Epidemiology, etiology, and prevention of esophageal squamous cell carcinoma in China

Background: Esophageal squamous cell carcinoma (ESCC) is one of the most common malignancies with poor diagnosis. Esophageal squamous dysplasia (ESD) is considered as an immediate precancerous lesion of ESCC. Lack of biomarkers for discriminating ESCC and ESD from healthy subjects limits the early diagnosis and treatment of ESCC. Therefore, a serum metabolomic strategy was conducted to identify and validate potential metabolic markers for the screening of ESCC and ESD subjects. Methods: A total of 74 patients with ESCC, 72 patients with ESD, and 75 normal control (NC) subjects were enrolled in this study. Gas chromatography-mass spectrometry was used to acquire serum metabolic profiles. Pathway analysis was conducted to uncover the fluctuated metabolic pathways during ESCC. Multivariate analyses were used to screen and validate the biomarkers. Results: ESCC, ESD, and NC subjects revealed progressively altered metabolic profiles, in which amino acids globally increased, while fatty acids decreased in ESCCs compared with the control groups. Pathway analysis demonstrated the activated biosynthesis of amino acids and inhibited desaturation of saturated fatty acids. The panel constructed with propanoic acid, linoleic acid, glycerol-3-phosphate, and l-glutamine showed the area under the curve (AUC), sensitivity, and specificity of 0.817, 0.75, and 0.74, respectively, in the discrimination of ESCC/ESD patients from NC subjects. The panel constructed by propanoic acid, l-leucine, and hydroxyproline revealed the AUC, sensitivity, and specificity of 0.819, 0.76, and 0.72, respectively, in the discrimination of ESD from NC subjects. The combination of hypoxanthine, 2-ketoisocaproic acid, l-glutamate, and l-aspartate showed the AUC, sensitivity, and specificity of 0.818, 0.83, and 0.74, respectively, in the discrimination of ESCC patients from ESD subjects. Conclusions: Our study revealed the systematic landscape for metabolic alterations in sera of ESD and ESCC patients. The defined metabolite markers showed reasonable performance in the discrimination of ESCC and ESD patients, and may provide helpful reference for clinicians and biologists.

The optimal treatment regimen for breast cancer patients with gBRCA mutations remains controversial given the availability of numerous options, such as platinum-based agents, polymerase inhibitors (PARPis), and other agents. We included phase II or III RCTs and estimated the HR with 95% CI for OS, PFS, and DFS, in addition to the OR with 95% CI for ORR and pCR. We determined the treatment arm rankings by P-scores. Furthermore, we carried out a subgroup analysis in TNBC and HR-positive patients. We conducted this network meta-analysis using R 4.2.0 and a random-effects model. A total of 22 RCTs were eligible, involving 4253 patients. In the pairwise comparisons, PARPi + Platinum + Chemo was better than PARPi + Chemo for OS (in whole study group and in both subgroups) as well as PFS. The ranking tests demonstrated that PARPi + Platinum + Chemo ranked first in PFS, DFS, and ORR. Platinum + Chemo showed higher OS than PARPi + Chemo. The ranking tests for PFS, DFS, and pCR indicated that, except for the best treatment (PARPi + Platinum + Chemo) containing PARPi, the second and third treatments were platinum monotherapy or platinum-based chemotherapy. In conclusion, PARPi + Platinum + Chemo might be the best regime for gBRCA-mutated BC. Platinum drugs showed more favorable efficacy than PARPi in both combination and monotherapy.

Background: This prospective study investigated the association between tooth loss and upper gastrointestinal (UGI) cancer mortality in the Linxian Dysplasia Nutrition Intervention Trial Cohort. Methods: Subjects were categorized into three groups according to age at baseline. No missing teeth and less or greater than median tooth loss in each group was defined as none, moderate, and severe, respectively. Hazard ratios (HRs) and 95% confidence intervals (95% CIs) were estimated using the Cox proportional hazard model. Results: Through 30 September 2015, 541 esophageal squamous cell carcinoma (ESCC), 284 gastric cardia carcinoma (GCC), and 77 gastric non‐cardia carcinoma (GNCC) deaths occurred. In the six‐year follow‐up, severe tooth loss was associated with an increased risk of GCC mortality (HR 1.55, 95% CI 1.06–2.18). In the 15‐year follow‐up, moderate tooth loss increased the ESCC mortality risk by 58% (HR 1.58, 95% CI 1.06–2.35), while severe loss increased the GCC mortality risk by 30% (HR 1.30, 95% CI 1.03–1.64). In the 30‐year follow‐up, moderate tooth loss increased the risk of ESCC mortality (HR 1.34, 95% CI 1.01–1.76). In subjects aged < 55 at baseline and men, moderate tooth loss had 53% and 52% higher risks of ESCC mortality (HR _{<55 years} 1.53, 95% CI 1.06–2.05; HRmen 1.52, 95% CI 1.01–2.28). No significant association was observed for GNCC in any subjects or subgroups. Conclusion: Moderate tooth loss increased the risk of ESCC mortality, particularly in younger subjects and men. Severe tooth loss increased the risk of GCC mortality. Future studies are needed to confirm these findings.

To explore the optimal cutoff score for initial detection of Alzheimer’s Disease (AD) through the Chinese version of Mini-Mental State Examination (CMMSE) in rural areas in China, we conducted a cross-sectional study within the Linxian General Population Nutritional Follow-up study. 16,488 eligible cohort members participated in the survey and 881 completed the CMMSE. Among 881 participants, the median age (Interquartile range) was 69.00 (10.00), 634 (71.92%) were female, 657 (74.57%) were illiterate, 35 (3.97%) had 6 years of education or higher, and 295 (33.48%) were diagnosed with AD. By reducing the CMMSE criteria for illiterate to 16 points, primary school to 19 points, and middle school or higher to 23 points, the efficiency of Chinese version of Mini-Mental State Examination can be significantly improved for initial detection of AD in rural areas in China, especially in those nutrition deficient areas.

This study aims to evaluate deep learning (DL) performance in differentiating low- and high-grade glioma. Search online database for studies continuously published from 1st January 2015 until 16th August 2022. The random-effects model was used for synthesis, based on pooled sensitivity (SE), specificity (SP), and area under the curve (AUC). Heterogeneity was estimated using the Higgins inconsistency index (I²). 33 were ultimately included in the meta-analysis. The overall pooled SE and SP were 94% and 93%, with an AUC of 0.98. There was great heterogeneity in this field. Our evidence-based study shows DL achieves high accuracy in glioma grading. Subgroup analysis reveals several limitations in this field: 1) Diagnostic trials require standard method for data merging for AI; 2) small sample size; 3) poor-quality image preprocessing; 4) not standard algorithm development; 5) not standard data report; 6) different definition of HGG and LGG; and 7) poor extrapolation.