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The Laparoscopic Approach to Cervical Cancer (LACC) trial was published in 2018 and demonstrated that minimally invasive surgery (MIS) yields inferior survival outcomes in early-stage cervical cancer compared to open surgery. This study investigates how the results of the LACC trial have impacted the selection of the primary treatment modality and adjuvant radiation utilization in early-stage cervical cancer. Using the National Cancer Database (NCDB), we compared patients with stage IA2-IB1 cervical cancer before (1/2016–12/2017) and after (1/2019–12/2020) the LACC trial. A total of 7930 patients were included: 4609 before and 3321 after the LACC trial. There was a decline in MIS usage from 67% pre-LACC to 35% thereafter (p < 0.001). In both the pre- and post-LACC periods, patients undergoing radical MIS more frequently had small volume disease (pre-LACC tumors ≤ 2 cm, 48% MIS vs. 41% open, p = 0.023; post-LACC stage IA2, 22% vs. 15%, p = 0.002). Pre-LACC, MIS radical hysterectomy was associated with White race (82% vs. 77%, p = 0.001) and private insurance (63% vs. 54%, p = 0.004), while there was no difference in socioeconomic factors in the post-LACC period. Although the proportion of patients treated with primary chemoradiation remained stable, the post-LACC cohort had a younger median age (52.47 vs. 56.37, p = 0.005) and more microscopic disease cases (13% vs. 5.4%, p = 0.002). There was no difference in the rate of radiation after radical hysterectomy before and after the trial (26% vs. 24%, p = 0.3). Conclusions: Post-LACC, patients were less likely to undergo MIS but received adjuvant radiation at similar rates, and primary chemoradiation patients were younger and more likely to have microscopic disease.

Recent advancements in mobile health (mHealth) technology and the ubiquity of wearable devices and smartphones have expanded a market for digital health and have emerged as innovative tools for data collection on individualized behavior. Heterogeneous levels of device usage across users and across days within a single user may result in different degrees of underestimation in passive sensing data, subsequently introducing biases if analyzed without addressing this issue. In this work, we propose an unsupervised 2-Stage Pre-processing Algorithm for Passively Sensed mHealth Data (2SpamH) algorithm that uses device usage variables to infer the quality of passive sensing data from mobile devices. This article provides a series of simulation studies to show the utility of the proposed algorithm compared to existing methods. Application to a real clinical dataset is also illustrated.

ObjectivesBamboo is a globally significant plant with ecological, environmental, and economic bene-fits. Choosing suitable native tree species for mixed planting in bamboo forests is an effective measure for achieving both ecological and economic benefits of bamboo forests. However, little is currently known about the impact of bamboo forests on nitrogen cycling and utilization efficiency after mixing with other tree species. Therefore, our study aims to compare the nitrogen cycling in pure bamboo forests with that in mixed forests.MethodsThrough field experiments, we investigated pure Qiongzhuea tumidinoda forests and Q. tumidinoda-Phellodendron chinense mixed forests, and utilized 15N tracing technology to explore the fertilization effects and fate of urea-15N in different forest stands.ResultsThe results demonstrated the following: 1) in both forest stands, bamboo culms account for the highest biomass percentage (42.99%-51.86%), while the leaves exhibited the highest nitrogen concentration and total nitrogen uptake (39.25%-44.52%/29.51%-33.21%, respectively) Additionally, the average nitrogen uptake rate of one-year-old bamboo is higher (0.25 mg kg-1 a-1) compared to other age groups. 2) the urea-15N absorption in mixed forests (1066.51–1141.61 g ha-1, including 949.65–1000.07 g ha-1 for bamboo and 116.86–141.54 g ha-1 for trees) was significantly higher than that in pure forests (663.93–727.62 g ha-1, P

Halide perovskite materials have broad prospects for applications in various fields such as solar cells, LED devices, photodetectors, fluorescence labeling, bioimaging, and photocatalysis due to their bandgap characteristics. This study compiled experimental data from the published literature and utilized the excellent predictive capabilities, low overfitting risk, and strong robustness of ensemble learning models to analyze the bandgaps of halide perovskite compounds. The results demonstrate the effectiveness of ensemble learning decision tree models, especially the gradient boosting decision tree model, with a root mean square error of 0.090 eV, a mean absolute error of 0.053 eV, and a determination coefficient of 93.11%. Research on data related to ratios calculated through element molar quantity normalization indicates significant influences of ions at the X and B positions on the bandgap. Additionally, doping with iodine atoms can effectively reduce the intrinsic bandgap, while hybridization of the s and p orbitals of tin atoms can also decrease the bandgap. The accuracy of the model is validated by predicting the bandgap of the photovoltaic material MASn1−xPbxI3. In conclusion, this study emphasizes the positive impact of machine learning on material development, especially in predicting the bandgaps of halide perovskite compounds, where ensemble learning methods demonstrate significant advantages.

