[The impact of non-HDL-C level on major adverse cardiovascular and cerebrovascular events and all-cause mortality after revascularization]

Objective: To investigate the impact of non-high-density lipoprotein cholesterol (non-HDL-C) level on major adverse cardiovascular and cerebrovascular events (MACCE) and all-cause mortality in the Kailuan Study cohort undergoing revascularization. Methods: This is a prospective cohort study, with participants from the Kailuan Study cohort who participated in physical examinations from 2006 to 2020 and received revascularization therapy for the first time. According to the level of non-HDL-C, the study subjects were divided into 3 groups:<2.6 mmol/L group, 2.6-<3.4 mmol/L group, and≥3.4 mmol/L group. Annual follow-up was performed, and the endpoint events were MACCE and all-cause mortality. Cox proportional regression model was implemented to estimate the impact on MACCE and all-cause mortality associated with the different non-HDL-C groups. The partial distributed risk model was used to analyze the impact of different non-HDL-C levels on MACCE event subtypes, and death was regarded as a competitive event. The restricted cubic spline regression model was used to explore the dose-response relationship between non-HDL-C level and all-cause mortality, MACCE and its subtypes. Results: A total of 2 252 subjects were enrolled in the study, including 2 019 males (89.65%), aged (62.8±8.3) years, the follow-up time was 5.72 (3.18, 8.46) years. There were 384 cases(17.05%) of MACCE and 157 cases(6.97%) of all-cause mortality. Compared with patients with non-HDL-C≥3.4 mmol/L, patients with non-HDL-C<2.6 mmol/L were associated with a 38% reduced risk of MACCE after revascularization [HR=0.62(95%CI: 0.48-0.80)]. Every 1 mmol/L decrease in non-HDL-C was associated with a 20% reduction in the risk of MACCE [HR=0.80(95%CI: 0.73-0.88)]. The results of restricted cubic spline also showed that non-HDL-C levels after revascularization therapy were positively correlated with MACCE events (overall association P<0.001, non-linear association P=0.808). For all-cause mortality, compared to the non-HDL-C≥3.4 mmol/L group, the HR for all-cause mortality after revascularization in non-HDL-C<2.6 mmol/L group was 0.67(95%CI: 0.46-1.01). Every 1 mmol/L decrease in non-HDL-C was associated with a 15% reduction in the risk of all-cause mortality [HR=0.85(95%CI: 0.73-0.99)]. The restricted cubic spline results showed a linear association between non-HDL-C levels after revascularization therapy and the risk of all-cause mortality (overall association P=0.039, non-linear association P=0.174). Conclusion: The decrease in non-HDL-C levels after revascularization were significantly associated with a reduced risk of MACCE and all-cause mortality.

Modification of χ_{c1}(3872) and ψ(2S) Production in pPb Collisions at sqrt[s_{NN}]=8.16 TeV

The LHCb Collaboration measures production of the exotic hadron χ_{c1}(3872) in proton-nucleus collisions for the first time. Comparison with the charmonium state ψ(2S) suggests that the exotic χ_{c1}(3872) experiences different dynamics in the nuclear medium than conventional hadrons, and comparison with data from proton-proton collisions indicates that the presence of the nucleus may modify χ_{c1}(3872) production rates. This is the first measurement of the nuclear modification factor of an exotic hadron.

Amplitude Analysis of the B^{0}→K^{*0}μ^{+}μ^{-} Decay

An amplitude analysis of the B^{0}→K^{*0}μ^{+}μ^{-} decay is presented using a dataset corresponding to an integrated luminosity of 4.7  fb^{-1} of pp collision data collected with the LHCb experiment. For the first time, the coefficients associated to short-distance physics effects, sensitive to processes beyond the standard model, are extracted directly from the data through a q^{2}-unbinned amplitude analysis, where q^{2} is the μ^{+}μ^{-} invariant mass squared. Long-distance contributions, which originate from nonfactorizable QCD processes, are systematically investigated, and the most accurate assessment to date of their impact on the physical observables is obtained. The pattern of measured corrections to the short-distance couplings is found to be consistent with previous analyses of b- to s-quark transitions, with the largest discrepancy from the standard model predictions found to be at the level of 1.8 standard deviations. The global significance of the observed differences in the decay is 1.4 standard deviations.

