INTRODUCTION: Cancer patients attending emergency departments (EDs) often present with acute symptoms and are frequently admitted. This study aimed to characterise the profile of oncology patients who were discharged from the ED. METHODS: This was a retrospective audit of patients with cancer-related diagnoses who presented to the ED at the Singapore General Hospital (SGH) over a 6-month period from 1 October 2018 to 31 March 2019 and were directly discharged from the ED. Data was extracted from the hospital's electronic medical record system. RESULTS: Of the 492 participants included in the study, the majority were triaged as Priority 2 (61.4%), while 30.7% were triaged as Priority 3, 6.9% as Priority 1 and 1.0% as Priority 4. There was no statistical difference between the National Early Warning scores across the different triage categories in these patients. The most common complaint was (44.3%), followed by genitourinary symptoms (19.5%) and those related to devices, catheters or stomas (17.3%). More investigations of all types were done for patients being managed in Priority 1 (57.6%) than in the other triage categories (40.1% for Priority 2, 23.2% for Priority 3 and 12.0% for Priority 4). Treatment procedures carried out were mainly symptomatic (analgesics, antiemetics, proton pump inhibitors) for 79.8% of the patients. There were no significant differences in the proportion of patients requiring various treatment modalities among the triage categories. CONCLUSION Selected oncological patients may potentially be managed in an ambulatory setting.
Humans ; Emergency Service, Hospital/statistics & numerical data* ; Retrospective Studies ; Female ; Neoplasms/diagnosis* ; Male ; Singapore ; Patient Discharge/statistics & numerical data* ; Middle Aged ; Aged ; Triage ; Adult ; Emergencies ; Aged, 80 and over

OBJECTIVES: To study genotype-phenotype correlations in children with FGFR3 variants and to improve clinical recognition of related disorders. METHODS: Clinical data of 95 patients aged 0-18 years harboring FGFR3 variants, confirmed by whole‑exome sequencing at Shanghai Children's Medical Center from January 2012 to December 2023, were retrospectively reviewed. Detailed phenotypic characterization was performed for 22 patients with achondroplasia (ACH) and 10 with hypochondroplasia (HCH). RESULTS: Among the 95 patients, 52 (55%) had ACH, 24 (25%) had HCH, 9 (9%) had thanatophoric dysplasia, 3 (3%) had syndromic skeletal dysplasia, 2 (2%) had severe achondroplasia with developmental delay and acanthosis nigricans, and 5 (5%) remained unclassified. A previously unreported FGFR3 variant, c.1663G>T, was identified. All 22 ACH patients presented with disproportionate short stature accompanied by limb dysplasia, commonly with macrocephaly, a depressed nasal bridge, bowed legs, and frontal bossing; complications were present in 17 (77%). The 10 HCH patients predominantly exhibited disproportionate short stature with limb dysplasia and depressed nasal bridge. CONCLUSIONS ACH is the most frequent phenotype associated with FGFR3 variants, and missense variants constitute the predominant variant type. The degree of FGFR3 activation appears to correlate with the clinical severity of skeletal dysplasia.
Humans ; Receptor, Fibroblast Growth Factor, Type 3/genetics* ; Child ; Male ; Child, Preschool ; Female ; Infant ; Adolescent ; Dwarfism/genetics* ; Achondroplasia/genetics* ; Lordosis/genetics* ; Infant, Newborn ; Retrospective Studies ; Genetic Association Studies ; Bone and Bones/abnormalities* ; Phenotype ; Limb Deformities, Congenital

Objective To explore the risk factors for noncontiguous spinal fractures(NSFs)in the elderly.Methods The clinical data of 614 elderly patients with spinal fracture from January 2013 to December 2019 were analyzed retrospectively.Patients were divided into the NSFs group and the Non-NSFs group according to whether NSFs occurred or not.Univariate analysis and multivariate Logistic regression analysis were used to screen the risk factors of NSFs.Results Univariate analysis showed that female(P=0.003),high-energy violent injury(P=0.032),osteoporosis(P=0.004),fracture in spring(P=0.020),and previous spinal fracture history(P<0.001)were associated with the occurrence of NSFs.Multivariate Logistic regression analysis showed that fracture in spring(P=0.024),previous spinal fracture history(P<0.001)and high-energy violent injury(P=0.038)were the independent risk factors for the occurrence of NSFs in the elderly.Conclusion High-energy violent injury,fracture in spring and previous spinal fracture history are the independent risk factors for the occurrence of NSFs in the elderly.Therefore,elderly patients with the above risk factors should be examined more carefully and comprehensively to avoid missed diagnosis and delayed diagnosis.In order to reduce the incidence of this disease,corresponding measures should be taken according to the preventable risk factors.