Abstract Background A significant number of late middle-aged adults with depression have a high illness burden resulting from chronic conditions which put them at high risk of hospitalization. Many late middle-aged adults are covered by commercial health insurance, but such insurance claims have not been used to identify the risk of hospitalization in individuals with depression. In the present study, we developed and validated a non-proprietary model to identify late middle-aged adults with depression at risk for hospitalization, using machine learning methods. Methods This retrospective cohort study involved 71,682 commercially insured older adults aged 55–64 years diagnosed with depression. National health insurance claims were used to capture demographics, health care utilization, and health status during the base year. Health status was captured using 70 chronic health conditions, and 46 mental health conditions. The outcomes were 1- and 2-year preventable hospitalization. For each of our two outcomes, we evaluated seven modelling approaches: four prediction models utilized logistic regression with different combinations of predictors to evaluate the relative contribution of each group of variables, and three prediction models utilized machine learning approaches - logistic regression with LASSO penalty, random forests (RF), and gradient boosting machine (GBM). Results Our predictive model for 1-year hospitalization achieved an AUC of 0.803, with a sensitivity of 72% and a specificity of 76% under the optimum threshold of 0.463, and our predictive model for 2-year hospitalization achieved an AUC of 0.793, with a sensitivity of 76% and a specificity of 71% under the optimum threshold of 0.452. For predicting both 1-year and 2-year risk of preventable hospitalization, our best performing models utilized the machine learning approach of logistic regression with LASSO penalty which outperformed more black-box machine learning models like RF and GBM. Conclusions Our study demonstrates the feasibility of identifying depressed middle-aged adults at higher risk of future hospitalization due to burden of chronic illnesses using basic demographic information and diagnosis codes recorded in health insurance claims. Identifying this population may assist health care planners in developing effective screening strategies and management approaches and in efficient allocation of public healthcare resources as this population transitions to publicly funded healthcare programs, e.g., Medicare in the US.

Qiongzhuea tumidinoda stands out as an endemic bamboo species of significant conservation importance in Southwest China, particularly in the upper reaches of the Yangtze River. It holds a pivotal role in poverty alleviation through the commercialization of its wood and bamboo shoots. However, the suitable area of this species is undergoing rapid changes due to climate change, resulting in species redistribution and potential losses for bamboo farmers. We utilized 209 presence records and 25 environmental variables from 1987 to 2012 to predict the potentially suitable habitats for Q. tumidinoda using MaxEnt (version 3.4.1), ArcGIS (version 10.8.2), and R (4.3.3). We rigorously screened the recorded data for reliability and accuracy through expert consultations and observer interviews. We performed pre-processing to select the variables with high contributions for modeling, and 11 variables were selected for the final modeling. Our findings reveal that the top three most influential variables associated with Q. tumidinoda’s distribution were the mean monthly potential evapotranspiration (Pet), annual range of air temperatures (Bio7), and mean diurnal air temperature range (Bio2), and the rates of contributions from 1987 to 2012 were 4.8333, 3.5833, and 1.7000. There was a southeastward shift and an elevation increase in the potentially suitable habitats for Q. tumidinoda. The area of potentially suitable habitats in the study region exhibited fluctuating growth, expanding from 3063.42 km2 to 7054.38 km2. The mean monthly potential evapotranspiration (Pet) emerged as a critical determinant shaping the distribution of potentially suitable habitats for Q. tumidinoda. Our study sheds light on the response of Q. tumidinoda to climate change, offering valuable insights for the development and management of plantation industries associated with this species. In the future, to enhance prediction accuracy, researchers could equally consider both organic and inorganic environmental variables. For better preservation of environment and development, Q. tumidinoda could be introduced into nature restoration projects in areas with a suitable habitat or as a commodity that participates in forest carbon sink trading.

