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.

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.

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.

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.

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 .

Nrf2-mediated redox balance alleviates LPS-induced vascular endothelial cell inflammation by inhibiting endothelial cell ferroptosis.

Ferroptosis plays an important role in inflammation and oxidative stress. Whether ferroptosis is involved in the inflammation of vascular endothelial cells and its regulation mechanism remains unclear. We estimated the correlation between serum iron ion levels and the inflammation index of 33 patients with arteriosclerosis. In vitro, HUVECs with or without ferrostatin-1 were exposed to Lipopolysaccharide. Corresponding cell models to verify the target signaling pathway. The results showed that serum iron ion levels had a significant positive correlation with N ratio, N/L, LDL level, and LDL/HDL (P < 0.05), and a negative correlation with L ratio (P < 0.05) in the arteriosclerosis patients. In vitro, ferroptosis is involved in HUVECs inflammation. Ferrostatin-1 can rescue LPS-induced HUVECs inflammation by decreasing HMGB1/IL-6/TNF-α expression. Nrf2 high expression could protect HUVECs against ferroptosis by activating the GPX4/GSH system, inhibiting ferritinophagy, and alleviating inflammation in HUVECs by inhibiting HMGB1/IL-6/TNF-α expression. It also found that Nrf2 is a key adaptive regulatory factor in the oxidative damage of HUVECs induced by NOX4 activation. These findings indicated that ferroptosis contributed to the pathogenesis of vascular endothelial cell damage by mediating endothelial cell inflammation. Nrf2-mediated redox balance in vascular inflammation may be a therapeutic strategy in vascular diseases.

Clinical Characteristics and Nomogram for Predicting Mortality in Patients with Postoperative Bloodstream Infection in Surgical Intensive Care Unit.

Background: Bloodstream infection is amongst the leading causes of mortality for critical postoperative patients. However, data, especially from developing countries, are scary. Clinical decision-making tools for predicting postoperative bloodstream infection-related mortality are important but still lacking. Objective: To analyze the distribution of pathogens and develop a nomogram for predicting mortality in patients with postoperative bloodstream infection in the surgical intensive care unit. Methods: The clinical data, infection and pathogen-related data, and prognosis of patients with PBSI in the SICU from January 2017 to January 2022 were retrospectively collected. The distribution of pathogens and clinical characteristics of patients with PBSI were analyzed. The patients were assigned to a died group and a survived group according to their survival status. Independent predictors for mortality were identified by univariate and multivariate analyses. A nomogram for predicting PBSI-related death was developed based on these independent predictors. Calibration and decision-curve analysis were established to evaluate the nomogram. We collected postoperative patients admitted to our center from February 2022 to June 2023 as external validation sets to verify the nomogram. We also add the Brier score to further validate the model. Results: In the training set, 7128 patients admitted to the SICU after different types of surgery were collected. A total of 198 patients and 308 pathogens were finally enrolled. The mean age of patients with PBSI was 64.38 ± 16.22 (range 18-90) years, and 56.1% were male. Forty-five patients (22.7%) died in the hospital. Five independent predictors including BMI, APACHE II score, estimated glomerular filtration rate (eGFR), urine volume in the first 24 hours after surgery, and peak temperature before positive blood cultures were selected to establish the nomogram. The area under the receiver operating characteristic curve for the prediction model was 0.922. Calibration curve and decision curve analysis showed good performance of the nomogram. Seventy patients with PBSI were collected as an external validation set, and thirteen patients died in this set. The external validation set was used to validate the nomogram, and the results showed that the AUC was 0.930 which was higher than that in the training set indicating that the nomogram had a good discrimination. The brier score was 0.087 for training set and 0.050 for validation set. Conclusions: PBSI was one of the key issues that clinicians were concerned and could be assessed with a good predictive model using simple clinical factors.

Autoimmune diseases exhibit shared alterations in the gut microbiota.

