Identification and validation of sepsis subphenotypes using time-series data.

Purpose: The recognition of sepsis as a heterogeneous syndrome necessitates identifying distinct subphenotypes to select targeted treatment. Methods: Patients with sepsis from the MIMIC-IV database (2008-2019) were randomly divided into a development cohort (80%) and an internal validation cohort (20%). Patients with sepsis from the ICU database of Peking University People's Hospital (2008-2022) were included in the external validation cohort. Time-series k-means clustering analysis and dynamic time warping was performed to develop and validate sepsis subphenotypes by analyzing the trends of 21 vital signs and laboratory indicators within 24 h after sepsis onset. Inflammatory biomarkers were compared in the ICU database of Peking University People's Hospital, whereas treatment heterogeneity was compared in the MIMIC-IV database. Findings: Three sub-phenotypes were identified in the development cohort. Type A patients (N = 2525, 47%) exhibited stable vital signs and fair organ function, type B (N = 1552, 29%) was exhibited an obvious inflammatory response and stable organ function, and type C (N = 1251, 24%) exhibited severely impaired organ function with a deteriorating tendency. Type C demonstrated the highest mortality rate (33%) and levels of inflammatory biomarkers, followed by type B (24%), whereas type A exhibited the lowest mortality rate (11%) and levels of inflammatory biomarkers. These subphenotypes were confirmed in both the internal and external cohorts, demonstrating similar features and comparable mortality rates. In type C patients, survivors had significantly lower fluid intake within 24 h after sepsis onset (median 2891 mL, interquartile range (IQR) 1530-5470 mL) than that in non-survivors (median 4342 mL, IQR 2189-7305 mL). For types B and C, survivors showed a higher proportion of indwelling central venous catheters (p < 0.05). Conclusion: Three novel phenotypes of patients with sepsis were identified and validated using time-series data, revealing significant heterogeneity in inflammatory biomarkers, treatments, and consistency across cohorts.

Machine learning-based prediction models affecting the recovery of postoperative bowel function for patients undergoing colorectal surgeries.

PURPOSE: The debate surrounding factors influencing postoperative flatus and defecation in patients undergoing colorectal resection prompted this study. Our objective was to identify independent risk factors and develop prediction models for postoperative bowel function in patients undergoing colorectal surgeries. METHODS: A retrospective analysis of medical records was conducted for patients who undergoing colorectal surgeries at Peking University People's Hospital from January 2015 to October 2021. Machine learning algorithms were employed to identify risk factors and construct prediction models for the time of the first postoperative flatus and defecation. The prediction models were evaluated using sensitivity, specificity, the Youden index, and the area under the receiver operating characteristic curve (AUC) through logistic regression, random forest, Naïve Bayes, and extreme gradient boosting algorithms. RESULTS: The study included 1358 patients for postoperative flatus timing analysis and 1430 patients for postoperative defecation timing analysis between January 2015 and December 2020 as part of the training phase. Additionally, a validation set comprised 200 patients who undergoing colorectal surgeries from January to October 2021. The logistic regression prediction model exhibited the highest AUC (0.78) for predicting the timing of the first postoperative flatus. Identified independent risk factors influencing the time of first postoperative flatus were Age (p < 0.01), oral laxatives for bowel preparation (p = 0.01), probiotics (p = 0.02), oral antibiotics for bowel preparation (p = 0.02), duration of operation (p = 0.02), postoperative fortified antibiotics (p = 0.02), and time of first postoperative feeding (p < 0.01). Furthermore, logistic regression achieved an AUC of 0.72 for predicting the time of first postoperative defecation, with age (p < 0.01), oral antibiotics for bowel preparation (p = 0.01), probiotics (p = 0.01), and time of first postoperative feeding (p < 0.01) identified as independent risk factors. CONCLUSIONS: The study suggests that he use of probiotics and early recovery of diet may enhance the recovery of bowel function in patients undergoing colorectal surgeries. Among the various analytical methods used, logistic regression emerged as the most effective approach for predicting the timing of the first postoperative flatus and defecation in this patient population.

Effectiveness and feasibility of 5G-based remote interactive ultrasound training in critical care.