Abstract While it is known that social deprivation index (SDI) plays an important role on risk for acquiring Coronavirus Disease 2019 (COVID-19), the impact of SDI on in-hospital outcomes such as intubation and mortality are less well-characterized. We analyzed electronic health record data of adults hospitalized with confirmed COVID-19 between March 1, 2020 and February 8, 2021 from the INSIGHT Clinical Research Network (CRN). To compute the SDI (exposure variable), we linked clinical data using patient’s residential zip-code with social data at zip-code tabulation area. SDI is a composite of seven socioeconomic characteristics determinants at the zip-code level. For this analysis, we categorized SDI into quintiles. The two outcomes of interest were in-hospital intubation and mortality. For each outcome, we examined logistic regression and random forests to determine incremental value of SDI in predicting outcomes. We studied 30,016 included COVID-19 patients. In a logistic regression model for intubation, a model including demographics, comorbidity, and vitals had an Area under the receiver operating characteristic curve (AUROC) = 0.73 (95% CI 0.70–0.75); the addition of SDI did not improve prediction [AUROC = 0.73 (95% CI 0.71–0.75)]. In a logistic regression model for in-hospital mortality, demographics, comorbidity, and vitals had an AUROC = 0.80 (95% CI 0.79–0.82); the addition of SDI in Model 2 did not improve prediction [AUROC = 0.81 (95% CI 0.79–0.82)]. Random forests revealed similar findings. SDI did not provide incremental improvement in predicting in-hospital intubation or mortality. SDI plays an important role on who acquires COVID-19 and its severity; but once hospitalized, SDI appears less important.

Thorium monocarbide (ThC) is interesting as an alternative fertile material to be used in nuclear breeder systems and thorium molten salt reactors because of its high thermal conductivity, good irradiation performance, and wide homogeneous composition range. Here, the influence of carbon vacancy site and concentration on lattice distortions in non-stoichiometric ThC1−x (x = 0, 0.03125, 0.0625, 0.125, 0.1875, 0.25, or 0.3125) is systematically investigated using first-principle calculations by the projector augmented wave (PAW) method. The energy, mechanical parameters, and thermodynamic properties of the ThC1-x system are calculated. The results show that vacancy disordering has little influence on the total energy of the system at a constant carbon vacancy concentration using the random substitution method. As the concentration of carbon vacancies increases, significant lattice distortion occurs, leading to poor structural stability in ThC1−x systems. The changes in lattice constant and volume indicate that ThC0.75 and ThC0.96875 represent the boundaries between two-phase and single-phase regions, which is consistent with our experiments. Furthermore, the structural phase of ThC1−x (x = 0.25–0.3125) transforms from a cubic to a tetragonal structure due to its ‘over-deficient’ composition. In addition, the elastic moduli, Poisson’s ratio, Zener anisotropic factor, and Debye temperature of ThC1-x approximately exhibit a linear downward trend as x increases. The thermal expansion coefficient of ThC1−x (x = 0–0.3125) exhibits an obvious ‘size effect’ and follows the same trend at high temperatures, except for x = 0.03125. Heat capacity and Helmholtz free energy were also calculated using the Debye model; the results showed the C vacancy defect has the greatest influence on non-stoichiometric ThC1−x. Our results can serve as a theoretical basis for studying the radiation damage behavior of ThC and other thorium-based nuclear fuels in reactors.

Pamiparib, an investigational Poly (ADP-ribose) polymerase (PARP) inhibitor in clinical development, demonstrates excellent selectivity for both PARP1 and PARP2, and superb anti-proliferation activities in tumor cell lines with BRCA1/2 mutations or HR pathway deficiency (HRD). Pamiparib has good bioavailability and is 16-fold more potent than olaparib in an efficacy study using BRCA1 mutated MDA-MB-436 breast cancer xenograft model. Pamiparib also shows strong anti-tumor synergy with temozolomide (TMZ), a DNA alkylating agent used to treat brain tumors. Compared to other PARP inhibitors, pamiparib demonstrated improved penetration across the blood brain barrier (BBB) in mice. Oral administration of pamiparib at a dose as low as 3 mg/kg is sufficient to abrogate PARylation in brain tumor tissues. In SCLC-derived, TMZ-resistant H209 intracranial xenograft model, combination of pamiparib with TMZ overcomes its resistance and shows significant tumor inhibitory effects and prolonged life span. Our data suggests that combination of pamiparib with TMZ has unique potential for treatment of brain tumors. Currently, the combination therapy of pamiparib with TMZ is evaluated in clinical trial [NCT03150862].

