Ningbo First Hospital

BACKGROUND: Recent studies have focused on initial clinical and epidemiological characteristics of the coronavirus disease 2019 (COVID-19), which is the mainly revealing situation in Wuhan, Hubei. AIM: This study aims to reveal more data on the epidemiological and clinical characteristics of COVID-19 patients outside of Wuhan, Zhejiang, China. DESIGN: This study was a retrospective case series. METHODS: Eighty-eight cases of laboratory-confirmed and three cases of clinically confirmed COVID-19 were admitted to five hospitals in Zhejiang province, China. Data were collected from 20 January 2020 to 11 February 2020. RESULTS AND DISCUSSION: Of all 91 patients, 88 (96.70%) were laboratory-confirmed COVID-19 with throat swab samples that tested positive for SARS-Cov-2, three (3.30%) cases were clinically diagnosed. The median age of the patients was 50 (36.5-57) years, and female accounted for 59.34%. In this sample, 40 (43.96%) patients had contracted the disease from local cases, 31 (34.07%) patients had been to Wuhan/Hubei, eight (8.79%) patients had contacted with people from Wuhan, and 11 (12.09%) patients were diagnosed after having flown together in the same flight with no passenger that could later be identified as the source of infection. In particular within the city of Ningbo, 60.52% cases can be traced back to an event held in a temple. The most common symptoms were fever (71.43%), cough (60.44%) and fatigue (43.96%). The median of incubation period was 6 (interquartile range 3-8) days and the median time from the first visit to a doctor to the confirmed diagnosis was 1 (1-2) days. According to the chest computed tomography scans, 67.03% cases had bilateral pneumonia. CONCLUSIONS: Social activity cluster, family cluster and flying alongside with persons already infected with COVID-19 were how people got infected with COVID-19 in Zhejiang.

Importance: Gastric and gastroesophageal junction cancers are diagnosed in more than 1 million people worldwide annually, and few effective treatments are available. Sintilimab, a recombinant human IgG4 monoclonal antibody that binds to programmed cell death 1 (PD-1), in combination with chemotherapy, has demonstrated promising efficacy. Objective: To compare overall survival of patients with unresectable locally advanced or metastatic gastric or gastroesophageal junction cancers who were treated with sintilimab with chemotherapy vs placebo with chemotherapy. Also compared were a subset of patients with a PD ligand 1 (PD-L1) combined positive score (CPS) of 5 or more (range, 1-100). Design, Setting, and Participants: Randomized, double-blind, placebo-controlled, phase 3 clinical trial conducted at 62 hospitals in China that enrolled 650 patients with unresectable locally advanced or metastatic gastric or gastroesophageal junction adenocarcinoma between January 3, 2019, and August 5, 2020. Final follow-up occurred on June 20, 2021. Interventions: Patients were randomized 1:1 to either sintilimab (n = 327) or placebo (n = 323) combined with capecitabine and oxaliplatin (the XELOX regimen) every 3 weeks for a maximum of 6 cycles. Maintenance therapy with sintilimab or placebo plus capecitabine continued for up to 2 years. Main Outcomes and Measures: The primary end point was overall survival time from randomization. Results: Of the 650 patients (mean age, 59 years; 483 [74.3%] men), 327 were randomized to sintilimab plus chemotherapy and 323 to placebo plus chemotherapy. Among the randomized patients, 397 (61.1%) had tumors with a PD-L1 CPS of 5 or more; 563 (86.6%) discontinued study treatment and 388 (59.7%) died; 1 patient (<0.1%) was lost to follow-up. Among all randomized patients, sintilimab improved overall survival compared with placebo (median, 15.2 vs 12.3 months; stratified hazard ratio [HR], 0.77 [95% CI, 0.63-0.94]; P = .009). Among patients with a CPS of 5 or more, sintilimab improved overall survival compared with placebo (median, 18.4 vs 12.9 months; HR, 0.66 [95% CI, 0.50-0.86]; P = .002). The most common grade 3 or higher treatment-related adverse events were decreased platelet count (sintilimab, 24.7% vs placebo, 21.3%), decreased neutrophil count (sintilimab, 20.1% vs placebo, 18.8%), and anemia (sintilimab, 12.5% vs placebo, 8.8%). Conclusions and Relevance: Among patients with unresectable locally advanced or metastatic gastric and gastroesophageal junction adenocarcinoma treated with first-line chemotherapy, sintilimab significantly improved overall survival for all patients and for patients with a CPS of 5 or more compared with placebo. Trial Registration: ClinicalTrials.gov Identifier: NCT03745170.