BACKGROUND: Ultrasound has widely used in various medical fields related to critical care. While online and offline ultrasound trainings are faced by certain challenges, remote ultrasound based on the 5G cloud platform has been gradually adopted in many clinics. However, no study has used the 5G remote ultrasound cloud platform operating system for standardized critical care ultrasound training. This study aimed to evaluate the feasibility and effectiveness of 5G-based remote interactive ultrasound training for standardized diagnosis and treatment in critical care settings. METHODS: A 5G-based remote interactive ultrasound training system was constructed, and the course was piloted among critical care physicians. From July 2022 to July 2023, 90 critical care physicians from multiple off-site locations were enrolled and randomly divided into experimental and control groups. The 45 physicians in the experimental group were trained using the 5G-based remote interactive ultrasound training system, while the other 45 in the control group were taught using theoretical online videos. The theoretical and practical ultrasonic capabilities of both groups were evaluated before and after the training sessions, and their levels of satisfaction with the training were assessed as well. RESULTS: The total assessment scores for all of the physicians were markedly higher following the training (80.7 ± 11.9) compared to before (42.1 ± 13.4) by a statistically significant margin (P < 0.001). Before participating in the training, the experimental group scored 42.2 ± 12.5 in the critical care ultrasound competency, and the control group scored 41.9 ± 14.3-indicating no significant differences in their assessment scores (P = 0.907). After participating in the training, the experimental group's assessment scores were 88.4 ± 6.7, which were significantly higher than those of the control group (72.9 ± 10.8; P < 0.001). The satisfaction score of the experimental group was 42.6 ± 2.3, which was also significantly higher than that of the control group (34.7 ± 3.1, P < 0.001). CONCLUSION: The 5G-based remote interactive ultrasound training system was well-received and effective for critical care. These findings warrant its further promotion and application.

Prioritizing genomic variants pathogenicity via DNA, RNA, and protein-level features based on extreme gradient boosting.

Genetic diseases are mostly implicated with genetic variants, including missense, synonymous, non-sense, and copy number variants. These different kinds of variants are indicated to affect phenotypes in various ways from previous studies. It remains essential but challenging to understand the functional consequences of these genetic variants, especially the noncoding ones, due to the lack of corresponding annotations. While many computational methods have been proposed to identify the risk variants. Most of them have only curated DNA-level and protein-level annotations to predict the pathogenicity of the variants, and others have been restricted to missense variants exclusively. In this study, we have curated DNA-, RNA-, and protein-level features to discriminate disease-causing variants in both coding and noncoding regions, where the features of protein sequences and protein structures have been shown essential for analyzing missense variants in coding regions while the features related to RNA-splicing and RBP binding are significant for variants in noncoding regions and synonymous variants in coding regions. Through the integration of these features, we have formulated the Multi-level feature Genomic Variants Predictor (ML-GVP) using the gradient boosting tree. The method has been trained on more than 400,000 variants in the Sherloc-training set from the 6th critical assessment of genome interpretation with superior performance. The method is one of the two best-performing predictors on the blind test in the Sherloc assessment, and is further confirmed by another independent test dataset of de novo variants.

Preparation of Lanthanum-Modified Tea Waste Biochar and Its Adsorption Performance on Fluoride in Water.

In this study, tea waste was used as a raw material, and TBC (tea waste biochar) was prepared by pyrolysis at 700 °C. La(NO3)3·6H2O was used as the modifier to optimize one-way modification; the orthogonal experiment was undertaken to determine the optimal preparation conditions; and La-TBC (lanthanum-modified biochar) was obtained. The key factors for the adsorption of fluoride by La-TBC were investigated by means of batch adsorption experiments, and kinetics and isothermal adsorption experiments were carried out on the adsorption of fluoride in geothermal hot spring water. The adsorption mechanism of fluoride by La-TBC was analyzed via characterization methods such as SEM-EDS (Scanning Electron Microscope and Energy Dispersive Spectrometer), BET (Brunauer-Emmett-Teller), FTIR (Fourier transform infrared), XRD (X-ray diffraction), and so on. The results show that La-TBC had the best adsorption effect on fluoride at pH 7. The process of adsorption of fluoride follows the pseudo-second-order kinetics and Langmuir isothermal model, and the maximum theoretical adsorption quantity was 47.47 mg/g at 80 °C, while the removal rate of fluoride from the actual geothermal hot spring water reached more than 95%. The adsorption process was dominated by the monolayer adsorption of chemicals, and the mechanisms mainly include pore filling, ion exchange, and electrostatic interaction.