Purpose: To establish the time course of the subjective visual function changes during the first month of orthokeratology treatment in myopic children, and to investigate how the time course variations are associated with the objective optical quality changes and the axial length growth (ALG) after 1 year of treatment.Methods: A total of 58 myopic children aged from 8 to 16 years participated in this self-controlled prospective study. All subjects were fitted with designed spherical four-zone orthokeratology lenses. Subjective visual function was evaluated with orientation discrimination threshold (ODT), and objective optical quality was quantified with the high-order aberration root-mean-square (HOA-RMS) and the changing speed of HOA. The measurements were done before the lens fitting and 1 day, 1-, 2-, and 4-weeks after lens wear. Axial length was obtained at baseline and 1-year follow-up, and ALG was defined as the difference. One-way ANOVA was conducted to compare the difference for statistical analysis.Results: After lens fitting, the ODT time courses peaked on day 1 in 28 children, 1 week in 15 children, 2 weeks in 11 children, and 4 weeks in 4 children. In contrast, the HOA-RMS steadily rose during the first month, and the changing speed of HOA was only transiently elevated on day 1 after the initial lens wear. The ALG was 0.12 ± 0.20 mm in subjects whose ODT peaked at day 1, 0.08 ± 0.09 mm in subjects whose ODT peaked on 1-week, and 0.12 ± 0.15 mm in subjects whose ODT peaked on 2-week or later. There was no difference in axial growth among the subjects whose ODT peaked at different days (P = 0.734).Conclusion: While half ODT time course resembled the changing speed of HOA with a transient elevation on day 1, about a quarter of the ODT time course resemble the steadily rising of HOA-RMS, and the rest was located in the middle. The ALGs in children with different types of ODT time courses were similar.

ObjectivesThere is limited evidence on how clinical outcomes differ by socioeconomic conditions among patients with coronavirus disease 2019 (COVID-19). Most studies focused on COVID-19 patients from a single hospital. Results based on patients from multiple health systems have not been reported. The objective of this study is to examine variation in patient characteristics, outcomes, and healthcare utilization by neighborhood social conditions among COVID-19 patients.MethodsWe extracted electronic health record data for 23,300 community dwelling COVID-19 patients in New York City between March 1st and June 11th, 2020 from all care settings, including hospitalized patients, patients who presented to the emergency department without hospitalization, and patients with ambulatory visits only. Zip Code Tabulation Area-level social conditions were measured by the Social Deprivation Index (SDI). Using logistic regressions and Cox proportional-hazards models, we examined the association between SDI quintiles and hospitalization and death, controlling for race, ethnicity, and other patient characteristics.ResultsAmong 23,300 community dwelling COVID-19 patients, 60.7% were from neighborhoods with disadvantaged social conditions (top SDI quintile), although these neighborhoods only account for 34% of overall population. Compared to socially advantaged patients (bottom SDI quintile), socially disadvantaged patients (top SDI quintile) were older (median age 55 vs. 53, PConclusionSubstantial socioeconomic disparities in health outcomes exist among COVID-19 patients in NYC. Disadvantaged neighborhood social conditions were associated with higher risk for hospitalization, severity of disease, and death.

ObjectivesEarly hospital readmissions or deaths are key healthcare quality measures in pay-for-performance programs. Predictive models could identify patients at higher risk of readmission or death and target interventions. However, existing models usually do not incorporate social determinants of health (SDH) information, although this information is of great importance to address health disparities related to social risk factors. The objective of this study is to examine the impact of social determinants of health on predictive models for potentially avoidable 30-day readmission.MethodsWe extracted electronic health record data for 19,941 hospital admissions between January 2015 and November 2017 at an academic medical center in New York City. We applied the Simplified HOSPITAL score model to predict potentially avoidable 30-day readmission or death and examined if incorporating individual- and community-level SDH could improve the prediction using cross-validation. We calculated the C-statistic for discrimination, Brier score for accuracy, and Hosmer-Lemeshow test for calibration for each model using logistic regression. Analysis was conducted for all patients and three subgroups that may be disproportionately affected by social risk factors, namely Medicaid patients, patients who are 65 or older, and obese patients.ResultsThe Simplified HOSPITAL score model achieved similar performance in our sample compared to previous studies. Adding SDH did not improve the prediction among all patients. However, adding individual- and community-level SDH at the US census tract level significantly improved the prediction for all three subgroups. Specifically, C-statistics improved from 0.70 to 0.73 for Medicaid patients, from 0.66 to 0.68 for patients 65 or older, and from 0.70 to 0.73 for obese patients.ConclusionsPatients from certain subgroups may be more likely to be affected by social risk factors. Incorporating SDH into predictive models may be helpful to identify these patients and reduce health disparities associated with vulnerable social conditions.

