Prognostic model incorporating immune checkpoint genes to predict the immunotherapy efficacy for lung adenocarcinoma: a cohort study integrating machine learning algorithms.

This study aimed to develop and validate a nomogram based on immune checkpoint genes (ICGs) for predicting prognosis and immune checkpoint blockade (ICB) efficacy in lung adenocarcinoma (LUAD) patients. A total of 385 LUAD patients from the TCGA database and 269 LUAD patients in the combined dataset (GSE41272 + GSE50081) were divided into training and validation cohorts, respectively. Three different machine learning algorithms including random forest (RF), least absolute shrinkage and selection operator (LASSO) logistic regression analysis, and support vector machine (SVM) were employed to select the predictive markers from 82 ICGs to construct the prognostic nomogram. The X-tile software was used to stratify patients into high- and low-risk subgroups based on the nomogram-derived risk scores. Differences in functional enrichment and immune infiltration between the two subgroups were assessed using gene set variation analysis (GSVA) and various algorithms. Additionally, three lung cancer cohorts receiving ICB therapy were utilized to evaluate the ability of the model to predict ICB efficacy in the real world. Five ICGs were identified as predictive markers across all three machine learning algorithms, leading to the construction of a nomogram with strong potential for prognosis prediction in both the training and validation cohorts (all AUC values close to 0.800). The patients were divided into high- (risk score ≥ 185.0) and low-risk subgroups (risk score < 185.0). Compared to the high-risk subgroup, the low-risk subgroup exhibited enrichment in immune activation pathways and increased infiltration of activated immune cells, such as CD8 + T cells and M1 macrophages (P < 0.05). Furthermore, the low-risk subgroup had a greater likelihood of benefiting from ICB therapy and longer progression-free survival (PFS) than did the high-risk subgroup (P < 0.05) in the two cohorts receiving ICB therapy. A nomogram based on ICGs was constructed and validated to aid in predicting prognosis and ICB treatment efficacy in LUAD patients.

Predictive Model to Identify the Long Time Survivor in Patients with Glioblastoma: A Cohort Study Integrating Machine Learning Algorithms.

We aimed to develop and validate a predictive model for identifying long-term survivors (LTS) among glioblastoma (GB) patients, defined as those with an overall survival (OS) of more than 3 years. A total of 293 GB patients from CGGA and 169 from TCGA database were assigned to training and validation cohort, respectively. The differences in expression of immune checkpoint genes (ICGs) and immune infiltration landscape were compared between LTS and short time survivor (STS) (OS<1.5 years). The differentially expressed genes (DEGs) and weighted gene co-expression network analysis (WGCNA) were used to identify the genes differentially expressed between LTS and STS. Three different machine learning algorithms were employed to select the predictive genes from the overlapping region of DEGs and WGCNA to construct the nomogram. The comparison between LTS and STS revealed that STS exhibited an immune-resistant status, with higher expression of ICGs (P<0.05) and greater infiltration of immune suppression cells compared to LTS (P<0.05). Four genes, namely, OSMR, FMOD, CXCL14, and TIMP1, were identified and incorporated into the nomogram, which possessed good potential in predicting LTS probability among GB patients both in the training (C-index, 0.791; 0.772-0.817) and validation cohort (C-index, 0.770; 0.751-0.806). STS was found to be more likely to exhibit an immune-cold phenotype. The identified predictive genes were used to construct the nomogram with potential to identify LTS among GB patients.

[Facial nerve protection in surgery for rare tumors of the internal auditory canal].

