The prognostic role of an optimal machine learning model based on clinical available indicators in HCC patients.

Although methods in diagnosis and therapy of hepatocellular carcinoma (HCC) have made significant progress in the past decades, the overall survival (OS) of liver cancer is still disappointing. Machine learning models have several advantages over traditional cox models in prognostic prediction. This study aimed at designing an optimal panel and constructing an optimal machine learning model in predicting prognosis for HCC. A total of 941 HCC patients with completed survival data and preoperative clinical chemistry and immunology indicators from two medical centers were included. The OCC panel was designed by univariate and multivariate cox regression analysis. Subsequently, cox model and machine-learning models were established and assessed for predicting OS and PFS in discovery cohort and internal validation cohort. The best OCC model was validated in the external validation cohort and analyzed in different subgroups. In discovery, internal and external validation cohort, C-indexes of our optimal OCC model were 0.871 (95% CI, 0.863-0.878), 0.692 (95% CI, 0.667-0.717) and 0.648 (95% CI, 0.630-0.667), respectively; the 2-year AUCs of OCC model were 0.939 (95% CI, 0.920-0.959), 0.738 (95% CI, 0.667-0.809) and 0.725 (95% CI, 0.643-0.808), respectively. For subgroup analysis of HCC patients with HBV, aged less than 65, cirrhosis or resection as first therapy, C-indexes of our optimal OCC model were 0.772 (95% CI, 0.752-0.792), 0.769 (95% CI, 0.750-0.789), 0.855 (95% CI, 0.846-0.864) and 0.760 (95% CI, 0.741-0.778), respectively. In general, the optimal OCC model based on RSF algorithm shows prognostic guidance value in HCC patients undergoing individualized treatment.

Association between complete blood count-derived inflammatory markers and the risk of frailty and mortality in middle-aged and older adults.

Objective: This study aimed to evaluate the association between six complete blood count (CBC)-derived inflammatory markers [neutrophil-to-lymphocyte ratio (NLR), monocyte-to-lymphocyte ratio (MLR), platelet-to-lymphocyte ratio (PLR), systemic immune-inflammatory index (SII), systemic inflammatory response index (SIRI), and pan-immune inflammation value (PIV)] and the risk of frailty and mortality. Methods: Data were obtained from the National Health and Nutrition Examination Survey (NHANES) 1999-2018. Mortality was identified using the National Death Index until December 31, 2019. Multiple logistic regression analysis was conducted to evaluate the association between six CBC-derived inflammatory markers and frailty. The Cox regression model assessed the association between six CBC-derived inflammatory markers and mortality in frail populations. Restricted cubic spline (RCS) was used to visualize the association of the six CBC-derived inflammatory markers with mortality risk. The predictive value of CBC-derived inflammatory markers for mortality was further assessed using a random survival forest (RSF) approach. Results: This study analyzed data from a total of 16,705 middle-aged and older participants. Among them, 6,503 participants were frail, with a mortality rate of 41.47%. Multiple logistic regression analysis showed that NLR, MLR, PLR, SII, SIRI, and PIV were positively associated with frailty risk. The Cox regression model revealed that participants in the highest quartile had a significantly increased risk of death compared to those in the lowest quartile: NLR (HR = 1.73, 95% CI:1.54, 1.94), MLR (HR = 1.71, 95% CI:1.51, 1.93), PLR (HR = 1.28, 95%CI: 1.15, 1.43), SII (HR = 1.50, 95%CI:1.34, 1.68), SIRI (HR = 1.88, CI 95%:1.67, 2.12), PIV (HR = 1.55, 95%CI:1.38, 1.73). Random survival forest (RSF) analyses demonstrated that MLR had the highest predictive value for mortality risk middle-aged and older adult frail participants. Conclusion: The results suggest that CBC-derived inflammatory markers are associated with a higher risk of frailty as well as mortality in the middle and old-aged population of the United States.

Machine learning-based model for predicting tumor recurrence after interventional therapy in HBV-related hepatocellular carcinoma patients with low preoperative platelet-albumin-bilirubin score.