Data-driven prediction of colonization outcomes for complex microbial communities

Microbial interactions can lead to different colonization outcomes of exogenous species, be they pathogenic or beneficial in nature. Predicting the colonization of exogenous species in complex communities remains a fundamental challenge in microbial ecology, mainly due to our limited knowledge of the diverse mechanisms governing microbial dynamics. Here, we propose a data-driven approach independent of any dynamics model to predict colonization outcomes of exogenous species from the baseline compositions of microbial communities. We systematically validate this approach using synthetic data, finding that machine learning models can predict not only the binary colonization outcome but also the post-invasion steady-state abundance of the invading species. Then we conduct colonization experiments for commensal gut bacteria species Enterococcus faecium and Akkermansia muciniphila in hundreds of human stool-derived in vitro microbial communities, confirming that the data-driven approaches can predict the colonization outcomes in experiments. Furthermore, we find that while most resident species are predicted to have a weak negative impact on the colonization of exogenous species, strongly interacting species could significantly alter the colonization outcomes, e.g., Enterococcus faecalis inhibits the invasion of E. faecium invasion. The presented results suggest that the data-driven approaches are powerful tools to inform the ecology and management of microbial communities.

Fraction of χ_{c} Decays in Prompt J/ψ Production Measured in pPb Collisions at sqrt[s_{NN}]=8.16 TeV

The fraction of χ_{c1} and χ_{c2} decays in the prompt J/ψ yield, F_{χ_{c}→J/ψ}=σ_{χ_{c}→J/ψ}/σ_{J/ψ}, is measured by the LHCb detector in pPb collisions at sqrt[s_{NN}]=8.16  TeV. The study covers the forward (1.5 Collapse

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Enhanced Production of Λ_{b}^{0} Baryons in High-Multiplicity pp Collisions at sqrt[s]=13 TeV

The production rate of Λ_{b}^{0} baryons relative to B^{0} mesons in pp collisions at a center-of-mass energy sqrt[s]=13  TeV is measured by the LHCb experiment. The ratio of Λ_{b}^{0} to B^{0} production cross sections shows a significant dependence on both the transverse momentum and the measured charged-particle multiplicity. At low multiplicity, the ratio measured at LHCb is consistent with the value measured in e^{+}e^{-} collisions, and increases by a factor of ∼2 with increasing multiplicity. At relatively low transverse momentum, the ratio of Λ_{b}^{0} to B^{0} cross sections is higher than what is measured in e^{+}e^{-} collisions, but converges with the e^{+}e^{-} ratio as the momentum increases. These results imply that the evolution of heavy b quarks into final-state hadrons is influenced by the density of the hadronic environment produced in the collision. Comparisons with several models and implications for the mechanisms enforcing quark confinement are discussed.

Identifying keystone species in microbial communities using deep learning

Previous studies suggested that microbial communities can harbour keystone species whose removal can cause a dramatic shift in microbiome structure and functioning. Yet, an efficient method to systematically identify keystone species in microbial communities is still lacking. Here we propose a data-driven keystone species identification (DKI) framework based on deep learning to resolve this challenge. Our key idea is to implicitly learn the assembly rules of microbial communities from a particular habitat by training a deep-learning model using microbiome samples collected from this habitat. The well-trained deep-learning model enables us to quantify the community-specific keystoneness of each species in any microbiome sample from this habitat by conducting a thought experiment on species removal. We systematically validated this DKI framework using synthetic data and applied DKI to analyse real data. We found that those taxa with high median keystoneness across different communities display strong community specificity. The presented DKI framework demonstrates the power of machine learning in tackling a fundamental problem in community ecology, paving the way for the data-driven management of complex microbial communities.