Improving water quality and mitigating CH4 and N2O production in urban landscape water simultaneously by optimizing calcium peroxide dosage.

Recent studies show that greenhouse gas (GHG) emissions from urban landscape water are significant and cannot be overlooked, underscoring the need to develop effective strategies for mitigating GHG production from global freshwater systems. Calcium peroxide (CaO2) is commonly used as an eco-friendly reagent for controlling eutrophication in water bodies, but whether CaO2 can reduce GHG emissions remains unclear. This study investigated the effects of CaO2 dosage on the production of methane (CH4) and nitrous oxide (N2O) in urban landscape water under anoxic conditions during summer. The findings reveal that CaO2 addition not only improved the physicochemical and organoleptic properties of simulated urban landscape water but also reduced N2O production by inhibiting the activity of denitrifying bacteria across various dosages. Moreover, CaO2 exhibited selective effects on methanogens. Specifically, the abundance of acetoclastic methanogen Methanosaeta and methylotrophic methanogen Candidatus_Methanofastidiosum increased whereas the abundance of the hydrogenotrophic methanogen Methanoregula decreased at low, medium, and high dosages, leading to higher CH4 production at increased CaO2 dosage. A comprehensive multi-objective evaluation indicated that an optimal dosage of 60 g CaO2/m2 achieved 41.21 % and 84.40 % reductions in CH4 and N2O production, respectively, over a 50-day period compared to the control. This paper not only introduces a novel approach for controlling the production of GHGs, such as CH4 and N2O, from urban landscape water but also suggests a methodology for optimizing CaO2 dosage, providing valuable insights for its practical application.

Control of defensive behavior by the nucleus of Darkschewitsch GABAergic neurons.

The nucleus of Darkschewitsch (ND), mainly composed of GABAergic neurons, is widely recognized as a component of the eye-movement controlling system. However, the functional contribution of ND GABAergic neurons (NDGABA) in animal behavior is largely unknown. Here, we show that NDGABA neurons were selectively activated by different types of fear stimuli, such as predator odor and foot shock. Optogenetic and chemogenetic manipulations revealed that NDGABA neurons mediate freezing behavior. Moreover, using circuit-based optogenetic and neuroanatomical tracing methods, we identified an excitatory pathway from the lateral periaqueductal gray (lPAG) to the ND that induces freezing by exciting ND inhibitory outputs to the motor-related gigantocellular reticular nucleus, ventral part (GiV). Together, these findings indicate the NDGABA population as a novel hub for controlling defensive response by relaying fearful information from the lPAG to GiV, a mechanism critical for understanding how the freezing behavior is encoded in the mammalian brain.

Risk factors and predictive model for mortality in patients undergoing allogeneic hematopoietic stem cell transplantation admitted to the intensive care unit.

Hematological malignant tumors represent a group of major diseases carrying a substantial risk to the lives of affected patients. Risk factors for mortality in critically ill patients have garnered substantial attention in recent research endeavors. The present research aimed to identify factors predicting intensive care unit (ICU) mortality in patients undergoing allogeneic hematopoietic stem cell transplantation (allo-HSCT). Furthermore, the present study analyzed and compared the mortality rate between patients undergoing haploidentical hematopoietic stem cell transplantation (Haplo-SCT) and those undergoing identical sibling donor (ISD) transplantation. A total of 108 patients were included in the present research, 83 (76.9%) of whom underwent Haplo-SCT. ICU mortality was reported in 58 (53.7%) patients, with the values of 55.4 and 48.0% associated with Haplo-SCT and ISD, respectively (P=0.514). The mortality rate of patients undergoing Haplo-SCT was comparable to that of patients undergoing ISD transplantation. The present study found that reduced hemoglobin, elevated total bilirubin, elevated brain natriuretic peptide, elevated fibrinogen degradation products, need for vasoactive drugs at ICU admission, need for invasive mechanical ventilation and elevated APACHE II scores were independent risk factors for ICU mortality. Among patients presenting with 5-7 risk factors, the ICU mortality reached 100%, significantly exceeding that of other patients. The present research revealed that ICU mortality rates remain elevated among patients who underwent allo-HSCT, especially those presenting multiple risk factors. However, the outcome of patients undergoing Haplo-SCT were comparable to those of patients undergoing ISD transplants.