Objective:To explore strategies for preserving facial nerve function during surgeries for rare tumors of the internal auditory canal. Methods:A total of 235 cases of internal auditory canal tumors treated between 2010 and 2023 were included, encompassing vestibular schwannomas, cavernous hemangiomas, meningiomas, and other rare tumors. Various data, including clinical presentations, imaging classifications, and treatment processes, were meticulously analyzed to delineate the characteristics of rare tumors and assess pre-and postoperative facial nerve function. Results:Among all internal auditory canal tumors, vestibular schwannomas accounted for 91.9%. In rare tumors, facial nerve schwannomas constituted 5.3%, cavernous hemangiomas 26.3%, meningiomas 15.8%, and arterial aneurysms 10.5%. Significantly, patients with cavernous hemangiomas displayed pronounced invasion of the facial nerve by the tumor, in contrast to other tumor types where clear boundaries with the facial nerve were maintained. During surgery, individualized approaches and strategies for facial nerve protection were implemented for different tumor types, involving intraoperative dissection, tumor excision, and facial nerve reconstruction. Conclusion:Preservation of the facial nerve is crucial in the surgical management of rare tumors of the internal auditory canal. Accurate preoperative diagnosis, appropriate timing of surgery, selective surgical approaches, and meticulous intraoperative techniques can maximize the protection of facial nerve function. Personalized treatment plans and strategies for facial nerve functional reconstruction are anticipated to enhance surgical success rates, reduce the risk of postoperative facial nerve dysfunction, and ultimately improve the quality of life for patients.

Serum Sulfur-Containing Amino Acids and Risk of Maternal Gestational Diabetes and Adverse Growth Patterns in Offspring.

BACKGROUND: To estimate associations of sulfur-containing amino acids (SAAs) in the early trimester of pregnancy and gestational diabetes mellitus (GDM) and estimate associations of maternal SAAs with adverse growth patterns in offspring. METHODS: We established a 1:1 matched case-control study (n = 486) from our cohort of pregnant women, and 401 children were followed up at ages 1 to 8 years. We conducted binary conditional logistic regression to estimate the risk associations of serum SAAs with GDM. Multinomial logistic regression was implemented to explore associations of maternal SAAs with adverse growth patterns in the offspring. RESULTS: High serum methionine and cystine were independently associated with increased GDM risk (OR: 1.92, 95%CI: 1.18-3.13 and 2.69, 1.59-4.53). Conversely, a low level of serum taurine was independently associated with increased GDM risk (2.61, 1.64-4.16). Maternal high cystine and low taurine were also associated with an increased risk of persistent obesity growth pattern (POGP) in offspring (OR: 2.79, 95%CI: 1.09-7.17 and 3.92, 1.11-13.89) and the effect was largely independent of GDM. CONCLUSIONS: High serum methionine, cystine and low serum taurine in the early trimester of pregnancy were associated with a greatly increased risk of GDM. Maternal high cystine and low taurine were associated with elevated risk of offspring POGP, largely independent of GDM.

MRI-based scoring model to predict massive hemorrhage during dilatation and curettage in patients with cesarean scar pregnancy.

OBJECTIVE: To construct a scoring model based on MRI signs to predict massive hemorrhage during dilatation and curettage in cesarean scar pregnancy (CSP) patients. MATERIALS AND METHODS: The MRIs of CSP patients admitted to a tertiary referral hospital between February 2020 and July 2022 were retrospectively reviewed. The included patients were randomly assigned to the training and validation cohorts. The univariate and multivariate logistic regression analyses were adopted to identify the independent risk factors for massive hemorrhage (the amount of bleeding ≥ 200 ml) during the dilatation and curettage. A scoring model predicting intraoperative massive hemorrhage was established where each positive independent risk factor was assigned 1 point, and the predictive power of this model was evaluated both in the training and validation cohorts via the receiver operating characteristic curve. RESULTS: A total of 187 CSP patients were enrolled, who were divided into the training cohort (31 in 131 patients had massive hemorrhage) and validation cohort (10 in 56 patients had massive hemorrhage). The independent risk factors for intraoperative massive hemorrhage included cesarean section diverticulum area (OR = 6.957, 95% CI 1.993-21.887; P = 0.001), uterine scar thickness (OR = 5.113, 95% CI 2.086-23.829; P = 0.025) and gestational sac diameter (OR = 3.853, 95% CI 1.103-13.530; P = 0.025). A scoring model with a total point of 3 was developed and the CSP patients were divided into low-risk (Total points < 2) and high-risk groups (Total points ≥ 2) for intraoperative massive hemorrhage accordingly. This model possessed high prediction performance both in the training cohort (area under the curve [AUC] = 0.896, 95% CI 0.830-0.942) and validation cohort (AUC = 0.915, 95% CI 0.785-1.000). CONCLUSION: We first constructed a MRI-based scoring model for predicting intraoperative massive hemorrhage in CSP patients, which could help the decision-making of the patients' therapy strategies. Low-risk patients can be cured by D&C alone to reduce the financial burden, while high-risk patients require more adequate preoperative preparation or consideration of changing surgical approaches to reduce bleeding risk.