[Risks to predict blood loss and cranial nerve injury in carotid body paraganglioma resection]

Objective: To investigate clinical and imaging parameters to predict blood loss and cranial nerve injury (CNI) following carotid body paraganglioma (CBP) resection. Methods: A retrospective examination of clinical and imaging data was conducted on 63 patients who underwent CBP resection at Xiangya Hospital of Central South University from January 2016 to December 2022, including 23 males and 40 females, aged 26-87 years old. Three imaging parameters including tumor volume, the angle of contact with the internal carotid artery (ICA), and the distance to the base of skull (DTBOS) were gauged using the IMEDPACS software on CTA and MR imaging. The predictive efficacies of age, gender, Shamblin classification, and three imaging parameters for blood loss and CNI following surgery were analysed. Logistic composite parameter models were constructed and their predictive validity was assessed. Results: Multivariate logistic regression analysis underscored that only tumor volume (OR=1.381,95%CI:1.167-1.507,P=0.001) showed significant statistical correlations with blood loss following surgery. Area under curve (AUC) values of 0.910 for receiver operating characteristic (ROC) curves showed a sensitivity of 1.000 and a specificity of 0.694. Tumor volume (OR=1.126,95%CI:1.030-1.231, P=0.002) and DTBOS (OR=0.225,95%CI:0.081-0.630,P=0.005) were significantly associated with postoperative CNI. The analysis of logistic composite model showed AUC values for tumor volume, DTBOS and combination of the two parameters were 0.858, 0.788, and 0.872, respectively. The model for combination of tumor volume and DTBOS also proved superior in predicting postoperative CNI (Z=3.106, P<0.001), with a sensitivity of 0.833 and a specificity of 0.769. Conclusions: Tumor volume and DTBOS emerged as effective predictors for blood loss and/or CNI in patients with CBP resection. Moreover, the logistic composite parameter model outclassed single-parameter models in terms of their predictive clinical value.

[Disseminated nocardiosis caused by Nocardia otitidiscaviarum in an immunocompetent host: a case report]

Nocardiosis can be caused by various Nocardia spp., including Nocardia asteroides, Nocardia brasiliensis, Nocardia cyriacigeorgica, Nocardia farcinica and Nocardia otitidiscaviarum. As compared with the other Nocardia spp., Nocardia otitidiscaviarum appears to be rare which can spread through the bloodstream and affect multiple organs. The disease is usually seen in immunocompromised patients' but may also occur in immunocompetent patients. The clinical symptoms and laboratory and imaging examinations of the disease are nonspecific.Here, we reported a case of disseminated nocardiosis caused by infection with Nocardiosis otitidiscaviarum in an immunocompetent host to improve the knowledge and diagnosis of nocardiosis.

Nutritional redundancy in the human diet and its application in phenotype association studies

Studying human dietary intake may help us identify effective measures to treat or prevent many chronic diseases whose natural histories are influenced by nutritional factors. Here, by examining five cohorts with dietary intake data collected on different time scales, we show that the food intake profile varies substantially across individuals and over time, while the nutritional intake profile appears fairly stable. We refer to this phenomenon as 'nutritional redundancy' and attribute it to the nested structure of the food-nutrient network. This network enables us to quantify the level of nutritional redundancy for each diet assessment of any individual. Interestingly, this nutritional redundancy measure does not strongly correlate with any classical healthy diet scores, but its performance in predicting healthy aging shows comparable strength. Moreover, after adjusting for age, we find that a high nutritional redundancy is associated with lower risks of cardiovascular disease and type 2 diabetes.

[Secular trends of age at menarche and age at menopause in women born since 1951 from a county of Shandong Province, China]

OBJECTIVE

To describe the secular trends of age at menarche and age at natural menopause of women from a county of Shandong Province.