FOXO1A and FOXO3A are two members of the FoxO family. FOXO3A has recently been linked to human longevity in Japanese, German and Italian populations. Here we tested the genetic contribution of FOXO1A and FOXO3A to the longevity phenotype in Han Chinese population. Six tagging SNPs from FOXO1A and FOXO3A were selected and genotyped in 1817 centenarians and younger individuals. Two SNPs of FOXO1A were found to be associated with longevity in women (P = 0.01-0.005), whereas all three SNPs of FOXO3A were associated with longevity in both genders (P = 0.005-0.001). One SNP from FOXO1A was found not to be associated with longevity. In haplotype association tests, the OR (95% CI) for haplotypes TTG and CCG of FOXO1A in association with female longevity were 0.72 (0.58-0.90) and 1.38 (1.08-1.76), P = 0.0033 and 0.0063, respectively. The haplotypes of FOXO3A were associated with longevity in men [GTC: OR (95% CI) = 0.67 (0.51-0.86), P = 0.0014; CGT: OR (95% CI) = 1.48 (1.12-1.94), P = 0.0035] and in women [GTC: OR (95% CI) = 0.75 (0.60-0.94), P = 0.0094; CGT: OR (95% CI) = 1.47 (1.16-1.86), P = 0.0009]. The haplotype association tests were validated by permutation analysis. The association of FOXO1A with female longevity was replicated in 700 centenarians and younger individuals that were sampled geographically different from the original population. Thus, we demonstrate that, unlike FOXO3A, FOXO1A is more closely associated with human female longevity, suggesting that the genetic contribution to longevity trait may be affected by genders.

We report a family cluster of coronavirus disease 2019 (COVID-19) caused by a presymptomatic case. There were 9 family members, including 8 laboratory-confirmed with COVID-19, and a 6-year-old child had no evidence of infection. Among the 8 patients, 1 adult and a 13-month-old infant were asymptomatic, and 1 adult was diagnosed as having severe pneumonia.

Gut microbiota plays an important role in the bidirectional communication between the gut and the central nervous system. Mounting evidences suggest that gut microbiota can influence the brain function via neuroimmune, neuroendocrine pathways and the nervous system. Advances in gene sequencing techniques further facilitate investigating the underlying relationship between gut microbiota and psychiatric disorders. In recent years, researchers have preliminarily explored the gut microbiota in patients with mood disorders. The current review aims to summarize the published human studies of gut microbiota in mood disorders. The findings showed the microbial diversity and taxonomic compositions were significantly changed compared with healthy individuals. Most of these findings revealed that short-chain fatty acids (SCFAs)-producing bacterial genera were decreased, while pro-inflammatory genera and those involved in lipid metabolism were increased in patients with depressive episodes. Interestingly, the abundance of Actinobacteria, Enterobacteriaceae was increased and Faecalibacterium was decreased consistently in patients with either bipolar disorder or major depressive disorder. Some studies further indicated that specific bacteria were associated with clinical characteristics, inflammatory profiles, metabolic markers and pharmacological treatment. These studies present preliminary evidence that the important role of gut microbiota in mood disorders through the brain-gut-microbiota axis, which emerges as a promising target for disease diagnosis and therapeutic interventions in the future.