A Scoring System to Select the Candidates for Adjuvant Chemotherapy Alone in High-Risk Early-Stage Cervical Cancer Patients With Pelvic Lymph Node Metastases After Surgery.

BACKGROUND: The aim was to build a risk scoring system to guide the adjuvant treatment for early-stage cervical cancer patients with pelvic lymph node (LN) metastases after surgery. METHODS: A cohort of 1213 early-stage cervical cancer patients with pelvic LN metastases (T1-2aN1M0) were selected from the NCI SEER database, of which 1040 patients received adjuvant external beam radiotherapy concurrent with chemotherapy (EBRT+Chemo) and 173 patients received adjuvant chemotherapy alone. The Cox regression analysis was applied to identify the risk factors associated with worse survival. The exp (ß) of each independent risk factors from multivariate analysis was assigned to develop the risk scoring system. The total cohort was divided into different risk subgroups accordingly and the efficacy of different adjuvant modalities in each risk subgroups was compared. RESULTS: The patients were divided into 3 risk subgroups (Low-risk: total score <7.20, Middle-risk:7.20≤ total score≤ 8.40, High-risk: total score<8.40) based on the scoring system incorporating 5 independent risk factors. The survival analysis suggested that low-risk (hazard ratio [HR]=1.046, 95% CI: 0.586-1.867; P= 0.879) and middle-risk patients (HR=0.709, 95% CI: 0.459-1.096; P =0.122) could not benefit more from EBRT+Chemo than Chemo alone. However, EBRT+Chemo remained the superiority to Chemo alone in the high-risk subgroup (HR=0.482, 95% CI: 0.294-0.791; P =0.003). CONCLUSION: A risk scoring system has been built to direct the adjuvant treatment for early-stage cervical cancer patients with pelvic LN metastases after surgery, where Chemo alone was totally enough for low-risk and middle-risk patients stratified by the model while EBRT+Chemo was still recommended for patients in the high-risk subgroup.

A population-based risk scoring system to individualize adjuvant treatment for stage IIIC endometrial cancer patients after surgery.

BACKGROUND: To develop a risk scoring system to tailor the adjuvant treatment for stage IIIC EC patients after surgery. METHODS: Data source was from the Surveillance, Epidemiology, and End Results (SEER) registry, where 3251 post-operative stage IIIC EC patients with different adjuvant treatment were included. Cox regression analysis was used to identify risk factors. The exp (ß) of each independent risk factors generating from the cox analysis was used to construct the risk scoring system, which was further utilized to divide the patients into different risk subgroups and the efficacy of different adjuvant modalities in each risk subgroups would be compared accordingly. RESULTS: Six independent risk factors were identified to develop the scoring system, which further divided the patients into three risk subgroups based on the total risk score (Low-risk≤8.46, 8.47 ≤ Middle-risk≤9.94, High-risk≥9.95). This study revealed that CRT was not superior to RT alone (HR:1.208, 95%CI: 0.852-1.741; P = 0.289) or CT alone (HR:1.260, 95%CI: 0.750-2.116; P = 0.382) in Low-risk subgroup. We also observed that CRT had a survival advantage over other treatment modalities in the Middle-risk subgroup (All P < 0.001), but CRT and CT alone to be superimposable in the High-risk subgroup (HR: 1.395, 95%CI: 0.878-2.216; P = 0.159). CONCLUSION: A risk scoring system has been developed to tailor the adjuvant treatment for stage IIIC EC patients after surgery, where RT or CT alone could be a substitute for CRT in Low-risk patients and CT alone was a potential alternative for High-risk patients while CRT remained to be the optimal choice for the Middle-risk patients.

Prognostic model for the exemption of adjuvant chemotherapy in stage IIIC endometrial cancer patients.