Enhancing healthy aging with small molecules: A mitochondrial perspective.

The pursuit of enhanced health during aging has prompted the exploration of various strategies focused on reducing the decline associated with the aging process. A key area of this exploration is the management of mitochondrial dysfunction, a notable characteristic of aging. This review sheds light on the crucial role that small molecules play in augmenting healthy aging, particularly through influencing mitochondrial functions. Mitochondrial oxidative damage, a significant aspect of aging, can potentially be lessened through interventions such as coenzyme Q10, alpha-lipoic acid, and a variety of antioxidants. Additionally, this review discusses approaches for enhancing mitochondrial proteostasis, emphasizing the importance of mitochondrial unfolded protein response inducers like doxycycline, and agents that affect mitophagy, such as urolithin A, spermidine, trehalose, and taurine, which are vital for sustaining protein quality control. Of equal importance are methods for modulating mitochondrial energy production, which involve nicotinamide adenine dinucleotide boosters, adenosine 5'-monophosphate-activated protein kinase activators, and compounds like metformin and mitochondria-targeted tamoxifen that enhance metabolic function. Furthermore, the review delves into emerging strategies that encourage mitochondrial biogenesis. Together, these interventions present a promising avenue for addressing age-related mitochondrial degradation, thereby setting the stage for the development of innovative treatment approaches to meet this extensive challenge.

Applying image features of proximal paracancerous tissues in predicting prognosis of patients with hepatocellular carcinoma.

BACKGROUND: Most of the methods using digital pathological image for predicting Hepatocellular carcinoma (HCC) prognosis have not considered paracancerous tissue microenvironment (PTME), which are potentially important for tumour initiation and metastasis. This study aimed to identify roles of image features of PTME in predicting prognosis and tumour recurrence of HCC patients. METHODS: We collected whole slide images (WSIs) of 146 HCC patients from Sun Yat-sen Memorial Hospital (SYSM dataset). For each WSI, five types of regions of interests (ROIs) in PTME and tumours were manually annotated. These ROIs were used to construct a Lasso Cox survival model for predicting the prognosis of HCC patients. To make the model broadly useful, we established a deep learning method to automatically segment WSIs, and further used it to construct a prognosis prediction model. This model was tested by the samples of 225 HCC patients from the Cancer Genome Atlas Liver Hepatocellular Carcinoma (TCGA-LIHC). RESULTS: In predicting prognosis of the HCC patients, using the image features of manually annotated ROIs in PTME achieved C-index 0.668 in the SYSM testing dataset, which is higher than the C-index 0.648 reached by the model only using image features of tumours. Integrating ROIs of PTME and tumours achieved C-index 0.693 in the SYSM testing dataset. The model using automatically segmented ROIs of PTME and tumours achieved C-index of 0.665 (95% CI: 0.556-0.774) in the TCGA-LIHC samples, which is better than the widely used methods, WSISA (0.567), DeepGraphSurv (0.593), and SeTranSurv (0.642). Finally, we found the Texture SumAverage Skew HV on immune cell infiltration and Texture related features on desmoplastic reaction are the most important features of PTME in predicting HCC prognosis. We additionally used the model in prediction HCC recurrence for patients from SYSM-training, SYSM-testing, and TCGA-LIHC datasets, indicating the important roles of PTME in the prediction. CONCLUSIONS: Our results indicate image features of PTME is critical for improving the prognosis prediction of HCC. Moreover, the image features related with immune cell infiltration and desmoplastic reaction of PTME are the most important factors associated with prognosis of HCC.