Renal tubulointerstitial fibrosis was a crucial pathological feature of diabetic nephropathy (DN), and renal tubular injury might associate with abnormal mitophagy. In this study, we investigated the effects and molecular mechanisms of AMPK agonist metformin on mitophagy and cellular injury in renal tubular cell under diabetic condition. The high fat diet (HFD) and streptozotocin (STZ)-induced type 2 diabetic mice model and HK-2 cells were used in this study. Metformin was administered in the drinking water (200 mg/kg/d) for 24 weeks. Renal tubulointerstitial lesions, oxidative stress and some indicators of mitophagy (e.g., LC3II, Pink1, and Parkin) were examined both in renal tissue and HK-2 cells. Additionally, compound C (an AMPK inhibitor) and Pink1 siRNA were applied to explore the molecular regulation mechanism of metformin on mitophagy. We found that the expression of p-AMPK, Pink1, Parkin, LC3II, and Atg5 in renal tissue of diabetic mice was decreased obviously. Metformin reduced the levels of serum creatinine, urine protein, and attenuated renal oxidative injury and fibrosis in HFD/STZ induced diabetic mice. In addition, Metformin reversed mitophagy dysfunction and the over-expression of NLRP3. In vitro pretreatment of HK-2 cells with AMPK inhibitor compound C or Pink1 siRNA negated the beneficial effects of metformin. Furthermore, we noted that metformin activated p-AMPK and promoted the translocation of Pink1 from the cytoplasm to mitochondria, then promoted the occurrence of mitophagy in HK-2 cells under HG/HFA ambience. Our results suggested for the first time that AMPK agonist metformin ameliorated renal oxidative stress and tubulointerstitial fibrosis in HFD/STZ-induced diabetic mice via activating mitophagy through a p-AMPK-Pink1-Parkin pathway.

BACKGROUND AND AIMS: Early identification of modifiable risk factors is essential for the prevention of nonalcoholic fatty liver disease (NAFLD). We aimed to systematically explore the relationships between genetically predicted modifiable risk factors and NAFLD. APPROACH AND RESULTS: We applied univariable and multivariable Mendelian randomization analyses to explore the relationships between 35 modifiable risk factors and NAFLD. We also evaluated the combined results in three independent large genome-wide association studies. Genetically predicted alcohol frequency, elevated serum levels of liver enzymes, triglycerides, C-reactive protein, and obesity traits, including body mass index, waist circumference, and body fat mass, were associated with increased risks of NAFLD (all with p < 0.05). Poor physical condition had a suggestive increased risk for NAFLD (odds ratio [OR] = 2.63, p = 0.042). Genetically instrumented type 2 diabetes (T2DM), hypothyroidism, and hypertension all increased the risk for NAFLD, and the ORs (95% confidence interval) were 1.508 (1.20-1.90), 13.08 (1.53-111.65), and 3.11 (1.33-7.31) for a 1-U increase in log-transformed odds, respectively. The positive associations of T2DM and hypertension with NAFLD remained significant in multivariable analyses. The combined results from the discovery and two replication datasets further confirmed that alcohol frequency, elevated serum liver enzymes, poor physical condition, obesity traits, T2DM, and hypertension significantly increase the risk of NAFLD, whereas higher education and high-density lipoprotein cholesterol (HDL-cholesterol) could lower NAFLD risk. CONCLUSIONS: Genetically predicted alcohol frequency, elevated serum liver enzymes, poor physical condition, obesity traits, T2DM, and hypertension were associated with an increased risk of NAFLD, whereas higher education and HDL-cholesterol were associated with a decreased risk of NAFLD.

Purine metabolism in the circulatory system yields uric acid as its final oxidation product, which is believed to be linked to the development of gout and kidney stones. Hyperuricemia is closely correlated with cardiovascular disease, metabolic syndrome, and chronic kidney disease, as attested by the epidemiological and empirical research. In this review, we summarize the recent knowledge about hyperuricemia, with a special focus on its physiology, epidemiology, and correlation with cardiovascular disease. This review also discusses the possible positive effects of treatment to reduce urate levels in patients with cardiovascular disease and hyperuricemia, which may lead to an improved clinical treatment plan.