Background: This study aimed to develop a nomogram to predict the survival for stage IIIC endometrial cancer (EC) patients with adjuvant radiotherapy (ART) alone and personalize recommendations for the following adjuvant chemotherapy (ACT). Methods: In total, 746 stage IIIC EC patients with ART alone were selected from the Surveillance, Epidemiology, and End Results (SEER) registry. Cox regression analysis was performed to identify independent risk factors. A nomogram was developed accordingly, and the area under the receiver operating characteristic curve (AUC) and C-index were implemented to assess the predictive power. The patients were divided into different risk strata based on the total points derived from the nomogram, and survival probability was compared between each risk stratus and another SEER-based cohort of stage IIIC EC patients receiving ART+ACT (cohort ART+ACT). Results: Five independent predictors were included in the model, which had favorable discriminative power both in the training (C-index: 0.732; 95% CI: 0.704-0.760) and validation cohorts (C-index: 0.731; 95% CI: 0.709-0.753). The patients were divided into three risk strata (low risk <135, 135 ≤ middle risk ≤205, and high risk >205), where low-risk patients had survival advantages over patients from cohort ART+ACT (HR: 0.45, 95% CI: 0.33-0.61, P < 0.001). However, the middle- and high-risk patients were inferior to patients from cohort ART+ACT in survival (P < 0.001). Conclusion: A nomogram was developed to exclusively predict the survival for stage IIIC EC patients with ART alone, based on which the low-risk patients might be perfect candidates to omit the following ACT. However, the middle- and high-risk patients would benefit from the following ACT.

Construction and validation of a prognostic model for stage IIIC endometrial cancer patients after surgery.

BACKGROUND: To explore the most predictive lymph node (LN) scheme for stage IIIC endometrial cancer (EC) patients after hysterectomy and develop a scheme-based nomogram. METHODS: Data from 2626 stage IIIC EC patients, diagnosed between 2010 and 2014, were extracted from the Surveillance, Epidemiology, and End Results (SEER) registry. The predictive ability of four LN schemes was assessed using C-index and Akaike information criterion (AIC). A nomogram based on the most predictive LN scheme was constructed and validated. The comparison of the predictive ability between nomogram and FIGO stage was conducted using the area under the receiver operating characteristic curve (AUC) and decision curve analysis (DCA). RESULTS: FIGO stage (stage IIIC1/stage IIIC2) was not an independent risk factor for OS in stage IIIC EC patients (P = 0.672) and log odds of positive lymph nodes (LODDS) had the best predictive ability (C-index: 0.742; AIC: 8228.95). A nomogram based on LODDS was constructed and validated, which had a decent C-index of 0.742 (0.723-0.762). The nomogram showed a better predictive ability than that of the FIGO staging system. CONCLUSION: FIGO IIIC1/FIGO IIIC2 could not differentiate the prognosis for stage IIIC EC patients. We developed and validated a nomogram based on LODDS to predict OS for post-operative patients with stage IIIC EC.

A real-world, population-based study of the trends for incidence and prognosis in high-grade neuroendocrine tumor of cervix.

To explore the incidence and prognosis trends for high-grade cervical neuroendocrine tumor (HGCNET) and construct a nomogram to predict prognosis for HGCNET. Annual age-adjusted incidence of HGCNET from 1975 to 2015 was retrieved from the Surveillance, Epidemiology, and End Results program, the linear regression, poisson regression and annual percentage changes were used to assess the incidence trend. Also, trends for relative survival (RS) and overall survival (OS) in HGCNET patients from 1975 to 2015 were evaluated. From 1988 to 1975, 514 HGCNET patients were selected and divided into two cohorts with a ratio of 7:3. Nomogram to predict OS for these patients was constructed and validated. The incidence trend for HGCNET was unchanged in the past four decades (P = 0.734), but the proportion of HGCNET in diagnosed cervical cancer slightly increased from 0.9% in 1975 to 1.9% in 2015 (P < 0.001). The 5-year RS and OS for HGCNET in the study periods decreased steadily (RS: P = 0.009; OS: P = 0.008). Nomogram incorporating age, T stage, lymph-node positive, distant metastasis and surgery was constructed. The C-index of the nomogram was 0.716 (0.680-0.752), which was higher than the FIGO staging system. The incidence of HGCNET remained unchanged in the past four decades but the proportion of HGCNET has slightly increased. Besides, a steadily decreasing survival for HGCNET was observed in the study periods. A nomogram was constructed to better predict prognosis for HGCNET.