Abstract Background Acute respiratory failure occurs frequently in hospitalized patients and often starts before ICU admission. A risk stratification tool to predict mortality and risk for mechanical ventilation (MV) may allow for earlier evaluation and intervention. We developed and validated an automated electronic health record (EHR)-based model—Accurate Prediction of Prolonged Ventilation (APPROVE)—to identify patients at risk of death or respiratory failure requiring >= 48 h of MV. Methods This was an observational study of adults admitted to four hospitals in 2013 or a fifth hospital in 2017. Clinical data were extracted from the EHRs. The 2013 patients were randomly split 50:50 into a derivation/validation cohort. The qualifying event was death or intubation leading to MV >= 48 h. Random forest method was used in model derivation. APPROVE was calculated retrospectively whenever data were available in 2013, and prospectively every 4 h after hospital admission in 2017. The Modified Early Warning Score (MEWS) and National Early Warning Score (NEWS) were calculated at the same times as APPROVE. Clinicians were not alerted except for APPROVE in 2017cohort. Results There were 68,775 admissions in 2013 and 2258 in 2017. APPROVE had an area under the receiver operator curve of 0.87 (95% CI 0.85–0.88) in 2013 and 0.90 (95% CI 0.84–0.95) in 2017, which is significantly better than the MEWS and NEWS in 2013 but similar to the MEWS and NEWS in 2017. At a threshold of > 0.25, APPROVE had similar sensitivity and positive predictive value (PPV) (sensitivity 63% and PPV 21% in 2013 vs 64% and 16%, respectively, in 2017). Compared to APPROVE in 2013, at a threshold to achieve comparable PPV (19% at MEWS > 4 and 22% at NEWS > 6), the MEWS and NEWS had lower sensitivity (16% for MEWS and NEWS). Similarly in 2017, at a comparable sensitivity threshold (64% for APPROVE > 0.25 and 67% for MEWS and NEWS > 4), more patients who triggered an alert developed the event with APPROVE (PPV 16%) while achieving a lower false positive rate (FPR 5%) compared to the MEWS (PPV 7%, FPR 14%) and NEWS (PPV 4%, FPR 25%). Conclusions An automated EHR model to identify patients at high risk of MV or death was validated retrospectively and prospectively, and was determined to be feasible for real-time risk identification. Trial registration ClinicalTrials.gov, NCT02488174. Registered on 18 March 2015.

Abstract N‐methyl‐D‐aspartate (NMDA) receptors mediate excitatory neurotransmission in the nervous system and are preferentially inhibited by general anesthetics such as sevoflurane. Spontaneous movement is a common complication during sevoflurane anesthesia induction and seriously affects operations. In this study, we investigated the relationship between NMDA polymorphisms and spontaneous movement during sevoflurane induction. This prospective clinical study enrolled 393 patients undergoing sevoflurane anesthesia as part of their surgical routine. In the GRIN1, GRIN2A, and GRIN2B genes, 13 polymorphisms that form a heteromeric complex as part of the NMDA receptor were selected using Haploview and genotyped using matrix‐assisted laser desorption ionization–time of flight mass spectrometry MassARRAY. Both RNAfold and Genotype‐Tissue Expression portals were used to identify gene expression profiles. Our data showed that 35.8% of subjects exhibited spontaneous movement. The GRIN2A rs12918566 polymorphism was associated with spontaneous movement during sevoflurane induction. A logistic regression analysis of additive, dominant, and recessive models indicated a significant association (odds ratio [OR] (95% confidence limit [CI]): 0.58 (0.42–0.80), p = .00086; OR (95% CI): 0.51 (0.31–0.84), p = .0075, and OR (95% CI): 0.47 (0.27–0.81), p = .0060, respectively). After false discovery rate (FDR) correction, the additive model was still significant with a PFDR =0.010. Bioinformatics demonstrated that the rs12918566 genomic variation affected GRIN2A expression in brain tissue. We also revealed that GRIN2A rs12918566 was significantly associated with spontaneous movement during sevoflurane induction. We believe the NMDA receptor plays an important role in regulating the anesthetic effects of sevoflurane.

High-grade serous ovarian carcinoma (HGSOC) originates mainly from the fallopian tube (FT) epithelium and always carries early TP53 mutations. We previously reported that tumors initiate in the FT fimbria epithelium because of apoptotic failure and the expansion of cells with DNA double-strand breaks (DSB) caused by bathing of the FT epithelial cells in reactive oxygen species (ROSs) and hemoglobin-rich follicular fluid (FF) after ovulation. Because ovulation is frequent and HGSOC is rare, we hypothesized that luteal-phase progesterone (P4) could eliminate p53-defective FT cells. Here we show that P4, via P4 receptors (PRs), induces necroptosis in Trp53−/− mouse oviduct epithelium and in immortalized human p53-defective fimbrial epithelium through the TNF-α/RIPK1/RIPK3/MLKL pathway. Necroptosis occurs specifically at diestrus, recovers at the proestrus phase of the estrus cycle, and can be augmented with P4 supplementation. These results reveal the mechanism of the well-known ability of progesterone to prevent ovarian cancer.