Introduction: This study aimed to develop a prognostic nomogram for predicting the recurrence-free survival (RFS) of hepatitis B virus (HBV)-related hepatocellular carcinoma (HCC) patients with low preoperative platelet-albumin-bilirubin (PALBI) scores after transarterial chemoembolization (TACE) combined with local ablation treatment. Methods: We gathered clinical data from 632 HBV-related HCC patients who received the combination treatment at Beijing You'an Hospital, affiliated with Capital Medical University, from January 2014 to January 2020. The patients were divided into two groups based on their PALBI scores: low PALBI group (n=247) and high PALBI group (n=385). The low PALBI group was then divided into two cohorts: training cohort (n=172) and validation cohort (n=75). We utilized eXtreme Gradient Boosting (XGBoost), random survival forest (RSF), and multivariate Cox analysis to pinpoint the risk factors for RFS. Then, we developed a nomogram based on the screened factors and assessed its risk stratification capabilities and predictive performance. Results: The study finally identified age, aspartate aminotransferase (AST), and prothrombin time activity (PTA) as key predictors. The three variables were included to develop the nomogram for predicting the 1-, 3-, and 5-year RFS of HCC patients. We confirmed the nomogram's ability to effectively discern high and low risk patients, as evidenced by Kaplan-Meier curves. We further corroborated the excellent discrimination, consistency, and clinical utility of the nomogram through assessments using the C-index, area under the curve (AUC), calibration curve, and decision curve analysis (DCA). Conclusion: Our study successfully constructed a robust nomogram, effectively predicting 1-, 3-, and 5-year RFS for HBV-related HCC patients with low preoperative PALBI scores after TACE combined with local ablation therapy.

Time-programmed release of curcumin and Zn2+ from multi-layered RSF coating modified PET graft for improvement of graft-host integration.

Artificial graft serves as the primary grafts used in the clinical management of sports-related injuries. Until now, optimizing its graft-host integration remains a great challenge due to the excessive inflammatory response during the inflammatory phase, coupled with an absence of tissue-inductive capacity during the regeneration phase. Here, a multi-layered regenerated silk fibroin (RSF) coating loaded with curcumin (Cur) and Zn2+ on the surface of the PET grafts (Cur@Zn2+@PET) was designed and fabricated for providing time-matched regulation specifically tailored to address issues arising at both inflammatory and regeneration phases, respectively. The release of Cur and Zn2+ from the Cur@Zn2+@PET followed a time-programmed pattern in vitro. Specifically, cellular assays revealed that Cur@Zn2+@PET initially released Cur during the inflammatory phase, thereby markedly inhibit the expression of inflammatory cytokines TNF-a and IL-1ß. Meanwhile, a significant release of Zn2+ was major part during the regeneration phase, serving to induce the osteogenic differentiation of rBMSC. Furthermore, rat model of anterior cruciate ligament reconstruction (ACLR) showed that through time-programmed drug release, Cur@Zn2+@PET could suppress the formation of fibrous interface (FI) caused by inflammatory response, combined with significant new bone (NB) formation during regeneration phase. Consequently, the implementation of the Cur@Zn2+@PET characterized by its time-programmed release patterns hold considerable promise for improving graft-host integration for sports-related injuries.

Cryo-EM structure and functional analysis of the chromatin remodeler RSF.

The RSF complex belongs to the ISWI chromatin-remodeling family and is composed of two subunits: RSF1 (remodeling and spacing factor 1) and SNF2h (sucrose nonfermenting protein 2 homolog). The RSF complex participates in nucleosome spacing and assembly, and subsequently promotes nucleosome maturation. Although SNF2h has been extensively studied in the last few years, the structural and functional properties of the remodeler RSF1 still remain vague. Here, a cryo-EM structure of the RSF-nucleosome complex is reported. The 3D model shows a two-lobe architecture of RSF, and the structure of the RSF-nucleosome (flanked with linker DNA) complex shows that the RSF complex moves the DNA away from the histone octamer surface at the DNA-entry point. Additionally, a nucleosome-sliding assay and a restriction-enzyme accessibility assay show that the RSF1 subunit may cause changes in the chromatin-remodeling properties of SNF2h. As a `nucleosome ruler', the results of an RSF-dinucleosome binding affinity test led to the proposal that the critical distance that RSF `measures' between two nucleosomes is about 24 base pairs.