Label-Free Virtual HER2 Immunohistochemical Staining of Breast Tissue using Deep Learning.

The immunohistochemical (IHC) staining of the human epidermal growth factor receptor 2 (HER2) biomarker is widely practiced in breast tissue analysis, preclinical studies, and diagnostic decisions, guiding cancer treatment and investigation of pathogenesis. HER2 staining demands laborious tissue treatment and chemical processing performed by a histotechnologist, which typically takes one day to prepare in a laboratory, increasing analysis time and associated costs. Here, we describe a deep learning-based virtual HER2 IHC staining method using a conditional generative adversarial network that is trained to rapidly transform autofluorescence microscopic images of unlabeled/label-free breast tissue sections into bright-field equivalent microscopic images, matching the standard HER2 IHC staining that is chemically performed on the same tissue sections. The efficacy of this virtual HER2 staining framework was demonstrated by quantitative analysis, in which three board-certified breast pathologists blindly graded the HER2 scores of virtually stained and immunohistochemically stained HER2 whole slide images (WSIs) to reveal that the HER2 scores determined by inspecting virtual IHC images are as accurate as their immunohistochemically stained counterparts. A second quantitative blinded study performed by the same diagnosticians further revealed that the virtually stained HER2 images exhibit a comparable staining quality in the level of nuclear detail, membrane clearness, and absence of staining artifacts with respect to their immunohistochemically stained counterparts. This virtual HER2 staining framework bypasses the costly, laborious, and time-consuming IHC staining procedures in laboratory and can be extended to other types of biomarkers to accelerate the IHC tissue staining used in life sciences and biomedical workflow.

IDF Diabetes Atlas: Estimation of Global and Regional Gestational Diabetes Mellitus Prevalence for 2021 by International Association of Diabetes in Pregnancy Study Group's Criteria.

AIMS: The approaches used to screen and diagnose gestational diabetes mellitus (GDM) vary widely. We generated a comparable estimate of the global and regional prevalence of GDM by International Association of Diabetes in Pregnancy Study Group (IADPSG)'s criteria. METHODS: We searched PubMed and other databases and retrieved 57 studies to estimate the prevalence of GDM. Prevalence rate ratios of different diagnostic criteria, screening strategies and age groups, were used to standardize the prevalence of GDM in individual studies included in the analysis. Fixed effects meta-analysis was conducted to estimate standardized pooled prevalence of GDM by IDF regions and World Bank country income groups. RESULTS: The pooled global standardized prevalence of GDM was 14.0% (95% confidence interval: 13.97-14.04%). The regional standardized prevalence of GDM were 7.1% (7.0-7.2%) in North America and Caribbean (NAC), 7.8% (7.2-8.4%) in Europe (EUR), 10.4% (10.1-10.7%) in South America and Central America (SACA), 14.2% (14.0-14.4%) in Africa (AFR), 14.7% (14.7-14.8%) in Western Pacific (WP), 20.8% (20.2-21.4%) in South-East Asia (SEA) and 27.6% (26.9-28.4%) in Middle East and North Africa (MENA). The standardized prevalence of GDM in low-, middle- and high-income countries were 12.7% (11.0-14.6%), 9.2% (9.0-9.3%) and 14.2% (14.1-14.2%), respectively. CONCLUSIONS: The highest standardized prevalence of GDM was in MENA and SEA, followed by WP and AFR. Among the three World Bank country income groups, high income countries had the highest standardized prevalence of GDM. The standardized estimates for the prevalence of GDM provide an insight for the global picture of GDM.

Interactions between Prepregnancy Overweight and Passive Smoking for Macrosomia and Large for Gestational Age in Chinese Pregnant Women.