BACKGROUND: Use of artificial intelligence to identify dermoscopic images has brought major breakthroughs in recent years to the early diagnosis and early treatment of skin cancer, the incidence of which is increasing year by year worldwide and poses a great threat to human health. Achievements have been made in the research of skin cancer image classification by using the deep backbone of the convolutional neural network (CNN). This approach, however, only extracts the features of small objects in the image, and cannot locate the important parts. OBJECTIVES: As a result, researchers of the paper turn to vision transformers (VIT) which has demonstrated powerful performance in traditional classification tasks. The self-attention is to improve the value of important features and suppress the features that cause noise. Specifically, an improved transformer network named SkinTrans is proposed. INNOVATIONS: To verify its efficiency, a three step procedure is followed. Firstly, a VIT network is established to verify the effectiveness of SkinTrans in skin cancer classification. Then multi-scale and overlapping sliding windows are used to serialize the image and multi-scale patch embedding is carried out which pay more attention to multi-scale features. Finally, contrastive learning is used which makes the similar data of skin cancer encode similarly so that the encoding results of different data are as different as possible. MAIN RESULTS: The experiment is carried out based on two datasets, namely (1) HAM10000: a large dataset of multi-source dermatoscopic images of common skin cancers; (2)A clinical dataset of skin cancer collected by dermoscopy. The model proposed has achieved 94.3% accuracy on HAM10000 and 94.1% accuracy on our datasets, which verifies the efficiency of SkinTrans. CONCLUSIONS: The transformer network has not only achieved good results in natural language but also achieved ideal results in the field of vision, which also lays a good foundation for skin cancer classification based on multimodal data. This paper is convinced that it will be of interest to dermatologists, clinical researchers, computer scientists and researchers in other related fields, and provide greater convenience for patients.

Oxidative stress (OS), defined as redox imbalance in favor of oxidant burden, is one of the most significant biological events in cancer progression. Cancer cells generally represent a higher oxidant level, which suggests a dual therapeutic strategy by regulating redox status (i.e., pro-oxidant therapy and/or antioxidant therapy). Indeed, pro-oxidant therapy exhibits a great anti-cancer capability, attributing to a higher oxidant accumulation within cancer cells, whereas antioxidant therapy to restore redox homeostasis has been claimed to fail in several clinical practices. Targeting the redox vulnerability of cancer cells by pro-oxidants capable of generating excessive reactive oxygen species (ROS) has surfaced as an important anti-cancer strategy. However, multiple adverse effects caused by the indiscriminate attacks of uncontrolled drug-induced OS on normal tissues and the drug-tolerant capacity of some certain cancer cells greatly limit their further applications. Herein, we review several representative oxidative anti-cancer drugs and summarize their side effects on normal tissues and organs, emphasizing that seeking a balance between pro-oxidant therapy and oxidative damage is of great value in exploiting next-generation OS-based anti-cancer chemotherapeutics.

Abstract Purpose: The purpose of this study is to develop and validate a nomogram model combing radiomics features and clinical characteristics to preoperatively differentiate grade 1 and grade 2/3 tumors in patients with pancreatic neuroendocrine tumors (pNET). Experimental Design: A total of 137 patients who underwent contrast-enhanced CT from two hospitals were included in this study. The patients from the second hospital (n = 51) were selected as an independent validation set. The arterial phase in contrast-enhanced CT was selected for radiomics feature extraction. The Mann–Whitney U test and least absolute shrinkage and selection operator regression were applied for feature selection and radiomics signature construction. A combined nomogram model was developed by incorporating the radiomics signature with clinical factors. The association between the nomogram model and the Ki-67 index and rate of nuclear mitosis were also investigated respectively. The utility of the proposed model was evaluated using the ROC, area under ROC curve (AUC), calibration curve, and decision curve analysis (DCA). The Kaplan–Meier (KM) analysis was used for survival analysis. Results: An eight-feature–combined radiomics signature was constructed as a tumor grade predictor. The nomogram model combining the radiomics signature with clinical stage showed the best performance (training set: AUC = 0.907; validation set: AUC = 0.891). The calibration curve and DCA demonstrated the clinical usefulness of the proposed nomogram. A significant correlation was observed between the developed nomogram and Ki-67 index and rate of nuclear mitosis, respectively. The KM analysis showed a significant difference between the survival of predicted grade 1 and grade 2/3 groups (P = 0.002). Conclusions: The combined nomogram model developed could be useful in differentiating grade 1 and grade 2/3 tumor in patients with pNETs.