OBJECTIVE: Accumulating evidence from microbial studies have highlighted the modulatory roles of intestinal microbes in numerous human diseases, however, the shared microbial signatures across different diseases remain relatively unclear. METHODS: To consolidate existing knowledge across multiple studies, we performed meta-analyses of 17 disease types, covering 34 case-control datasets of 16S rRNA sequencing data, to identify shared alterations among different diseases. Furthermore, the impact of a microbial species, Lactobacillus salivarius, was established in a dextran sodium sulphate-induced colitis model and a collagen type II-induced arthritis mouse model. RESULTS: Microbial alterations among autoimmune diseases were substantially more consistent compared with that of other diseases (cancer, metabolic disease and nervous system disease), with microbial signatures exhibiting notable discriminative power for disease prediction. Autoimmune diseases were characterized by the enrichment of Enterococcus, Veillonella, Streptococcus and Lactobacillus and the depletion of Ruminococcus, Gemmiger, Oscillibacter, Faecalibacterium, Lachnospiracea incertae sedis, Anaerostipes, Coprococcus, Alistipes, Roseburia, Bilophila, Barnesiella, Dorea, Ruminococcus2, Butyricicoccus, Phascolarctobacterium, Parabacteroides and Odoribacter, among others. Functional investigation of L. salivarius, whose genus was commonly enriched in numerous autoimmune diseases, demonstrated protective roles in two separate inflammatory mouse models. CONCLUSION: Our study highlights a strong link between autoimmune diseases and the gut microbiota, with notably consistent microbial alterations compared with that of other diseases, indicating that therapeutic strategies that target the gut microbiome may be transferable across different autoimmune diseases. Functional validation of L. salivarius highlighted that bacterial genera associated with disease may not always be antagonistic, but may represent protective or adaptive responses to disease.

Long-term abuse of caffeine sodium benzoate induces endothelial cells injury and leads to coagulation dysfunction.

Our hospital admitted a patient who had difficulty in coagulation even after blood replacement, and the patient had abused caffeine sodium benzoate (CSB) for more than 20 years. Hence, we aimed to explore whether CSB may cause dysfunction in vascular endothelial cells and its possible mechanism. Low, medium, and high concentrations of serum of long-term CSB intake patients were used to treat HUVECs, with LPS as the positive control. MTT and CCK8 were performed to verify CSB's damaging effect on HUVECs. The expression of ET-1, ICAM-1, VCAM-1, and E-selectin were measured by ELISA. TUNEL assay and Matrigel tube formation assay were carried out to detect apoptosis and angiogenesis of HUVECs. Flow cytometry was applied to analyze cell cycles and expression of CD11b, PDGF, and ICAM-1. Expression of PDGF-BB and PCNA were examined by western blot. The activation of MAPK signaling pathway was detected by qRT-PCR and western blot. Intracellular Ca2+ density was detected by fluorescent probes. CCK8 assay showed high concentration of CSB inhibited cell viability. Cell proliferation and angiogenesis were inhibited by CSB. ET-1, ICAM-1, VCAM-1, and E-selectin upregulated in CSB groups. CSB enhanced apoptosis of HUVECs. CD11b, ICAM-1 increased and PDGF reduced in CSB groups. The expression level and phosphorylation level of MEK, ERK, JUN, and p38 in MAPK pathway elevated in CSB groups. The expression of PCNA and PDGF-BB was suppressed by CSB. Intracellular Ca2+ intensity was increased by CSB. Abuse of CSB injured HUVECs and caused coagulation disorders.

Integrated multi-omics analysis reveals the positive leverage of citrus flavonoids on hindgut microbiota and host homeostasis by modulating sphingolipid metabolism in mid-lactation dairy cows consuming a high-starch diet.