Urine-derived stem cells: Promising advancements and applications in regenerative medicine and beyond.

Currently, stem cells are a prominent focus of regenerative engineering research. However, due to the limitations of commonly used stem cell sources, their application in therapy is often restricted to the experimental stage and constrained by ethical considerations. In contrast, urine-derived stem cells (USCs) offer promising advantages for clinical trials and applications. The noninvasive nature of the collection process allows for repeated retrieval within a short period, making it a more feasible option. Moreover, studies have shown that USCs have a protective effect on organs, promoting vascular regeneration, inhibiting oxidative stress, and reducing inflammation in various acute and chronic organ dysfunctions. The application of USCs has also been enhanced by advancements in biomaterials technology, enabling better targeting and controlled release capabilities. This review aims to summarize the current state of research on USCs, providing insights for future applications in basic and clinical settings.

Genetic evidence for T-wave area from 12-lead electrocardiograms to monitor cardiovascular diseases in patients taking diabetes medications.

Aims Many studies indicated use of diabetes medications can influence the electrocardiogram (ECG), which remains the simplest and fastest tool for assessing cardiac functions. However, few studies have explored the role of genetic factors in determining the relationship between the use of diabetes medications and ECG trace characteristics (ETC). Methods Genome-wide association studies (GWAS) were performed for 168 ETCs extracted from the 12-lead ECGs of 42,340 Europeans in the UK Biobank. The genetic correlations, causal relationships, and phenotypic relationships of these ETCs with medication usage, as well as the risk of cardiovascular diseases (CVDs), were estimated by linkage disequilibrium score regression (LDSC), Mendelian randomization (MR), and regression model, respectively. Results The GWAS identified 124 independent single nucleotide polymorphisms (SNPs) that were study-wise and genome-wide significantly associated with at least one ETC. Regression model and LDSC identified significant phenotypic and genetic correlations of T-wave area in lead aVR (aVR_T-area) with usage of diabetes medications (ATC code: A10 drugs, and metformin), and the risks of ischemic heart disease (IHD) and coronary atherosclerosis (CA). MR analyses support a putative causal effect of the use of diabetes medications on decreasing aVR_T-area, and on increasing risk of IHD and CA. ConclusionPatients taking diabetes medications are prone to have decreased aVR_T-area and an increased risk of IHD and CA. The aVR_T-area is therefore a potential ECG marker for pre-clinical prediction of IHD and CA in patients taking diabetes medications.

Phenotypes and Lung Microbiota Signatures of Immunocompromised Patients with Pneumonia-Related Acute Respiratory Distress Syndrome.

Objective: We aim to identify the clinical phenotypes of immunocompromised patients with pneumonia-related ARDS, to investigate the lung microbiota signatures and the outcomes of different phenotypes, and finally, to develop a machine learning classifier for a specified phenotype. Methods: This prospective study included immunocompromised patients with pneumonia-related ARDS. We identified phenotypes using hierarchical clustering to analyze clinical variables and serum cytokine levels. We then compared outcomes and lung microbiota signatures between phenotypes. Based on lung microbiota markers, we developed a random forest classifier for a specified phenotype with worse outcomes. Results: This study included 92 patients, who were divided into three phenotypes, namely "type α" (N = 33), "type ß" (N = 12), and "type γ" (N = 47). Compared to type α or type ß, patients with type γ had no obvious inflammatory presentation and had significantly lower IL-6 levels and more severe oxygenation failure. Type γ was also related to higher 30-day mortality and lower ventilator free days. The microbiota signatures of type γ were characterized by lower alpha diversity and distinct compositions than those of other patients. We developed a lung microbiota-derived random forest model to differentiate patients with type γ from other phenotypes. Conclusion: Immunocompromised patients with pneumonia-related ARDS can be clustered into three clinical phenotypes, namely type α, type ß, and type γ. Phenotypes were distinguished from each other with different outcomes and lung microbiota signatures. Type γ, which was characterized by insufficient inflammation response and worse outcomes, can be detected with a random forest model based on lung microbiota markers.