Objective To analyze the risk factors for increased drainage volume after open transforaminal lumbar interbody fusion(TLIF),and to establish a predictive model and then validate it.Methods The clinical data of 680 patients who underwent open TLIF at the General Hospital of Northern Theater Command from January 2016 to December 2019 were collected and the patients were randomly divided into the training group(n=476)and the validation group(n=204).Taking the predictive factors screened out by LASSO regression analysis as independent variables,a multivariate Logistic regression predictive model was constructed.The model was internally validated through the receiver operating characteristic(ROC)curve,Hosmer-Lemeshow goodness-of-fit test,and calibration curve,and its clinical utility was assessed via decision curve analysis(DCA).Results LASSO regression analysis screened out four predictive variables:age,number of surgical segments,operative duration,and intraoperative blood loss.The multivariate Logistic regression predictive model demonstrated that age≥60 years,number of surgical segments≥4,operative duration≥2 hours,and intraoperative blood loss≥200 mL were independent influencing factors for the increased postoperative drainage volume in patients undergoing TLIF(P<0.05).ROC curve analysis revealed an area under the curve(AUC)of 0.816(95%CI:0.798 to 0.867)in the training group and 0.783(95%CI:0.685 to 0.823)in the validation group,indicating that the predictive model had good discriminatory ability.Additionally,the Hosmer-Lemeshow goodness-of-fit test and calibration curve indicated that the predictive model had a good degree of fit,and the predicted probability was basically consistent with the actual probability,demonstrating a good calibration.The DCA results confirmed that this predictive model could be applied in clinical practice.Conclusion The risk factors for increased drainage volume after open TLIF include age,number of surgical segments,operative duration,and intraoperative blood loss.The predictive model established based on these factors demonstrates good performance,and it can be applied in clinical guidance for the selection of drainage tube removal time after TLIF.

Objective To analyze the risk factors for increased drainage volume after open transforaminal lumbar interbody fusion(TLIF),and to establish a predictive model and then validate it.Methods The clinical data of 680 patients who underwent open TLIF at the General Hospital of Northern Theater Command from January 2016 to December 2019 were collected and the patients were randomly divided into the training group(n=476)and the validation group(n=204).Taking the predictive factors screened out by LASSO regression analysis as independent variables,a multivariate Logistic regression predictive model was constructed.The model was internally validated through the receiver operating characteristic(ROC)curve,Hosmer-Lemeshow goodness-of-fit test,and calibration curve,and its clinical utility was assessed via decision curve analysis(DCA).Results LASSO regression analysis screened out four predictive variables:age,number of surgical segments,operative duration,and intraoperative blood loss.The multivariate Logistic regression predictive model demonstrated that age≥60 years,number of surgical segments≥4,operative duration≥2 hours,and intraoperative blood loss≥200 mL were independent influencing factors for the increased postoperative drainage volume in patients undergoing TLIF(P<0.05).ROC curve analysis revealed an area under the curve(AUC)of 0.816(95%CI:0.798 to 0.867)in the training group and 0.783(95%CI:0.685 to 0.823)in the validation group,indicating that the predictive model had good discriminatory ability.Additionally,the Hosmer-Lemeshow goodness-of-fit test and calibration curve indicated that the predictive model had a good degree of fit,and the predicted probability was basically consistent with the actual probability,demonstrating a good calibration.The DCA results confirmed that this predictive model could be applied in clinical practice.Conclusion The risk factors for increased drainage volume after open TLIF include age,number of surgical segments,operative duration,and intraoperative blood loss.The predictive model established based on these factors demonstrates good performance,and it can be applied in clinical guidance for the selection of drainage tube removal time after TLIF.