BACKGROUND: Modern dairy diets have shifted from being forage-based to grain and energy dense. However, feeding high-starch diets can lead to a metabolic disturbance that is linked to dysregulation of the gastrointestinal microbiome and systemic inflammatory response. Plant flavonoids have recently attracted extensive interest due to their anti-inflammatory effects in humans and ruminants. Here, multi-omics analysis was conducted to characterize the biological function and mechanisms of citrus flavonoids in modulating the hindgut microbiome of dairy cows fed a high-starch diet. RESULTS: Citrus flavonoid extract (CFE) significantly lowered serum concentrations of lipopolysaccharide (LPS) proinflammatory cytokines (TNF-α and IL-6), acute phase proteins (LPS-binding protein and haptoglobin) in dairy cows fed a high-starch diet. Dietary CFE supplementation increased fecal butyrate production and decreased fecal LPS. In addition, dietary CFE influenced the overall hindgut microbiota's structure and composition. Notably, potentially beneficial bacteria, including Bacteroides, Bifidobacterium, Alistipes, and Akkermansia, were enriched in CFE and were found to be positively correlated with fecal metabolites and host metabolites. Fecal and serum untargeted metabolomics indicated that CFE supplementation mainly emphasized the metabolic feature "sphingolipid metabolism." Metabolites associated with the sphingolipid metabolism pathway were positively associated with increased microorganisms in dairy cows fed CFE, particularly Bacteroides. Serum lipidomics analysis showed that the total contents of ceramide and sphingomyelin were decreased by CFE addition. Some differentially abundant sphingolipid species were markedly associated with serum IL-6, TNF-α, LPS, and fecal Bacteroides. Metaproteomics revealed that dietary supplementation with CFE strongly impacted the overall fecal bacterial protein profile and function. In CFE cows, enzymes involved in carbon metabolism, sphingolipid metabolism, and valine, leucine, and isoleucine biosynthesis were upregulated. CONCLUSIONS: Our research indicates the importance of bacterial sphingolipids in maintaining hindgut symbiosis and homeostasis. Dietary supplementation with CFE can decrease systemic inflammation by maintaining hindgut microbiota homeostasis and regulating sphingolipid metabolism in dairy cows fed a high-starch diet. Video Abstract.

Risk factors and predictive model for pulmonary complications in patients transferred to ICU after hepatectomy.

OBJECTIVE: Postoperative pulmonary complications (PPCs) seriously harm the recovery and prognosis of patients undergoing surgery. However, its related risk factors in critical patients after hepatectomy have been rarely reported. This study aimed at analyzing the factors related to PPCs in critical adult patients after hepatectomy and create a nomogram for prediction of the PPCs. METHODS: 503 patients' data were collected form the Peking University People's Hospital. Multivariate logistic regression analysis was used to identify independent risk factors to derive the nomogram. Nomogram's discriminatory ability was assessed using the area under the receiver operating characteristic curve (AUC), and calibration was assessed using the Hosmer-Lemeshow goodness-of-fit test and calibration curve. RESULTS: The independent risk factor for PPCs are advanced age (odds ratio [OR] = 1.026; P = 0.008), higher body mass index (OR = 1.139; P < 0.001), lower preoperative serum albumin level (OR = 0.961; P = 0.037), and intensive care unit first day infusion volume (OR = 1.152; P = 0.040). And based on this, we created a nomogram to predict the occurrence of PPCs. Upon assessing the nomogram's predictive ability, the AUC for the model was 0.713( 95% CI: 0.668-0.758, P<0.001). The Hosmer-Lemeshow test (P = 0.590) and calibration curve showed good calibration for the prediction of PPCs. CONCLUSIONS: The prevalence and mortality of postoperative pulmonary complications in critical adult patients after hepatectomy are high. Advanced age, higher body mass index, lower preoperative serum albumin and intensive care unit first day infusion volume were found to be significantly associated with PPCs. And we created a nomogram model which can be used to predict the occurrence of PPCs.

Feeding citrus flavonoid extracts decreases bacterial endotoxin and systemic inflammation and improves immunometabolic status by modulating hindgut microbiome and metabolome in lactating dairy cows.