METHODS

Based on the data of the Premarital Medical Examination and the Cervical Cancer and Breast Cancer Screening of the county, the secular trends of age at menarche in women born in 1951 to 1998 and age at menopause in women born in 1951 to 1975 were studied. Joinpoint regression was used to identify potential inflection points regarding the trend of age at menarche. Average hazard ratios (AHR) of early menopause among women born in different generations were estimated by performing multivariate weighted Cox regression.

RESULTS

The average age at menarche was (16.43±1.89) years for women born in 1951 and (13.99±1.22) years for women born in 1998. The average age at menarche was lower for urban women than that for rural women, and the higher the education level, the lower the average age at menarche. Joinpoint regression analysis identified three inflection points: 1959, 1973 and 1993. The average age at menarche decreased annually by 0.03 (P < 0.001), 0.08 (P < 0.001), and 0.03 (P < 0.001) years respectively for women born during 1951-1959, 1960-1973, and 1974-1993, while it remained stable for those born during 1994-1998 (P=0.968). As for age at menopause, compared with women born during 1951-1960, those born during 1961-1965, 1966-1970 and 1971-1975 showed a gradual decrease in the risk of early menopause and a tendency to delay the age at menopause. The stratified analysis presented that the risk of early menopause gradually decreased and the age of menopause showed a significant delay among those with education level of junior high school and below, but this trend was not obvious among those with education level of senior high school and above, where the risk of early menopause decreased and then increased among those with education level of college and above, and the corresponding AHRs were 0.90 (0.66-1.22), 1.07 (0.79-1.44) and 1.14 (0.79-1.66).

CONCLUSION

The age at menarche for women born since 1951 gradually declined until 1994 and leveled off, with a decrease of nearly 2.5 years in these years. The age at menopause for women born between 1951 and 1975 was generally delayed over time, but the trend of first increase and then decrease was observed among those with relatively higher education levels. In the context of the increasing delay in age at marriage and childbearing and the decline of fertility, this study highlights the necessity of the assessment and monitoring of women' s basic reproductive health status, especially the risk of early menopause.

[Analysis on survival time and influencing factors among reported HIV/AIDS in Yunnan Province, 1989-2021]

Objective: To analyze the survival time of reported HIV/AIDS and influencing factors of Yunnan Province from 1989 to 2021. Methods: The data were extracted from the Chinese HIV/AIDS comprehensive response information management system. The retrospective cohort study was conducted. The life table method was applied to calculate the survival probability. Kaplan-Meier was used to draw survival curves in different situations. Furthermore, the Cox proportion hazard regression model was constructed to identify the factors related to survival time. Results: Of the 174 510 HIV/AIDS, the all-cause mortality density was 4.23 per 100 person-years, the median survival time was 20.00 (95%CI:19.52-20.48) years, and the cumulative survival rates in 1, 10, 20, and 30 years were 90.75%, 67.50%, 47.93% and 30.85%. Multivariate Cox proportional risk regression model results showed that the risk of death among 0-14 and 15-49 years old groups were 0.44 (95%CI: 0.34-0.56) times and 0.51 (95%CI:0.50-0.52) times of ≥50 years old groups. The risk for death among the first CD4⁺T lymphocytes counts (CD4) counts levels of 200-349 cells/μl, 350-500 cells/μl and ≥501 cells/μl groups were 0.52 (95%CI: 0.50-0.53) times, 0.41 (95%CI: 0.40-0.42) times and 0.35 (95%CI: 0.34-0.36) times of 0-199 cells/μl groups. The risk of death among the cases that have not received antiretroviral therapy (ART) was 11.56 (95%CI: 11.26-11.87) times. The risk for death among the cases losing to ART, stopping to ART, both losing and stopping ART was 1.66 (95%CI:1.61-1.72) times, 2.49 (95%CI:2.39-2.60) times, and 1.65 (95%CI:1.53-1.78) times of the cases on ART. Conclusions: The influencing factors for the survival time of HIV/AIDS cases were age at diagnosis in Yunnan province from 1989 to 2021. The first CD4 counts levels, antiretroviral therapy, and ART compliance. Early diagnosis, early antiretroviral therapy, and increasing ART compliance could extend the survival time of HIV/AIDS cases.

