. CU has a major impact on the quality of life (QoL) of patients who frequently experience sleep disturbance, depression, and anxiety. Nearly one-third of patients with CSU, in China, are resistant to second-generation H1-antihistamines (sgAHs), even at a fourfold dose (second line; off-label). Omalizumab is approved for the treatment of CSU treatment in Europe and shows remarkable efficacy and safety. In China, regulatory approval for the use of omalizumab is pending, and its use in clinical practice varies widely. Consensus on omalizumab CU treatment in China is urgently needed. The aim of this article is to propose a practical omalizumab treatment algorithm for the management of antihistamine-resistant CSU and CIndU in adults and special population including children and adolescents, and pregnant or breast feeding women, to guide daily clinical practice in China. In the development of this consensus, an expert group including mainly dermatologists, allergists, but also pulmonologists, ENTs, immunologists, and pediatricians in Allergic Disease Prevention and Control Committee, Chinese Preventive Medicine Association, reviewed the existing evidence and developed consensus on the use of omalizumab in CU patients from China. The goal of this consensus is to assist clinicians in making rational decisions in the management of refractory CU with omalizumab. The key clinical questions covered by the treatment algorithm are: 1) Omalizumab treatment routine strategy in both CSU and CIndU patients; 2) Recommended dose and treatment duration for different age stratification; 3) Treatment duration for CU patients with other allergic comorbidities; 4) Recommendation on omalizumab stopping strategy.

Objective: To collect the clinical, pathological, and computed tomography (CT) data of 143 accepted surgical cases of pancreatic body tail cancer (PBTC) and to model and predict its prognosis. Methods: The clinical, pathological, and CT data of 143 PBTC patients who underwent surgical resection or endoscopic ultrasound biopsy and were pathologically diagnosed in Xiangyang No.1 People's Hospital Hospital from December 2012 to December 2022 were retrospectively analyzed. The Kaplan-Meier method was adopted to make survival curves based on the 1 to 5 years’ follow-up data, and then the log-rank was employed to analyze the survival. According to the median survival of 6 months, the PBTC patients were divided into a group with a good prognosis (survival time ≥ 6 months) and a group with a poor prognosis (survival time

Objective: To establish a predictive model distinguishing focal mass-forming pancreatitis (FMFP) from pancreatic ductal adenocarcinoma (PDAC) based on computed tomography (CT) radiomics and clinical data. Methods: A total of 78 FMFP patients (FMFP group) and 120 PDAC patients (PDAC group) who were admitted to Xiangyang No.1 People's Hospital and Xiangyang Central Hospital from February 2012 to May 2021 and were pathologically diagnosed were included in this study, and were input to set up the training set and test set at a ratio of 7:3. The 3Dslicer software was used to extract the radiomic features and radiomic scores (Radscores) of the 2 groups, and the clinical data (age, gender, etc), CT imaging features (lesion location, size, enhancement degree, vascular wrapping, etc) and CT radiomic features of the 2 groups were compared. Logistic regression was used to screen the independent risk factors of the 2 groups, and multiple prediction models (clinical imaging model, radiomics model, and combined model) were established. Then the receiver operating characteristic (ROC) analysis and decision curve analysis (DCA) were conducted to compare the prediction performance and net benefit of the models. Results: The multivariate logistic regression results indicated that dilation of the main pancreatic duct, vascular wrapping, Radscore1 and Radscore2 were independent influencing factors for distinguishing FMFP from PDAC. In the training set, the combined model showed the best predictive performance (area under the ROC curve [AUC] 0.857, 95% CI [0.787-0.910]), significantly higher than the clinical imaging model (AUC 0.650, 95% CI [0.565–0.729]) and the radiomics model (AUC 0.812, 95% CI [0.759–0.890]). DCA confirmed that the combined model had the highest net benefit. These results were further validated by the test set. Conclusion: The combined model based on clinical–CT radiomics data can effectively identify FMFP and PDAC, providing a reference for clinical decision-making.

Objective To build a combined model that integrates clinical data, contrast-enhanced ultrasound, and magnetic resonance perfusion-weighted imaging-based radiomics for predicting the possibility of biochemical recurrence of prostate carcinoma and develop a nomogram tool. Method We retrospectively analyzed the clinical, ultrasound, and magnetic resonance imaging data of 206 patients pathologically confirmed with prostate carcinoma and receiving radical prostatectomy at Xiangyang No. 1 People’s Hospital from February 2015 to August 2021. Based on one to 7 years of follow-up (prostate specific antigen [PSA] level≥0.2 ng/mL, indicative of prostate carcinoma–biochemical recurrence), the patients were divided into biochemical recurrence group (n = 77) and normal group (n = 129). The training and testing sets were formed by dividing the patients at a 7:3 ratio. In training set, The magnetic resonance perfusion-weighted imaging–based radiomics radscore was generated using lasso regression. Several predictive models were built based on the patients’ clinical imaging data. The predictive efficacy (area under the curve) of these models was compared using the MedCalc software. The decision curve analysis was conducted using the R to compare the net benefit. Finally, an external validation was carried out on the testing set, and the nomogram tool was developed for predicting prostate carcinoma–biochemical recurrence. Result The univariate analysis confirmed that Tumor diameter, tumor node metastasis classification stage of tumor, lymph node metastasis or distance metastasis, Gleason grade, preoperative PSA, ultrasound (peak intensity, arrival time, and elastography grade), and magnetic resonance imaging-radscore1/2 were predictors of prostate carcinoma–biochemical recurrence. On the training set, the combined model based on the above factors had the highest predictive efficacy for prostate carcinoma–biochemical recurrence (area under the curve: 0.91; odds ratio 0.02, 95% confidence interval: 0.85-0.95). The predictive performance of the combined model was significantly higher than that of the model based on general clinical data (area under the curve: 0.74; odds ratio 0.04, 95% confidence interval: 0.67-0.81, P < .05), contrast-enhanced ultrasound (area under the curve: 0.61; odds ratio 0.05 95% confidence interval: 0.53-0.69, P < .05), and the magnetic resonance imaging–based radiomics model (area under the curve: 0.85; odds ratio 0.03, 95% confidence interval: 0.78-0.91, P = .01). The decision curve analysis also indicated the maximum net benefit derived from the combined model, which agreed with the validation results on the testing set. The nomogram tool developed based on the combined model achieved a good performance in clinical applications. Conclusion The magnetic resonance imaging texture parameters extracted by magnetic resonance perfusion-weighted imaging Lasso regression could help increase the accuracy of the predictive model. The combined model and the nomogram tool provide support for the clinical screening of the populations at a risk for biochemical recurrence.