Abstract Arrhythmia constitutes a problem with the rate or rhythm of the heartbeat, and an early diagnosis is essential for the timely inception of successful treatment. We have jointly optimized the entire multi-stage arrhythmia classification scheme based on 12-lead surface ECGs that attains the accuracy performance level of professional cardiologists. The new approach is comprised of a three-step noise reduction stage, a novel feature extraction method and an optimal classification model with finely tuned hyperparameters. We carried out an exhaustive study comparing thousands of competing classification algorithms that were trained on our proprietary, large and expertly labeled dataset consisting of 12-lead ECGs from 40,258 patients with four arrhythmia classes: atrial fibrillation, general supraventricular tachycardia, sinus bradycardia and sinus rhythm including sinus irregularity rhythm. Our results show that the optimal approach consisted of Low Band Pass filter, Robust LOESS, Non Local Means smoothing, a proprietary feature extraction method based on percentiles of the empirical distribution of ratios of interval lengths and magnitudes of peaks and valleys, and Extreme Gradient Boosting Tree classifier, achieved an F 1 -Score of 0.988 on patients without additional cardiac conditions. The same noise reduction and feature extraction methods combined with Gradient Boosting Tree classifier achieved an F 1 -Score of 0.97 on patients with additional cardiac conditions. Our method achieved the highest classification accuracy (average 10-fold cross-validation F 1 -Score of 0.992) using an external validation data, MIT-BIH arrhythmia database. The proposed optimal multi-stage arrhythmia classification approach can dramatically benefit automatic ECG data analysis by providing cardiologist level accuracy and robust compatibility with various ECG data sources.

Abstract Background Waist circumference (WC), visceral adiposity index (VAI), lipid accumulation product (LAP), and Chinese visceral adiposity index (CVAI) are considered surrogate indicators of abdominal fat deposition, but the longitudinal association of these indices with cardiovascular (CV) events in adults with type 2 diabetes (T2D) remains unclear. Our study aimed to examine the associations between abdominal obesity indices and incident CV events among people with T2D and to compare their predictive performance in risk assessment. Methods The present study included 2328 individuals with T2D from the Xinjiang Multi-Ethnic Cohort. Multivariable Cox regression analyses were applied to assess the associations between abdominal obesity indices and CV events. Harrell's concordance statistic (C-statistic), net reclassification improvement (NRI) index, and integrated discrimination improvement (IDI) index were utilized to evaluate the predictive performance of each abdominal obesity index. Results At a median follow-up period of 59 months, 289 participants experienced CV events. After multivariable adjustment, each 1-SD increase in WC, VAI, LAP, and CVAI was associated with a higher risk of CV events in people with T2D, with adjusted hazard ratios (HRs) being 1.57 [95% CI (confidence interval): 1.39–1.78], 1.11 (95% CI 1.06–1.16), 1.46 (95% CI 1.36–1.57), and 1.78 (95% CI 1.57–2.01), respectively. In subgroup analyses, these positive associations appeared to be stronger among participants with body mass index (BMI) &lt; 25 kg/m 2 compared to overweight/obese participants. As for the predictive performance, CVAI had the largest C-statistic (0.700, 95% CI 0.672–0.728) compared to VAI, LAP, WC, and BMI (C-statistic: 0.535 to 0.670, all P for comparison &lt; 0.05). When the abdominal obesity index was added to the basic risk model, the CVAI index also showed the greatest incremental risk stratification (C-statistic: 0.751 vs. 0.701, P &lt; 0.001; IDI: 4.3%, P &lt; 0.001; NRI: 26.6%, P &lt; 0.001). Conclusions This study provided additional evidence that all abdominal obesity indices were associated with the risk of CV events and highlighted that CVAI might be a valuable abdominal obesity indicator for identifying the high risk of CV events in Chinese populations with T2D. These results suggest that proactive assessment of abdominal obesity could be helpful for the effective clinical management of the diabetic population.