[Response characteristics of tislelizumab combined with chemotherapy in first-line treatment of locally advanced or metastatic non-squamous non-small cell lung cancer]

Objective: To investigate the response characteristics of patients with locally advanced/metastatic non-squamous non-small cell lung cancer (nsq-NSCLC) treated with tislelizumab in combination with chemotherapy in the first line. Methods: Patients with nsq-NSCLC who achieved complete or partial remission after treatment with tislelizumab in combination with chemotherapy or chemotherapy alone in the RATIONALE 304 study, as assessed by an independent review board, were selected to analyze the response characteristics and safety profile of the responders. Time to response (TTR) was defined as the time from randomization to the achievement of first objective response. Depth of response (DpR) was defined as the maximum percentage of tumor shrinkage compared with the sum of the baseline target lesion length diameters. Results: As of January 23, 2020, 128 patients treated with tislelizumab in combination with chemotherapy achieved objective tumor response (responders), representing 57.4%(128/223) of the intention-to-treat population, with a TTR of 5.1 to 33.3 weeks and a median TTR of 7.9 weeks. Of the responders (128), 50.8%(65) achieved first remission at the first efficacy assessment (week 6), 31.3%(40) at the second efficacy assessment (week 12), and 18.0%(23) at the third and subsequent tumor assessments. The percentages of responders who achieved a depth of tumor response of 30% to <50%, 50% to <70% and 70% to 100% were 45.3%(58/128), 28.1%(36/128) and 26.6%(34/128), respectively, with median progression-free survival (PFS) of 9.0 months (95% CI: 7.7 to 9.9 months), 11.5 months (95% CI: 7.7 months to not reached) and not reached (95% CI: 11.8 months to not estimable), respectively. Tislelizumab plus chemotherapy were generally well tolerated in responders with similar safety profile to the overall safety population. Conclusion: Among responders to tislelizumab in combination with chemotherapy for nsq-NSCLC, 82.0%(105/128) achieves response within the first two tumor assessments (12 weeks) and 18.0%(23/128) achieves response at later (18 to 33 weeks) assessments, and there is a trend toward prolonged PFS in responders with deeper tumor response.

Data-driven prediction of colonization outcomes for complex microbial communities

Complex microbial interactions can lead to different colonization outcomes of exogenous species, be they pathogenic or beneficial in nature. Predicting the colonization of exogenous species in complex communities remains a fundamental challenge in microbial ecology, mainly due to our limited knowledge of the diverse physical, biochemical, and ecological processes governing microbial dynamics. Here, we proposed a data-driven approach independent of any dynamics model to predict colonization outcomes of exogenous species from the baseline compositions of microbial communities. We systematically validated this approach using synthetic data, finding that machine learning models (including Random Forest and neural ODE) can predict not only the binary colonization outcome but also the post-invasion steady-state abundance of the invading species. Then we conducted colonization experiments for two commensal gut bacteria species Enterococcus faecium and Akkermansia muciniphila in hundreds of human stool-derived in vitro microbial communities, confirming that the data-driven approach can successfully predict the colonization outcomes. Furthermore, we found that while most resident species were predicted to have a weak negative impact on the colonization of exogenous species, strongly interacting species could significantly alter the colonization outcomes, e.g., the presence of Enterococcus faecalis inhibits the invasion of E. faecium . The presented results suggest that the data-driven approach is a powerful tool to inform the ecology and management of complex microbial communities.

Identifying chronic obstructive pulmonary disease from integrative omics and clustering in lung tissue

BACKGROUND

Chronic obstructive pulmonary disease (COPD) is a highly morbid and heterogenous disease. While COPD is defined by spirometry, many COPD characteristics are seen in cigarette smokers with normal spirometry. The extent to which COPD and COPD heterogeneity is captured in omics of lung tissue is not known.