INTRODUCTION: Previous analysis showed that passive smoking and overweight were associated with an increased risk of gestational diabetes mellitus (GDM) in a synergistic manner, while GDM increased the risk of macrosomia/large for gestational age (LGA). This study aimed to examine any interactive effects between passive smoking and overweight/obesity on risk of macrosomia/LGA. METHODS: From 2010 to 2012, 22,302 pregnant women registered for pregnancy at a primary hospital in Tianjin, China. Data were collected longitudinally; that is, from their first antenatal care visit, at the glucose challenge test (GCT) time (24-28 weeks of gestation) and at delivery. Passive smoking was self-reported. Macrosomia was defined as birth weight ≥4,000 g. Binary logistic regression was used to obtain odds ratios (ORs) and 95% confidence intervals (CIs). Additive interaction was used to test the synergistic effect. RESULTS: Passive smokers accounted for 57.4% of women (n = 8,230). Using nonpassive smoking and prepregnancy body mass index (BMI) <24.0 kg/m2 as the reference, the adjusted ORs of overweight alone and passive smoking alone for macrosomia were 2.39 (95% CI: 2.11-2.71) and 1.17 (95% CI: 1.04-1.32). Copresence of passive smoking and prepregnancy BMI ≥24.0 kg/m2 increased the OR to 2.70 (95% CI: 2.28-3.20), with a significant additive interaction. After further adjustment for GDM or GCT, the OR of copresence of both risk factors was slightly attenuated to 2.52 (2.13-3.00) and 2.51 (2.11-2.98), with significant additive interaction. However, the additive interaction between prepregnancy overweight/obesity and passive smoking for LGA was nonsignificant. CONCLUSIONS: Prepregnancy overweight/obesity was associated with an increased risk of macrosomia in Chinese women synergistically with passive smoking during pregnancy, and most of the association was not modified by hyperglycemia during pregnancy.

Comparison of Different Lymph Node Staging Schemes for Predicting Survival Outcomes in Node-Positive Endometrioid Endometrial Cancer Patients.

Objective: To compare the prognostic predictive performance of six lymph node (LN) staging schemes: American Joint Committee on Cancer (AJCC) N stage, number of retrieved lymph nodes (NRLN), number of positive lymph nodes (NPLN), number of negative lymph nodes (NNLN), lymph node ratio (LNR), and log odds of positive lymph nodes (LODDS) among node-positive endometrioid endometrial cancer (EEC) patients. Methods: A total of 3,533 patients diagnosed with node-positive EEC between 2010 and 2016 from the Surveillance, Epidemiology, and End Results (SEER) database were retrospectively analyzed. We applied X-tile software to identify the optimal cutoff value for different staging schemes. Univariate and multivariate Cox regression models were used to assess the relationships between different LN schemes and survival outcomes [disease-specific survival (DSS) and overall survival (OS)]. Moreover, Akaike information criterion (AIC) and Harrell concordance index (C-index) were used to evaluate the predictive performance of each scheme in both continuous and categorical patterns. Results: N stage (N1/N2) was not an independent prognostic factor for node-positive EEC patients based on multivariate analysis (DSS: p = 0.235; OS: p = 0.145). Multivariate model incorporating LNR demonstrated the most superior goodness of fit regardless of continuous or categorical pattern. Regarding discrimination power of the models, LNR outperformed other models in categorical pattern (OS: C-index = 0.735; DSS: C-index = 0.737); however, LODDS obtained the highest C-index in continuous pattern (OS: 0.736; DSS: 0.739). Conclusions: N stage (N1/N2) was unable to differentiate the prognosis for node-positive EEC patients in our study. However, LNR and LODDS schemes seemed to have a better predictive performance for these patients than other number-based LN schemes whether in DSS or OS, which revealed that LNR and LODDS should be more helpful in prognosis assessment for node-positive EEC patients than AJCC N stage.

Conditional survival for high-risk early-stage cervical cancer patients with lymph node metastasis after hysterectomy.