Objective To explore the risk factors for noncontiguous spinal fractures(NSFs)in the elderly.Methods The clinical data of 614 elderly patients with spinal fracture from January 2013 to December 2019 were analyzed retrospectively.Patients were divided into the NSFs group and the Non-NSFs group according to whether NSFs occurred or not.Univariate analysis and multivariate Logistic regression analysis were used to screen the risk factors of NSFs.Results Univariate analysis showed that female(P=0.003),high-energy violent injury(P=0.032),osteoporosis(P=0.004),fracture in spring(P=0.020),and previous spinal fracture history(P<0.001)were associated with the occurrence of NSFs.Multivariate Logistic regression analysis showed that fracture in spring(P=0.024),previous spinal fracture history(P<0.001)and high-energy violent injury(P=0.038)were the independent risk factors for the occurrence of NSFs in the elderly.Conclusion High-energy violent injury,fracture in spring and previous spinal fracture history are the independent risk factors for the occurrence of NSFs in the elderly.Therefore,elderly patients with the above risk factors should be examined more carefully and comprehensively to avoid missed diagnosis and delayed diagnosis.In order to reduce the incidence of this disease,corresponding measures should be taken according to the preventable risk factors.

Cervical cancer, the most common gynecological malignancy, significantly and adversely affects women's physical health and well-being. Traditional surgical interventions and chemotherapy, while potentially effective, often entail serious side effects that have led to an urgent need for novel therapeutic methods. Photothermal therapy (PTT) has emerged as a promising approach due to its ability to minimize damage to healthy tissue. Connecting a biothiol detection group to PTT-sensitive molecules can improve tumor targeting and further minimize potential side effects. In this study, we developed a near-infrared fluorescence (NIRF)/photoacoustic (PA) dual-mode probe, S-NBD, which demonstrated robust PTT performance. This innovative probe is capable of activating NIRF/PA signals to enable the detection of biothiols with high emission wavelength (838 nm) and large Stokes shift (178 nm), allowing for in vivo monitoring of cancer cells. Additionally, the probe achieved an outstanding photothermal conversion efficiency of 67.1%. The application of laser irradiation (660 nm, 1.0 W/cm², 5 min) was able to achieve complete tumor ablation without recurrence. In summary, this seminal study presents a pioneering NIRF/PA dual-mode dicyanoisophorone-based probe for biothiol imaging, incorporating features from PTT for the first time. This pioneering approach achieves the dual objectives of improving tumor diagnosis and treatment.

Objective:To investigate the value and clinical significance of fasting plasma glucose (FPG) in early pregnancy (8-12 gestational weeks) as a predictor of gestational diabetes mellitus (GDM) among women with different pre-pregnancy body mass index (pre-BMI) categories.Methods:A retrospective study was conducted including 9 710 singleton pregnant women (FPG levels in early pregnancy ≤5.6 mmol/L) who underwent prenatal screening and delivery in Yuyao People's Hospital from January 2020 to December 2022. Participants were stratified based on their pre-BMI as follows: <18.5 ( n=1 406), ≥18.5 to <25.0 ( n=7 162), ≥25.0 to <30.0 ( n=978), and ≥30.0 kg/m 2 ( n=164). Within each pre-BMI category, women were further divided into four groups based on FPG levels in early pregnancy (<4.5, ≥4.5 to <4.8, ≥4.8 to <5.1, and ≥5.1 mmol/L). Univariate and multivariate logistic regression analysis were used to identify risk factors for GDM, and receiver operating characteristic (ROC) curve was applied to evaluate the efficacy of FPG in early pregnancy based on different pre-BMI in predicting GDM. Results:The overall incidence of GDM in the singleton pregnancy women with FPG levels in early pregnancy ≤5.6 mmol/L was 12.3% (1 197/9 710). For a pre-BMI of <18.5 kg/m 2, the ORs with 95% CIs for GDM within the different FPG categories (<4.5, ≥4.5 to <4.8, ≥4.8 to <5.1, and ≥5.1 mmol/L) were 0.041 (95% CI: 0.015-0.409), 1.834 (95% CI: 1.089-3.088), 6.779 (95% CI: 4.041-11.371), and 13.723 (95% CI: 5.560-33.871), respectively. For pre-BMI of ≥18.5 to <25.0 kg/m 2, the respective the ORs with 95% CIs were 0.048 (95% CI: 0.012-0.203), 2.573 (95% CI: 2.091-3.168), 9.023 (95% CI: 7.240-11.245), and 9.158 (95% CI: 6.484-12.937). For pre-BMI of ≥25.0 to <30.0 kg/m 2, the ORs with 95% CIs were 0.108 (95% CI: 0.053-0.446), 1.698 (95% CI: 1.064-2.654), 7.537 (95% CI: 5.285-13.080), and 9.994 (95% CI: 5.613-18.218). For pre-BMI of ≥30.0 kg/m 2, the ORs with 95% CIs were 0.098 (95% CI: 0.072-1.015), 2.888 (95% CI: 0.911-9.157), 13.674 (95% CI: 3.480-53.736), and 20.509 (95% CI: 6.674-63.019). The optimal cutoff value of FPG in early pregnancy for GDM prediction was 4.7 mmol/L with an area under the curve of 0.752, the risk of GDM significantly increased with FPG levels ≥4.7 mmol/L in early pregnancy across all pregnant women ( OR=17.356, 95% CI: 13.757-21.896, P<0.001). Conclusions:In the singleton pregnancy women with FPG levels in early pregnancy ≤5.6 mmol/L, FPG in early pregnancy is an independent risk factor for the occurrence of GDM; for pregnant women stratified by the same pre-BMI, the risk of developing GDM increases progressively with the rise of FPG in early pregnancy. FPG in early pregnancy has a certain value in predicting the occurrence of GDM.