Nomogram for predicting post-therapy recurrence in BCLC A/B hepatocellular carcinoma with Child-Pugh B cirrhosis.

Introduction: This study conducts a retrospective analysis on patients with BCLC stage A/B hepatocellular carcinoma (HCC) accompanied by Child-Pugh B cirrhosis, who underwent transarterial chemoembolization (TACE) in combination with local ablation therapy. Our goal was to uncover risk factors contributing to post-treatment recurrence and to develop and validate an innovative 1-, 3-, and 5-year recurrence free survival (RFS) nomogram. Methods: Data from 255 BCLC A/B HCC patients with Child-Pugh B cirrhosis treated at Beijing You'an Hospital (January 2014 - January 2020) were analyzed using random survival forest (RSF), LASSO regression, and multivariate Cox regression to identify independent risk factors for RFS. The prognostic nomogram was then constructed and validated, categorizing patients into low, intermediate, and high-risk groups, with RFS assessed using Kaplan-Meier curves. Results: The nomogram, integrating the albumin/globulin ratio, gender, tumor number, and size, showcased robust predictive performance. Harrell's concordance index (C-index) values for the training and validation cohorts were 0.744 (95% CI: 0.703-0.785) and 0.724 (95% CI: 0.644-0.804), respectively. The area under the curve (AUC) values for 1-, 3-, and 5-year RFS in the two cohorts were also promising. Calibration curves highlighted the nomogram's reliability and decision curve analysis (DCA) confirmed its practical clinical benefits. Through meticulous patient stratification, we also revealed the nomogram's efficacy in distinguishing varying recurrence risks. Conclusion: This study advances recurrence prediction in BCLC A/B HCC patients with Child-Pugh B cirrhosis following TACE combined with ablation. The established nomogram accurately predicts 1-, 3-, and 5-year RFS, facilitating timely identification of high-risk populations.

Model validation and Robust State Feedback control for nonlinear subway traffic networks.

This paper develops a nonlinear Discrete-Event (DE) model for Intersecting Lines (ILs) of a Subway Network (SN). A class of Monte Carlo (MC) methods based on repeated random values is employed to validate the model. Model validation is performed by generating random values for delay rates and external disturbances. After model validation, a Robust State Feedback (RSF) controller is designed to compensate the delays caused by disturbances. Simulations are performed based on the characteristics and actual data of lines 2 and 4 of the Tehran subway to demonstrate the effectiveness of the proposed approach. Simulation results show the accuracy and proper performance of the proposed model. In addition, the closed-loop responses demonstrate the effectiveness of the proposed RSF controller for centralized and decentralized control configurations.

Machine Learning-Based Nomogram for Predicting Overall Survival in Elderly Patients with Cirrhotic Hepatocellular Carcinoma Undergoing Ablation Therapy.

Purpose: The aim of the study is to identify and evaluate multifaceted factors impacting the survival of elderly cirrhotic HCC patients following ablation therapy, with the goal of constructing a nomogram to predict their 3-, 5-, and 8-year overall survival (OS). Patients and Methods: A retrospective analysis was conducted on 736 elderly cirrhotic HCC patients who underwent ablation therapy between 2014 and 2022. LASSO regression, random survival forest (RSF), and multivariate Cox analyses were employed to identify independent prognostic factors for OS, followed by the development and validation of a predictive nomogram. Harrell's concordance index (C-index), calibration plot and decision curve analysis (DCA) were used to assess the performance of the nomogram. The nomogram was finally utilized to stratify patients into low-, intermediate-, and high-risk groups, aiming to assess its efficacy in precisely discerning individuals with diverse overall survival outcomes. Results: Alcohol drinking, tumor number, globulin (Glob) and prealbumin (Palb) were identified and integrated to establish a novel prognostic nomogram. The nomogram exhibited strong discriminative ability with C-indices of 0.723 (training cohort) and 0.693 (validation cohort), along with significant Area Under the Curve (AUC) values for 3-year, 5-year, and 8-year OS in both cohorts (0.758, 0.770, and 0.811 for training cohort; 0.744, 0.699 and 0.737 for validation cohort). Calibration plots substantiated its consistency, while DCA curves corroborated its clinical utility. The nomogram further demonstrated exceptional effectiveness in discerning distinct risk populations, highlighting its robust applicability for prognostic stratification. Conclusion: Our study successfully developed and validated a robust nomogram model based on four key clinical parameters for predicting 3-, 5- and 8-year OS among elderly cirrhotic HCC patients following ablation therapy. The nomogram exhibited a remarkable capability in identifying high-risk patients, furnishing clinicians with invaluable insights for postoperative surveillance and tailored therapeutic interventions.