BACKGROUND: To estimate conditional survival (CS) for high-risk early-stage cervical cancer patients with lymph node metastasis after hysterectomy. METHODS: 1964 T1-2N1M0 cervical cancer patients who underwent primary hysterectomy from 2004 to 2015 were extracted from the Surveillance, Epidemiology, and End Result (SEER) Program. Univariate and multivariate cox regression analysis were used to identify independent risk factors. 5-year conditional disease-specific survival (CDS5) and 5-year conditional relative survival (CRS5) were estimated. CDS5 and CRS5 stratified by risk factors were further calculated. RESULTS: CDS5 and CRS5 increased from 71.0% and 73.7% at 0-year to 89.2% and 91.7% at 5-year, respectively. Inversely, the actuarial disease-specific survival and RS dropped from 71.0% and 73.7% at 5-year to 63.3% and 67.6% at 10-year, respectively. Patients with unfavorable factors had a bigger gap between actuarial survival and CS. Both CDS5 and CRS5 curves across stratas of each prognostic factor had a tendency to level off with time elapsing. Notably, CRS5 couldn't exceed 95% even after 5-year follow-up except for patients with grade I disease (CRS5 at 5-year: 100%) or tumor size less than 2 cm (CRS5 at 5-year: 96%). CONCLUSION: CS increased over time while actuarial survival decreased as time passed. Patients with unfavorable factors had bigger improvement in CS than those with favorable factors. Excess mortality still existed in these patients after 5-year follow-up compared to the general population except for patients with grade I disease or tumor size <2 cm, who might gradually decrease follow-up times after 5-year.

Effects of lifestyle intervention on long-term risk of diabetes in women with prior gestational diabetes: A systematic review and meta-analysis of randomized controlled trials.

We performed two meta-analyses to estimate the effects of lifestyle intervention during pregnancy and after delivery on the risk of postpartum diabetes among women with gestational diabetes mellitus (GDM). We searched the major databases to retrieve articles published in English or Chinese before 15 December 2019. The inclusion criteria were randomized controlled trials (RCTs) of diet, physical activity or both, conducted during or after pregnancy among women with GDM. The exclusion criteria were (1) having type 1 or type 2 diabetes before the intervention and (2) without postpartum diabetes documented. Fixed-effects model analysis was used to obtain the pooled relative risks (RRs) and 95% confidence intervals (CIs) of lifestyle intervention for diabetes in women with GDM. Four RCTs were identified to have implemented the intervention during pregnancy (n = 2883) and 10 to have conducted it within 3 years after delivery (n = 1733). Lifestyle intervention during pregnancy was not effective at reducing the risk of postpartum diabetes (RR: 0.91, 95%CI: 0.66-1.25). However, lifestyle intervention initiated within 3 years after delivery was highly effective in reducing the risk of postpartum diabetes (pooled RR: 0.57, 95% CI: 0.42-0.78). In conclusion, our findings support the early initiation of lifestyle intervention in women with GDM for the prevention of diabetes.

Prevention of nosocomial COVID-19 infections in otorhinolaryngology-head and neck surgery.

Coronavirus disease 2019 (COVID-19) has rapidly evolved into a pandemic, causing a global public health crisis. Many frontline healthcare workers providing ear, nose and throat services have been reported to contract COVID-19 at work. Early during the COVID-19 outbreak, several medical professionals in Otolaryngology-Head and Neck Surgery were infected in Wuhan, China. A series of measures were then taken immediately, which successfully halted the spread of the disease. Here we would like to share the lessons we have learned, and our experience to protect our health care workers during the COVID-19 pandemic.

Association of obesity status and metabolic syndrome with site-specific cancers: a population-based cohort study.