Abstract This study aims to characterize the gut microbiota in depressed patients with bipolar disorder (BD) compared with healthy controls (HCs), to examine the effects of quetiapine treatment on the microbiota, and to explore the potential of microbiota as a biomarker for BD diagnosis and treatment outcome. Analysis of 16S‐ribosomal RNA gene sequences reveals that gut microbial composition and diversity are significantly different between BD patients and HCs. Phylum Bacteroidetes and Firmicutes are the predominant bacterial communities in BD patients and HCs, respectively. Lower levels of butyrate‐producing bacteria are observed in untreated patients. Microbial composition changes following quetiapine treatment in BD patients. Notably, 30 microbial markers are identified on a random forest model and achieve an area under the curve (AUC) of 0.81 between untreated patients and HCs. Ten microbial markers are identified with the AUC of 0.93 between responder and nonresponder patients. This study characterizes the gut microbiota in BD and is the first to evaluate microbial changes following quetiapine monotherapy. Gut microbiota‐based biomarkers may be helpful in BD diagnosis and predicting treatment outcome, which need further validations.

: The nomogram, incorporating the SVM score, CA 19-9 level and the MR-reported LNM factor, provided an individualized LN status evaluation and helped clinicians guide the surgical decisions.

BACKGROUND: Improving health literacy is an important public health goal in many countries. Although many studies have suggested that low health literacy has adverse effects on an individual's health outcomes, confounding factors are often not accounted. This paper examines the interplay between health literacy and chronic disease prevention. METHODS: A population-based sample of 8194 participants aged 15-69 years old in Ningbo were used from China's 2017 National Health Literacy Surveillance Data. We use multivariate regression analysis to disentangle the relationship between health literacy and chronic disease prevention. RESULTS: We find the association between health literacy and the occurrence of the first chronic condition is attenuated after we adjust the results for age and education. This might arise because having one or more chronic conditions is associated with better knowledge about chronic diseases, thus improve their health literacy. More importantly, we find health literacy is associated with a reduction in the likelihood of having a comorbid condition. However, this protective effect is only found among urban residents, suggesting health literacy might be a key factor explaining the rural-urban disparity in health outcomes. CONCLUSION: Our findings highlight the important role of health literacy in preventing comorbidities instead of preventing the first chronic condition. Moreover, family support could help improve health literacy and result in beneficial effects on health.

BACKGROUND: In patients with coronary artery disease who are being evaluated for percutaneous coronary intervention (PCI), procedures can be guided by fractional flow reserve (FFR) or intravascular ultrasonography (IVUS) for decision making regarding revascularization and stent implantation. However, the differences in clinical outcomes when only one method is used for both purposes are unclear. METHODS: with a plaque burden of more than 70%. The primary outcome was a composite of death, myocardial infarction, or revascularization at 24 months after randomization. We tested the noninferiority of the FFR group as compared with the IVUS group (noninferiority margin, 2.5 percentage points). RESULTS: The frequency of PCI was 44.4% among patients in the FFR group and 65.3% among those in the IVUS group. At 24 months, a primary-outcome event had occurred in 8.1% of the patients in the FFR group and in 8.5% of those in the IVUS group (absolute difference, -0.4 percentage points; upper boundary of the one-sided 97.5% confidence interval, 2.2 percentage points; P = 0.01 for noninferiority). Patient-reported outcomes as reported on the Seattle Angina Questionnaire were similar in the two groups. CONCLUSIONS: In patients with intermediate stenosis who were being evaluated for PCI, FFR guidance was noninferior to IVUS guidance with respect to the composite primary outcome of death, myocardial infarction, or revascularization at 24 months. (Funded by Boston Scientific; FLAVOUR ClinicalTrials.gov number, NCT02673424.).

Early identification of severe patients with coronavirus disease 2019 (COVID-19) is very important for individual treatment. We included 203 patients with COVID-19 by propensity score matching in this retrospective, case-control study. The effects of serum lactate dehydrogenase (LDH) at admission on patients with COVID-19 were evaluated. We found that serum LDH levels had a 58.7% sensitivity and 82.0% specificity, based on a best cut-off of 277.00 U/L, for predicting severe COVID-19. And a cut-off of 359.50 U/L of the serum LDH levels resulted in a 93.8% sensitivity, 88.2% specificity for predicting death of COVID-19. Additionally, logistic regression analysis and Cox proportional hazards model respectively indicated that elevated LDH level was an independent risk factor for the severity (HR: 2.73, 95% CI: 1.25-5.97; P=0.012) and mortality (HR: 40.50, 95% CI: 3.65-449.28; P=0.003) of COVID-19. Therefore, elevated LDH level at admission is an independent risk factor for the severity and mortality of COVID-19. LDH can assist in the early evaluating of COVID-19. Clinicians should pay attention to the serum LDH level at admission for patients with COVID-19.