Bioremediation of rapid sand filters for removal of organic micropollutants during drinking water production.

Rapid sand filtration (RSF) is used during drinking water production for removal of particles, possible harmful microorganisms, organic material and inorganic compounds such as iron, manganese, ammonium and methane. However, RSF can also be used for removal of certain organic micropollutants (OMPs). In this study, it was investigated if OMP removal in columns packed with sand from full scale RSFs could be stimulated by bioaugmentation (i.e. inoculating RSFs with sand from another RSF) and/or biostimulation (i.e. addition of nutrients, vitamins and trace-elements that stimulate microbial growth). The results showed that removal of PFOA, carbamazepine, 1-H benzotriazole, amidotrizoate and iopamidol in the columns was low (< 20 %). Propranolol and diclofenac removal was higher (50-60 %) and propranolol removal likely occurred via sorption processes, whereas for diclofenac it was unclear if removal was a combination of physical-chemical and biological processes. Moreover, bioaugmentation and biostimulation resulted in 99 % removal of gabapentin and metoprolol after 38 days and 99 % removal of acesulfame after 52 days of incubation. The bioaugmented column without biostimulation showed 99 % removal for gabapentin and metoprolol after 52 days, and for acesulfame after 80 days. In contrast, the non-bioaugmented column did not remove gabapentin, removed < 40 % metoprolol and showed 99 % removal of acesulfame only after 80 days of incubation. Removal of these OMPs was negatively correlated with ammonium oxidation and the absolute abundance of ammonia-oxidizing bacteria. 16S rRNA gene sequencing showed that OMP removal of acesulfame, gabapentin and metoprolol was positively correlated to the relative abundance of specific bacterial genera that harbor species with a heterotrophic and aerobic or denitrifying metabolism. These results show that bioaugmentation of RSF can be successful for OMP removal, where biostimulation can accelerate this removal.

Exosome-delivered circRNA circSYT15 contributes to cisplatin resistance in cervical cancer cells through the miR-503-5p/RSF1 axis.

The development of chemotherapy resistance is a major obstacle for cervical cancer (CC) patients. Exosome-mediated transfer of circular RNAs (circRNAs) was found to have relevance to the CC. This study is designed to explore the role and mechanism of exosomal circRNA synaptotagmin 15 (circSYT15) on cisplatin (DDP) resistance in CC. Cell proliferation ability and apoptosis rate were detected by Cell Counting Kit-8 (CCK-8), 5-ethynyl-2'-deoxyuridine (EdU), colony formation, and flow cytometry assays. CircSYT15, microRNA-503-5p (miR-503-5p), Remodeling spacing factor 1 (RSF1) levels were detected by real-time quantitative polymerase chain reaction (RT-qPCR). Exosomes were analyzed by a transmission electron microscope and nanoparticle tracking analysis. CD63, CD81, TSC101, Bcl-2, Bax, C-caspase 3, and RSF1 protein levels were examined by western blot assay. The binding between miR-503-5p and circSYT15 or RSF1 was predicted by circBank or Starbase and then verified by a dual-luciferase reporter and RNA Immunoprecipitation (RIP). The biological role of exosomal circSYT15 in DDP resistance of CC in vivo. CircSYT15 was upregulated in the DDP-resistant CC cells and exosomes isolated from DDP-resistant CC cells. CircSYT15 knockdown repressed the proliferation and drug resistance of CC and induced apoptosis in CC cells. Exosomes shuttled circSYT15 act as a sponge to affect RSF1 expression, thereby promoting proliferation and drug resistance and repressing apoptosis of sensitive CC cells. Exosomal circSYT15 boost DDP resistance of cervical cancer in vivo. Exosome-mediated transfer of circSYT15 enhanced DDP resistance in CC partly by targeting the miR-503-5p/RSF1 axis, providing a foundation for future clinical applications of CC drug resistance.