METHODS

We clustered gene expression and methylation data in 78 lung tissue samples from former smokers with normal lung function or severe COPD. We applied two integrative omics clustering methods: (1) Similarity Network Fusion (SNF) and (2) Entropy-Based Consensus Clustering (ECC).

RESULTS

SNF clusters were not significantly different by the percentage of COPD cases (48.8% vs. 68.6%, p = 0.13), though were different according to median forced expiratory volume in one second (FEV1) % predicted (82 vs. 31, p = 0.017). In contrast, the ECC clusters showed stronger evidence of separation by COPD case status (48.2% vs. 81.8%, p = 0.013) and similar stratification by median FEV1% predicted (82 vs. 30.5, p = 0.0059). ECC clusters using both gene expression and methylation were identical to the ECC clustering solution generated using methylation data alone. Both methods selected clusters with differentially expressed transcripts enriched for interleukin signaling and immunoregulatory interactions between lymphoid and non-lymphoid cells.

CONCLUSIONS

Unsupervised clustering analysis from integrated gene expression and methylation data in lung tissue resulted in clusters with modest concordance with COPD, though were enriched in pathways potentially contributing to COPD-related pathology and heterogeneity.

Identifying keystone species in microbial communities using deep learning

Previous studies suggested that microbial communities harbor keystone species whose removal can cause a dramatic shift in microbiome structure and functioning. Yet, an efficient method to systematically identify keystone species in microbial communities is still lacking. This is mainly due to our limited knowledge of microbial dynamics and the experimental and ethical difficulties of manipulating microbial communities. Here, we propose a Data-driven Keystone species Identification (DKI) framework based on deep learning to resolve this challenge. Our key idea is to implicitly learn the assembly rules of microbial communities from a particular habitat by training a deep learning model using microbiome samples collected from this habitat. The well-trained deep learning model enables us to quantify the community-specific keystoneness of each species in any microbiome sample from this habitat by conducting a thought experiment on species removal. We systematically validated this DKI framework using synthetic data generated from a classical population dynamics model in community ecology. We then applied DKI to analyze human gut, oral microbiome, soil, and coral microbiome data. We found that those taxa with high median keystoneness across different communities display strong community specificity, and many of them have been reported as keystone taxa in literature. The presented DKI framework demonstrates the power of machine learning in tackling a fundamental problem in community ecology, paving the way for the data-driven management of complex microbial communities.

Assessment of community efforts to advance network-based prediction of protein-protein interactions

Comprehensive understanding of the human protein-protein interaction (PPI) network, aka the human interactome, can provide important insights into the molecular mechanisms of complex biological processes and diseases. Despite the remarkable experimental efforts undertaken to date to determine the structure of the human interactome, many PPIs remain unmapped. Computational approaches, especially network-based methods, can facilitate the identification of previously uncharacterized PPIs. Many such methods have been proposed. Yet, a systematic evaluation of existing network-based methods in predicting PPIs is still lacking. Here, we report community efforts initiated by the International Network Medicine Consortium to benchmark the ability of 26 representative network-based methods to predict PPIs across six different interactomes of four different organisms: A. thaliana, C. elegans, S. cerevisiae, and H. sapiens. Through extensive computational and experimental validations, we found that advanced similarity-based methods, which leverage the underlying network characteristics of PPIs, show superior performance over other general link prediction methods in the interactomes we considered.