Objective To explore the diagnostic efficacy of serum 14-3-3β protein combined with fractional exhaled nitric oxide(FeNO)and conventional ventilatory lung function parameters in diagnosing bronchial asthma(referred to as"asthma")in children.Methods A prospective study included 136 children initially diagnosed with asthma during an acute episode as the asthma group,and 85 healthy children undergoing routine health checks as the control group.The study compared the differences in serum 14-3-3β protein concentrations between the two groups,analyzed the correlation of serum 14-3-3β protein with clinical indices,and evaluated the diagnostic efficacy of combining 14-3-3β protein,FeNO,and conventional ventilatory lung function parameters for asthma in children.Results The concentration of serum 14-3-3β protein was higher in the asthma group than in the control group(P<0.001).Serum 14-3-3β protein showed a positive correlation with the percentage of neutrophils and total serum immunoglobulin E,and a negative correlation with conventional ventilatory lung function parameters(P<0.05).Cross-validation of combined indices showed that the combination of 14-3-3β protein,FeNO,and the percentage of predicted value of forced expiratory flow at 75%of lung volume had an area under the curve of 0.948 for predicting asthma,with a sensitivity and specificity of 88.9%and 93.7%,respectively,demonstrating good diagnostic efficacy(P<0.001).The model had the best extrapolation.Conclusions The combination of serum 14-3-3β protein,FeNO,and the percentage of predicted value of forced expiratory flow at 75%of lung volume can significantly improve the diagnostic efficacy for asthma in children.

Objective To explore the efficiency of artificial intelligence and radiomics-assisted X-ray in diagnosis of lumbar osteoporotic vertebral compression fractures(OVCF).Methods The clinical data of 455 patients diagnosed as lumbar OVCF by MRI in our hospital were selected.The patients were divided into the training group(n=364)and the validation group(n=91),X-ray films were extracted,the image delineation,feature extraction and data analysis were carried out,and the artificial intelligence radiomics deep learning was applied to establish a diagnostic model for OVCF.After verifying the effectiveness of the model by receiver operating characteristic(ROC)curve,area under the curve(AUC),calibration curve,and decision curve analysis(DCA),the efficiencies of manual reading,model reading,and model-assisted manual reading of X-ray in the early diagnosis of OVCF were compared.Results The ROC curve,AUC and calibration curve proved that the model had good discrimination and calibration,and excellent diagnostic performance.DCA demonstrated that the model had a higher clinical net benefit.The diagnostic efficiency of the manual reading group:the accuracy rate was 0.89,the recall rate was 0.62.The diagnostic efficiency of the model reading group:the accuracy rate was 0.93,the recall rate was 0.86,the model diagnosis showed good predictive performance,which was significantly better than the manual reading group.The diagnostic efficiency of the model-assisted manual reading group:the accuracy rate was 0.92,the recall rate was 0.72,and the recall rate of the model-assisted manual reading group was higher than that of the manual reading group,but lower than that of the model reading group,indicating the superiority of the model diagnosis.Conclusion The diagnostic model established based on artificial intelligence and radiomics in this study has reached an ideal level of efficacy,with better diagnostic efficacy compared with manual reading,and can be used to assist X-ray in the early diagnosis of OVCF.