Molecular mechanism controlling anthocyanin composition and content in radish plants with different root colors.

Radish (Raphanus sativus) roots exhibit various colors that reflect their anthocyanin compositions and contents. However, the details of the mechanism linking the expression of anthocyanin biosynthesis and their transcriptional regulators to anthocyanin composition in radish roots remained unknown. Here, we characterized the role of the anthocyanin biosynthetic enzyme flavonoid 3'-hydroxylase (RsF3'H), together with the R2R3 MYB transcription factor (TF) RsMYB1 and the basic helix-loop-helix (bHLH) TF TRANSPARENT TESTA 8 (RsTT8), in four radish plants with different root colors: white (W), deep red (DR), dark purple (DP), and dark greyish purple (DGP). The DR plant contained heterozygous for RsF3'H with low expression level and accumulated a large amount of pelargonidin, resulting in deep red color. While, the DP and DGP plants accumulated the cyanidin due to the higher expression level of functional RsF3'H. Notably, RsMYB1 and RsTT8 transcripts were abundant in all pigmented roots, but not in white roots. To investigate the differential expression of RsMYB1 and RsTT8, we compared the sequences of their promoter regions among the four radish plants, revealing variations in the numbers of cis-elements and in promoter architecture. Promoter activation assays demonstrated that variation in the RsMYB1 and RsTT8 promoters may contribute to the expression level of these genes, and RsMYB1 can activate its own expression as well as promote the RsTT8 expression. These results suggested that RsF3'H plays a vital role in anthocyanin composition and the expression level of both RsMYB1 and RsTT8 are crucial determinants for anthocyanin content in radish roots. Overall, these findings provide insight into the molecular basis of anthocyanin composition and level in radish roots.

Associations of complete blood cell count-derived inflammatory biomarkers with asthma and mortality in adults: a population-based study.

Objective: This study aims to assess the associations of complete blood cell count (CBC)-derived inflammatory biomarkers with the prevalence of asthma and mortality. Methods: Data was collected from the 1999-2018 National Health and Nutrition Examination Survey (NHANES). Mortality was identified using the National Death Index until December 31, 2019. The study analyzed the relationship between CBC-derived inflammatory biomarkers, including neutrophil-to-lymphocyte ratio (NLR), platelet-to-lymphocyte ratio (PLR), monocyte-to-lymphocyte ratio (MLR), systemic inflammatory response index (SIRI), and systemic immune-inflammation index (SII), and the prevalence of asthma using multiple logistic regressions. To assess the significance of CBC-derived inflammatory biomarkers in predicting all-cause and respiratory disease mortality in asthma patients, Cox proportional regressions and the random survival forest (RSF) analysis were utilized. Results: A total of 48,305 participants were included, with a mean age of 47.27 ± 0.18 years and 49.44% male. Among them, 6,403 participants had asthma, with a prevalence of 13.28%. The all-cause and respiratory disease deaths at a median follow-up of 8.2 (4.5, 12.8) years were 929 and 137 respectively. After adjusting for confounders, the prevalence of asthma was found to be positively associated with NLR, PLR, MLR, SIRI and SII. Compared to the lowest quartile, the highest quartile of NLR (HR=1.765 [1.378-2.262]), MLR (HR=1.717 [1.316-2.241]), SIRI (HR=1.796 [1.353-2.383]) and SII (HR=1.432 [1.141-1.797]) were associated with an increased risk of all-cause mortality. These associations were more pronounced in respiratory disease mortality of asthma patients. RSF analysis showed that MLR had the highest predictive value for all-cause and respiratory disease mortality in adults with asthma. The sensitivity analysis demonstrated the stability of our results. Conclusion: The findings suggest that CBC-derived inflammatory biomarkers are associated with a higher risk of all-cause and respiratory disease mortality in adults with asthma.