The objectives of this study were to determine the effects of dietary supplementation with citrus flavonoid extracts (CFE) on milk performance, serum biochemistry parameters, fecal volatile fatty acids, fecal microbial community, and fecal metabolites in dairy cows. Eight multiparous lactating Holstein cows were used in a replicated 4 × 4 Latin square design (21-day period). Cows were fed a basal diet without addition (CON) or basal diet with added CFE at 50 (CFE50), 100 (CFE10), and 150 g/d (CFE150). Feeding CFE up to 150 g/d increased milk yield and milk lactose percentage. Supplementary CFE linearly decreased milk somatic cell count. Serum cytokines interleukin-1ß (IL-1ß), IL-2, IL-6, and tumor necrosis factor-α (TNF-α) concentrations decreased linearly as the levels of CFE increased. Cows in CFE150 had lower serum lipopolysaccharide and lipopolysaccharide binding protein compared with CON. These results indicate feeding CFE decreased systemic inflammation and endotoxin levels in dairy cows. Furthermore, feeding CFE linearly increased the concentrations of total volatile fatty acids, acetate, and butyrate in feces. The relative abundances of beneficial bacteria Bifidobacterium spp., Clostridium coccoides-Eubacterium rectale group, and Faecalibacterium prausnitzii in feces increased linearly with increasing CFE supplementation. The diversity and community structure of fecal microbiota were unaffected by CFE supplementation. However, supplementing CFE reduced the relative abundances of genera Ruminococcus_torques_group, Roseburia, and Lachnospira, but increased genera Bacteroides and Phascolarctobacterium. Metabolomics analysis showed that supplementary CFE resulted in a significant modification in the fecal metabolites profile. Compared with CON, fecal naringenin, hesperetin, hippuric acid, and sphingosine concentrations were greater in CFE150 cows, while fecal GlcCer(d18:1/20:0), Cer(d18:0/24:0), Cer(d18:0/22:0), sphinganine, and deoxycholic acid concentrations were less in CFE150 cows. Predicted pathway analysis suggested that "sphingolipid metabolism" was significantly enriched. Overall, these results indicate that citrus flavonoids could exert health-promoting effects by modulating hindgut microbiome and metabolism in lactating cows.

A radiomics model for predicting the response to methylprednisolone in brain necrosis after radiotherapy for nasopharyngeal carcinoma.

BACKGROUND: Methylprednisolone is recommended as the front-line therapy for radiation-induced brain necrosis (RN) after radiotherapy for nasopharyngeal carcinoma. However, some patients fail to benefit from methylprednisolone or even progress. This study aimed to develop and validate a radiomic model to predict the response to methylprednisolone in RN. METHODS: Sixty-six patients receiving methylprednisolone were enrolled. In total, 961 radiomic features were extracted from the pre-treatment magnetic resonance imagings of the brain. Least absolute shrinkage and selection operator regression was then applied to construct the radiomics signature. Combined with independent clinical predictors, a radiomics model was built with multivariate logistic regression analysis. Discrimination, calibration and clinical usefulness of the model were assessed. The model was internally validated using 10-fold cross-validation. RESULTS: The radiomics signature consisted of 16 selected features and achieved favorable discrimination performance. The radiomics model incorporating the radiomics signature and the duration between radiotherapy and RN diagnosis, yielded an AUC of 0.966 and an optimism-corrected AUC of 0.967 via 10-fold cross-validation, which also revealed good discrimination. Calibration curves showed good agreement. Decision curve analysis confirmed the clinical utility of the model. CONCLUSIONS: The presented radiomics model can be conveniently used to facilitate individualized prediction of the response to methylprednisolone in patients with RN.

Identification of a copper metabolism-related gene signature for predicting prognosis and immune response in glioma.

BACKGROUND: Gliomas are highly refractory intracranial cancers characterized by genetic and transcriptional heterogeneity. However, therapeutic options are limited. In the last years, copper-induced cell death is becoming a prospective treatment strategy for gliomas and other solid tumors, but copper metabolism-related genes associated with cancer development remain unclear. METHODS: We first collected gene expression data from The Cancer Genome Atlas (TCGA) to identify significantly differentially expressed copper metabolism-related genes in gliomas. Using these genes, we performed COX regression and Least Absolute Shrinkage and Selection Operator (LASSO) regression to construct the prognostic model. The prognostic value of the model was further validated by CGGA testing set. Subsequently, functional analyses were carried out, including gene set enrichment analysis (GSEA), immune infiltration analysis, and mutation analysis. Finally, the expression levels of these genes were verified by immunohistochemical analysis. RESULTS: The prognostic model consisted of 7 genes: CDK1, LOXL2, LOXL3, NFE2L2, SLC31A1, SUMF1 and FDX1. According to this prognosis model, glioma patients could be split into the high-risk group or low-risk group, and the low-risk group showed significantly better prognostic survival (p < 0.001). Moreover, the high-risk group had higher levels of immune cell infiltration, immune checkpoint genes expression, and higher tumor mutational burden (TMB), which indicates that they might benefit more from immunotherapy. Finally, we confirmed the expression level of FDX1, SUMF1, and SLC31A1 protein as significantly different in glioblastoma, lower-grade glioma, and non-tumor brain tissues by immunohistochemical analysis, and the high expression of FDX1 and SLC31A1 protein was related to poor survival in glioma patients. CONCLUSIONS: Our study could contribute to the prognosis prediction and decision-making in patients with gliomas.