Predicting metabolomic profiles from microbial composition through neural ordinary differential equations

Characterizing the metabolic profile of a microbial community is crucial for understanding its biological function and its impact on the host or environment. Metabolomics experiments directly measuring these profiles are difficult and expensive, while sequencing methods quantifying the species composition of microbial communities are well-developed and relatively cost-effective. Computational methods that are capable of predicting metabolomic profiles from microbial compositions can save considerable efforts needed for metabolomic profiling experimentally. Yet, despite existing efforts, we still lack a computational method with high prediction power, general applicability, and great interpretability. Here we develop a method - mNODE (Metabolomic profile predictor using Neural Ordinary Differential Equations), based on a state-of-the-art family of deep neural network models. We show compelling evidence that mNODE outperforms existing methods in predicting the metabolomic profiles of human microbiomes and several environmental microbiomes. Moreover, in the case of human gut microbiomes, mNODE can naturally incorporate dietary information to further enhance the prediction of metabolomic profiles. Besides, susceptibility analysis of mNODE enables us to reveal microbe-metabolite interactions, which can be validated using both synthetic and real data. The presented results demonstrate that mNODE is a powerful tool to investigate the microbiome-diet-metabolome relationship, facilitating future research on precision nutrition.

Benchmarking omics-based prediction of asthma development in children

BACKGROUND

Asthma is a heterogeneous disease with high morbidity. Advancement in high-throughput multi-omics approaches has enabled the collection of molecular assessments at different layers, providing a complementary perspective of complex diseases. Numerous computational methods have been developed for the omics-based patient classification or disease outcome prediction. Yet, a systematic benchmarking of those methods using various combinations of omics data for the prediction of asthma development is still lacking.

OBJECTIVE

We aimed to investigate the computational methods in disease status prediction using multi-omics data.

METHOD

We systematically benchmarked 18 computational methods using all the 63 combinations of six omics data (GWAS, miRNA, mRNA, microbiome, metabolome, DNA methylation) collected in The Vitamin D Antenatal Asthma Reduction Trial (VDAART) cohort. We evaluated each method using standard performance metrics for each of the 63 omics combinations.

RESULTS

Our results indicate that overall Logistic Regression, Multi-Layer Perceptron, and MOGONET display superior performance, and the combination of transcriptional, genomic and microbiome data achieves the best prediction. Moreover, we find that including the clinical data can further improve the prediction performance for some but not all the omics combinations.

CONCLUSIONS

Specific omics combinations can reach the optimal prediction of asthma development in children. And certain computational methods showed superior performance than other methods.

[Application of robot-assisted laparoscopic sentinel lymph node tracing in treating endometrial carcinoma]

Objective: To investigate the value of robot-assisted laparoscopic indocyanine green sentinel lymph node (SLN) tracing in treating endometrial carcinoma. Methods: Thirty-two patients with early-staging endometrial carcinoma were operated with laparoscopic comprehensive staging laparotomy from January 2019 to December 2021. At the same time, the SLN detection was performed by near-infrared fluorescence imaging tracer technology, in which the tracer was indocyanine green. Sixteen cases were injected with indocyanine green before laparoscopic surgery, and 16 cases were injected with indocyanine green before robot-assisted laparoscopic surgery. The operation index, postoperative complications, prognosis, and lymph node dissection were compared between the two groups. Results: (1) The mean age of patients in the robot group was (54.7±8.1) years old, and was (54.9±8.8) years old in the laparoscopic group. There were no significant difference between the two groups (t=0.06, P=0.951). (2) Intraoperative blood loss [(131±40) vs (169±57) ml], hemoglobin difference before and after surgery [(11.2±5.4) vs (15.5±5.7) g/L], the length of stay after operation [(6.2±1.3) vs (8.6±1.4) days] between the robot group and the laparoscopic group were compared, and the differences were statistically significant (all P<0.05). (3) SLNs were detected in all 16 patients in the robotic group, and a total of 41 SLNs were detected. SLNs were detected in 15 of the 16 patients in the laparoscopy group, and a total of 40 SLNs were detected. Compared with the laparoscopic group (15/16), the total detection rate of SLN in the robotic group (16/16), there were no statistical significance (χ²=1.03, P=0.310). Compared with the laparoscopic group (7/15), the SLN bilateral detection rate in the robotic group (10/16), there were also no significant difference (χ²=0.78, P=0.376). The number of lymph nodes detected in surgery group (16.6±4.1) were lower than those in the laparoscopy surgery group (21.0±7.1), while there were no statistically difference between the two groups (χ²=2.01, P=0.054). There was no tumor metastasis in the resected lymph nodes and SLN between the two groups. The false negative rate of SLN in diagnosing endometrial cancer postoperative lymph node metastasis was 0, and the negative predictive value was 100%. (4) The pelvic and retroperitoneal lymph nodes were divided into five regions, which were the left pelvis, the right pelvis, the presacral region, the deep inguinal region, and the abdominal aorta. The numbers of SLN of unilateral detection and bilateral pelvic detection between two groups showed no significant differences (all P>0.05). The left pelvis had the most SLN imaging in both groups, followed by the right pelvis, para-aortic, and deep groin. (5) There was one patient in both robotic group and laparoscopic group with postoperative complications, which were urinary retention and pelvic lymph node cyst respectively. There were no significant differences in the incidence of complications between the two groups (χ²=0.97, P=1.000). The median follow-up time after operation was 14 months (range 6-24 months). During the follow-up period, no local recurrence or distant metastasis was found between the two groups of endometrial cancer patients. Conclusions: Compared with the laparoscopic group, the robot group has less intraoperative blood loss and shorter postoperative hospital stay. The bilateral detection rate of SLN in the group was better than that of laparoscopy.