OBJECTIVES: To explore characteristics of urinary stone composition in China, and determine the effects of gender, age, body mass index (BMI), stone location, and geographical region on stone composition. PATIENTS AND METHODS: We prospectively used Fourier-transform infrared spectroscopy to analyse stones from consecutive patients presenting with new-onset urolithiasis at 46 hospitals in seven geographical areas of China, between 1 June 2010 and 31 May 2015. Chi-squared tests and logistic regression analyses were used to determine associations between stone composition and gender, age, BMI, stone location, and geographical region. RESULTS: The most common stone constituents were: calcium oxalate (CaOx; 65.9%), carbapatite (15.6%), urate (12.4%), struvite (2.7%), and brushite (1.7%). CaOx and urate stones occurred more frequently in males, whereas carbapatite and struvite were more common in females (P < 0.01). CaOx and carbapatite were more common in those aged 30-50 and 20-40 years than in other groups. Brushite and struvite were most common amongst those aged <20 and >70 years. The detection rate of urate increased with age, whilst cystine decreased with age. Obese patients were more likely to have urate stones than carbapatite or brushite stones (P < 0.01). CaOx, carbapatite, brushite, and cystine stones were more frequently found in the kidney than other types, whereas urate and struvite were more frequent in the bladder (P < 0.01). Stone composition varied by geographical region. CONCLUSIONS: The most common stone composition was CaOx, followed by carbapatite, urate, struvite, and brushite. Stone composition differed significantly in patients grouped by gender, age, BMI, stone location, and geographical region.

BACKGROUND: The difficulty of assessment of neoadjuvant chemotherapeutic response preoperatively may hinder personalized-medicine strategies that depend on the results from pathological examination. METHODS: A total of 191 patients with high-grade osteosarcoma (HOS) were enrolled retrospectively from November 2013 to November 2017 and received neoadjuvant chemotherapy (NCT). A cutoff time of November 2016 was used to divide the training set and validation set. All patients underwent diagnostic CTs before and after chemotherapy. By quantifying the tumor regions on the CT images before and after NCT, 540 delta-radiomic features were calculated. The interclass correlation coefficients for segmentations of inter/intra-observers and feature pair-wise correlation coefficients (Pearson) were used for robust feature selection. A delta-radiomics signature was constructed using the lasso algorithm based on the training set. Radiomics signatures built from single-phase CT were constructed for comparison purpose. A radiomics nomogram was then developed from the multivariate logistic regression model by combining independent clinical factors and the delta-radiomics signature. The prediction performance was assessed using area under the ROC curve (AUC), calibration curves and decision curve analysis (DCA). RESULTS: The delta-radiomics signature showed higher AUC than single-CT based radiomics signatures in both training and validation cohorts. The delta-radiomics signature, consisting of 8 selected features, showed significant differences between the pathologic good response (pGR) (necrosis fraction ≥90%) group and the non-pGR (necrosis fraction < 90%) group (P < 0.0001, in both training and validation sets). The delta-radiomics nomogram, which consisted of the delta-radiomics signature and new pulmonary metastasis during chemotherapy showed good calibration and great discrimination capacity with AUC 0.871 (95% CI, 0.804 to 0.923) in the training cohort, and 0.843 (95% CI, 0.718 to 0.927) in the validation cohort. The DCA confirmed the clinical utility of the radiomics model. CONCLUSION: The delta-radiomics nomogram incorporating the radiomics signature and clinical factors in this study could be used for individualized pathologic response evaluation after chemotherapy preoperatively and help tailor appropriate chemotherapy and further treatment plans.