Flavonoids from citrus peel display potential synergistic effects on inhibiting rumen methanogenesis and ammoniagenesis: a microbiome perspective.

Flavonoids have been recognized as potential phytochemicals to reduce enteric methane (CH4) production and improve rumen nitrogen efficiency in ruminants. We evaluated whether naringin, hesperidin, their combination, or a mixed citrus flavonoid extract (CFE) as additives can inhibit methanogenesis and ammoniagenesis in dairy cows using an in vitro rumen batch refermentation system. The rumen inocula from dairy cows were incubated in batch cultures with five groups: no addition (CON), hesperidin (20 g/kg DM), naringin (20 g/kg DM), hesperidin + naringin (10 g/kg DM of hesperidin + 10 g/kg DM of naringin), and CFE (20 g/kg DM). The combination of naringin plus hesperidin and CFE achieved greater reductions in CH4 and ammonia production compared to either naringin or hesperidin alone. Microbiome analysis revealed that the decrease in CH4 emissions may have been caused by both the direct inhibitory impact of citrus flavonoids on Methanobrevibacter and a simultaneous decrease in protozoa Isotricha abundance. The relatively lower proportion of Entodinium in naringin plus hesperidin or CFE was responsible for the lower ammonia concentration. These results suggest that citrus flavonoids possess potential synergistic effects on mitigating ruminal CH4 emissions by cows and improving nitrogen utilization.

[Effect of Personality and Physical Activity on Bone Mineral Density in College Students: A Cross-sectional Survey]. / å¤§å­¦ç”Ÿäººæ ¼å’Œä½“åŠ›æ´»åŠ¨å¯¹éª¨å¯†åº¦çš„å½±å“ï¼šä¸€é¡¹æ¨ªæ–­é¢è°ƒæŸ¥.

Objective: To explore the correlation between 5 dimensions of personality, physical activity (PA), and bone mineral density (BMD) among college students. Methods: A total of 705 undergraduates (329 males and 376 females) from a sports university were recruited. Based on their sports training experience, the participants were divided into 6 major sports groups, including ball sports, skilled sports, competitive sports, track and field, leisure sports, and no sports. Students with professional sports training (ie, athletes) were categorized into ballgame, skilled, competitive, and track and field groups, while the rest (non-athletes) were placed in leisure and no sports groups. Ten-Item Personality Inventory in China (TIPI-C), or the 5-factor model of personality, was used to measure the 5 personality dimensions of openness, conscientiousness, extraversion, agreeableness, and neuroticism of the participants. Their daily level was measured with GT3X+ triaxial accelerometers over 7 continuous days. Then, parameter thresholds were established and the participants' PA was categorized as light (LPA), moderate (MPA), and vigorous (VPA). The bone mineral density (BMD) of arms, legs, and the total body was measured using dual-energy X-ray absorptiometry. The mediation effect of PA and that of the 5-factor model of personality were tested using PROCESS and Sobel tests. The correlation between the 5 personality dimensions, PA, and BMD was explored, with PA and the 5 personality dimensions as mediator variables. A comparison of PA and BMD was conducted across the 6 major sports groups. The correlation between PA of different intensities and BMD was also analyzed using Spearman's correlation. Results: Although there were 90 potential relationships between PA, the 5 personality dimensions, and BMD, only 3 were significant. When conscientiousness was used as an independent variable and MPA, as a mediating variable, statistically significant differences in PROCESS results were reported (P<0.01), with MPA mediating 17.3% of arm BMD, 19.4% of leg BMD, and 19.1% of total body BMD. Among male students, there was no significant difference in LPA among the 6 groups, but significant differences in MPA and VPA (P<0.05). Among female students, significant differences in LPA, MPA, and VPA were observed in all 6 groups and the differences between MPA and VPA were especially prominent (P<0.05). For both males and females, the differences in arm, leg, and total body BMD across the 6 groups were statistically significant (P<0.05), with these differences being more pronounced in females. There was no correlation between LPA and BMD in either sex. MPA and VPA were positively correlated with BMD, with MPA correlating with arm, leg, and total body BMD (males, Spearman's correlation [rs]: 0.11-0.14, P<0.05; females, rs: 0.20-0.23, P<0.01). VPA correlated with arm, leg, and total body BMD (males, rs: 0.11-0.23, P<0.05; females, rs: 0.26-0.30, P<0.01). Conclusion: MPA is associated with BMD in college students scoring high in the conscientiousness dimension of personality. In addition, there is a weak positive correlation between both MPA and VPA and BMD levels, with these associations being more pronounced in females.