A statistical physics approach for disease module detection

Extensive evidence indicates that the pathobiological processes of a complex disease are associated with perturbation in specific neighborhoods of the human protein-protein interaction (PPI) network (also known as the interactome), often referred to as the disease module. Many computational methods have been developed to integrate the interactome and omics profiles to extract context-dependent disease modules. Yet, existing methods all have fundamental limitations in terms of rigor and/or efficiency. Here, we developed a statistical physics approach based on the random-field Ising model (RFIM) for disease module detection, which is both mathematically rigorous and computationally efficient. We applied our RFIM approach to genome-wide association studies (GWAS) of ten complex diseases to examine its performance for disease module detection. We found that our RFIM approach outperforms existing methods in terms of computational efficiency, connectivity of disease modules, and robustness to the interactome incompleteness.

[Safety and feasibility of overlapped delta-shaped anastomosis technique for digestive tract reconstruction during complete laparoscopic right hemicolectomy]

Objective: To explore the clinical safety and feasibility of overlapped delta-shaped anastomosis (ODA) in totally laparoscopic right hemicolectomy (TLRHC). Methods: From May 2017 to October 2019, of the 219 patients who underwent TLRHC at the Cancer Hospital, Chinese Academy of Medical Science and Peking Union Medical College, 104 cases underwent ODA (ODA group) and 115 cases underwent conventional extracorporeal anastomosis (control group) were compared the surgical outcomes, postoperative recovery, pathological outcomes and perioperative complications. Results: The length of the skin incision in the ODA group was significantly shorter than that in the control group [(5.6±0.9) cm vs. (7.1±1.7) cm, P<0.05], and the time to first flatus and first defecation after surgery in the ODA group was significantly earlier than that in the control group [(1.7±0.7) days vs. (2.0±0.7) days; (3.2±0.6) days vs. (3.3±0.7) days, P<0.05]. While the anastomosis time, operation time, intraoperative blood loss, the time of first ground activities, the number of bowel movements within 12 days after surgery, postoperative hospital stay, tumor size, the distal and proximal margins, the number of lymph node harvested and postoperative TNM stage in the ODA group did not differ from that of the control group (P>0.05). The postoperative complication rates of patients in the ODA group and the control group were 3.8% (4/104) and 4.3% (5/115), respectively, and the difference was not significant (P>0.05). Conclusion: The application of ODA technology in TLRHC can significantly shorten thelength of skin incisionand the recovery time of bowel function, and can obtain satisfactory short-term efficacy.