Metabolomic analysis of the sera of patients with the long-term inhalation of caffeine-sodium benzoate using LC-MS.

The present study aimed to systematically assess the potential biomarkers in the serum samples of patients with long-term inhalation of caffeine-sodium benzoate (CSB). LC-MS was applied to analyze the metabolic profiles of serum samples of patients with the long-term intake of CSB (n = 35) and other volunteers with no intake of CSB treated as the control group (n = 35). The raw data of metabolic profiles were analyzed via principal component analysis, partial least squares analysis, and orthogonal partial least squares analysis. MBRole 2.0 online tools were used to analyze the Kyoto Encyclopedia of Genes and Genomes pathway enrichment analysis of different metabolites. The serum metabolic profiles showed several metabolites with large variations, including 2-propyl-2,4-pentadienoic acid, 24-hydroxycholesterol, 3-O-sulfogalactosylceramide (d18:1/24:1(15Z)), 3-O-sulfogalactosylceramide (d18:1/12:0), 3-O-sulfogalactosylceramide (d18:1/14:0), 3a,7a-dihydroxy-5b-cholestan-26-al, 3a,7a-dihydroxy-5b-cholestane, 7a,25-dihydroxycholesterol, bilirubin, and dehydroepiandrosterone sulfate. The Kyoto Encyclopedia of Genes and Genomes pathways involved in metabolism included 'choline metabolism in cancer' and 'glycerophospholipid metabolism'. In conclusion, the present study provides a basis with which to explore the molecular-specific mechanisms concerning the effects of the long-term inhalation of CSB on human physical and mental health.

Machine learning on MRI radiomic features: identification of molecular subtype alteration in breast cancer after neoadjuvant therapy.

OBJECTIVES: Recent studies have revealed the change of molecular subtypes in breast cancer (BC) after neoadjuvant therapy (NAT). This study aims to construct a non-invasive model for predicting molecular subtype alteration in breast cancer after NAT. METHODS: Eighty-two estrogen receptor (ER)-negative/ human epidermal growth factor receptor 2 (HER2)-negative or ER-low-positive/HER2-negative breast cancer patients who underwent NAT and completed baseline MRI were retrospectively recruited between July 2010 and November 2020. Subtype alteration was observed in 21 cases after NAT. A 2D-DenseUNet machine-learning model was built to perform automatic segmentation of breast cancer. 851 radiomic features were extracted from each MRI sequence (T2-weighted imaging, ADC, DCE, and contrast-enhanced T1-weighted imaging), both in the manual and auto-segmentation masks. All samples were divided into a training set (n = 66) and a test set (n = 16). XGBoost model with 5-fold cross-validation was performed to predict molecular subtype alterations in breast cancer patients after NAT. The predictive ability of these models was subsequently evaluated by the AUC of the ROC curve, sensitivity, and specificity. RESULTS: A model consisting of three radiomics features from the manual segmentation of multi-sequence MRI achieved favorable predictive efficacy in identifying molecular subtype alteration in BC after NAT (cross-validation set: AUC = 0.908, independent test set: AUC = 0.864); whereas an automatic segmentation approach of BC lesions on the DCE sequence produced good segmentation results (Dice similarity coefficient = 0.720). CONCLUSIONS: A machine learning model based on baseline MRI is proven useful for predicting molecular subtype alterations in breast cancer after NAT. KEY POINTS: • Machine learning models using MRI-based radiomics signature have the ability to predict molecular subtype alterations in breast cancer after neoadjuvant therapy, which subsequently affect treatment protocols. • The application of deep learning in the automatic segmentation of breast cancer lesions from MRI images shows the potential to replace manual segmentation..