Effect of capsular tension ring on the refractive outcomes of patients with extreme high axial myopia after phacoemulsification.

PURPOSE: The aim of the study is to evaluate the effect of capsular tension ring (CTR) implantation following cataract surgery on the refractive outcomes of patients with extreme high axial myopia. METHODS: Sixty eyes (with an axial length of ≥26 mm) were retrospectively reviewed and classified into two groups: CTR group (n = 30), which underwent CTR implantation following phacoemulsification, and control group (n = 30), which did not undergo CTR implantation. Intraocular lens (IOL) calculation was performed using Barrett Universal II (UII), Haigis, and SRK/T formulas. The refractive prediction error (PE) was calculated by subtracting the postoperative refraction from predicted refraction. The mean PE (MPE), mean absolute error (MAE), and percentages of eyes that had a PE of ±0.25, ±0.50, ±1.00, or ±2.00 diopters (D) were calculated and compared. RESULTS: No significant differences were observed in PE between the two groups. The Barrett UII formula revealed a lower AE in the CTR group than in the control group (p = 0.015) and a lower AE than the other two formulas (p = 0.0000) in both groups. The Barrett UII formula achieved the highest percentage of eyes with a PE of ±0.25 D (66.67%). CONCLUSIONS: The refractive outcomes were more accurate in eyes with CTR implantation than in those with routine phacoemulsification based on the Barrett UII formula. The Barrett UII formula was recommended as the appropriate formula when planning CTR implantation in high myopia.

An uncertainty-based interpretable deep learning framework for predicting breast cancer outcome.

BACKGROUND: Predicting outcome of breast cancer is important for selecting appropriate treatments and prolonging the survival periods of patients. Recently, different deep learning-based methods have been carefully designed for cancer outcome prediction. However, the application of these methods is still challenged by interpretability. In this study, we proposed a novel multitask deep neural network called UISNet to predict the outcome of breast cancer. The UISNet is able to interpret the importance of features for the prediction model via an uncertainty-based integrated gradients algorithm. UISNet improved the prediction by introducing prior biological pathway knowledge and utilizing patient heterogeneity information. RESULTS: The model was tested in seven public datasets of breast cancer, and showed better performance (average C-index = 0.691) than the state-of-the-art methods (average C-index = 0.650, ranged from 0.619 to 0.677). Importantly, the UISNet identified 20 genes as associated with breast cancer, among which 11 have been proven to be associated with breast cancer by previous studies, and others are novel findings of this study. CONCLUSIONS: Our proposed method is accurate and robust in predicting breast cancer outcomes, and it is an effective way to identify breast cancer-associated genes. The method codes are available at: https://github.com/chh171/UISNet .

Predicting disease-gene associations through self-supervised mutual infomax graph convolution network.

Illuminating associations between diseases and genes can help reveal the pathogenesis of syndromes and contribute to treatments, but a large number of associations remained unexplored. To identify novel disease-gene associations, many computational methods have been developed using disease and gene-related prior knowledge. However, these methods remain of relatively inferior performance due to the limited external data sources and the inevitable noise among the prior knowledge. In this study, we have developed a new method, Self-Supervised Mutual Infomax Graph Convolution Network (MiGCN), to predict disease-gene associations under the guidance of external disease-disease and gene-gene collaborative graphs. The noises within the collaborative graphs were eliminated by maximizing the mutual information between nodes and neighbors through a graphical mutual infomax layer. In parallel, the node interactions were strengthened by a novel informative message passing layer to improve the learning ability of graph neural network. The extensive experiments showed that our model achieved performance improvement over the state-of-art method by more than 8 % on AUC. The datasets, source codes and trained models of MiGCN are available at https://github.com/biomed-AI/MiGCN.