BACKGROUND: Obesity and metabolic syndrome (MetS) appear in clusters and are both associated with an increased risk of cancer. However, it remains unknown whether obesity status with or without MetS increases the risk of site-specific cancers. METHODS: We used data derived from 390,575 individuals (37-73 years old) from the UK Biobank who were enrolled from 2006-2016 with a median of 7.8 years of follow-up. Obesity was defined by BMI ≥ 30 kg/m2 and MetS was defined by the criteria of the Adult Treatment Panel-III (ATP-III). Cox proportional hazards models were used to investigate the associations of BMI and MetS with 22 cancers. RESULTS: Metabolically healthy obesity (MHO) and metabolically unhealthy obesity (MUO) phenotypes represented 6.7% and 17.9% of the total analytic samples and 27.1% and 72.9% of the included subpopulation with obesity, respectively. Obesity was independently associated with higher risks of 10 of 22 cancers. Stratified by metabolic status, the MUO phenotype was consistently associated with 10 obesity-related cancers. In contrast, the MHO phenotype was only associated with increased risks of five cancers: endometrium, oesophagus, kidney, pancreas and postmenopausal breast cancers. CONCLUSION: Even in metabolically healthy individuals, obesity was associated with increased risks of five cancers, whereas we did not find that these individuals were associated with increased risks of several other obesity-related cancers.

Multi-level feature aggregation network for instrument identification of endoscopic images.

Identification of surgical instruments is crucial in understanding surgical scenarios and providing an assistive process in endoscopic image-guided surgery. This study proposes a novel multilevel feature-aggregated deep convolutional neural network (MLFA-Net) for identifying surgical instruments in endoscopic images. First, a global feature augmentation layer is created on the top layer of the backbone to improve the localization ability of object identification by boosting the high-level semantic information to the feature flow network. Second, a modified interaction path of cross-channel features is proposed to increase the nonlinear combination of features in the same level and improve the efficiency of information propagation. Third, a multiview fusion branch of features is built to aggregate the location-sensitive information of the same level in different views, increase the information diversity of features, and enhance the localization ability of objects. By utilizing the latent information, the proposed network of multilevel feature aggregation can accomplish multitask instrument identification with a single network. Three tasks are handled by the proposed network, including object detection, which classifies the type of instrument and locates its border; mask segmentation, which detects the instrument shape; and pose estimation, which detects the keypoint of instrument parts. The experiments are performed on laparoscopic images from MICCAI 2017 Endoscopic Vision Challenge, and the mean average precision (AP) and average recall (AR) are utilized to quantify the segmentation and pose estimation results. For the bounding box regression, the AP and AR are 79.1% and 63.2%, respectively, while the AP and AR of mask segmentation are 78.1% and 62.1%, and the AP and AR of the pose estimation achieve 67.1% and 55.7%, respectively. The experiments demonstrate that our method efficiently improves the recognition accuracy of the instrument in endoscopic images, and outperforms the other state-of-the-art methods.

Development and validation of an early pregnancy risk score for the prediction of gestational diabetes mellitus in Chinese pregnant women.

OBJECTIVE: To develop and validate a set of risk scores for the prediction of gestational diabetes mellitus (GDM) before the 15th gestational week using an established population-based prospective cohort. METHODS: From October 2010 to August 2012, 19 331 eligible pregnant women were registered in the three-tiered antenatal care network in Tianjin, China, to receive their antenatal care and a two-step GDM screening. The whole dataset was randomly divided into a training dataset (for development of the risk score) and a test dataset (for validation of performance of the risk score). Logistic regression was performed to obtain coefficients of selected predictors for GDM in the training dataset. Calibration was estimated using Hosmer-Lemeshow test, while discrimination was checked using area under the receiver operating characteristic curve (AUC) in the test dataset. RESULTS: In the training dataset (total=12 887, GDM=979 or 7.6%), two risk scores were developed, one only including predictors collected at the first antenatal care visit for early prediction of GDM, like maternal age, body mass index, height, family history of diabetes, systolic blood pressure, and alanine aminotransferase; and the other also including predictors collected during pregnancy, that is, at the time of GDM screening, like physical activity, sitting time at home, passive smoking, and weight gain, for maximum performance. In the test dataset (total=6444, GDM=506 or 7.9%), the calibrations of both risk scores were acceptable (both p for Hosmer-Lemeshow test >0.25). The AUCs of the first and second risk scores were 0.710 (95% CI: 0.680 to 0.741) and 0.712 (95% CI: 0.682 to 0.743), respectively (p for difference: 0.9273). CONCLUSION: Both developed risk scores had adequate performance for the prediction of GDM in Chinese pregnant women in Tianjin, China. Further validations are needed to evaluate their performance in other populations and using different methods to identify GDM cases.