Recognition of H2AK119ub plays an important role in RSF1-regulated early Xenopus development.

Polycomb group (PcG) proteins are key regulators of gene expression and developmental programs via covalent modification of histones, but the factors that interpret histone modification marks to regulate embryogenesis are less studied. We previously identified Remodeling and Spacing Factor 1 (RSF1) as a reader of histone H2A lysine 119 ubiquitination (H2AK119ub), the histone mark deposited by Polycomb Repressive Complex 1 (PRC1). In the current study, we used Xenopus laevis as a model to investigate how RSF1 affects early embryonic development and whether recognition of H2AK119ub is important for the function of RSF1. We showed that knockdown of Xenopus RSF1, rsf1, not only induced gastrulation defects as reported previously, but specific targeted knockdown in prospective neural precursors induced neural and neural crest defects, with reductions of marker genes. In addition, similar to knockdown of PRC1 components in Xenopus, the anterior-posterior neural patterning was affected in rsf1 knockdown embryos. Binding of H2AK119ub appeared to be crucial for rsf1 function, as a construct with deletion of the UAB domain, which is required for RSF1 to recognize the H2AK119ub nucleosomes, failed to rescue rsf1 morphant embryos and was less effective in interfering with early Xenopus development when ectopically expressed. Furthermore, ectopic deposition of H2AK119ub on the Smad2 target gene gsc using a ring1a-smad2 fusion protein led to ectopic recruitment of RSF1. The fusion protein was inefficient in inducing mesodermal markers in the animal region or a secondary axis when expressed in the ventral tissues. Taken together, our results reveal that rsf1 modulates similar developmental processes in early Xenopus embryos as components of PRC1 do, and that RSF1 acts at least partially through binding to the H2AK119ub mark via the UAB domain during development.

Psychometric evaluation of the postpartum specific anxiety scale - research short-form among iranian women (PSAS-IR-RSF).

BACKGROUND: The increasing prevalence of postpartum anxiety as a common psychological problem affects a large part of women's lives. Despite the existence of tools in this field, but due to the lack of specificity in reflecting postpartum anxiety, it is necessary to have a specific tool to screen it. Since the psychometric evaluation of the Postpartum Specific Anxiety Scale-Research Short-Form (PSAS-RSF) among Iranian women has not been assessed in Iran until now, so we decided to conduct this study with the aim of psychometric evaluation of the PSAS-IR-RSF. METHODS: We included 180 women (six weeks to six months postpartum) in the study by random sampling during the period from December 2021 to June 2022. We examined the validity of the PSAS-IR-RSF tool in terms of face, content and construct (through exploratory and confirmatory factor analyses). We used internal consistency and test-retest reliability to determine the reliability of the scale. RESULTS: In the present study, content validity index (CVI) and content validity ratio (CVR) of the PSAS-IR-RSF tool were equal to 0.91 and 0.97, respectively. We extracted a four-factor structure through the process of exploratory factor analysis. The values of fitting indices confirmed the validity of the model. Cronbach's alpha coefficient was equal to 0.72 and intra-class correlation coefficient (with 95% confidence interval) was 0.97 (0.98 to 0.93). CONCLUSIONS: The Persian version of the PSAS-IR-RSF is a valid and reliable tool for the specific evaluation of postpartum anxiety among Iranian women.

Validation of the Iranian version of the Postpartum Specific Anxiety Scale 12-item research short-form for use during global crises (PSAS-IR-RSF-C).

BACKGROUND: Due to its high pervasiveness and adversarial consequences, postpartum anxiety has been one of the most worrying public health concerns in the last decade. According to previous research, the occurrence of mental disorders among women in the postpartum period upsurges significantly in the course of universal disasters. The Postpartum Specific Anxiety Scale - Research Short Form - for use in global Crises [PSAS-IR-RSF-C] has not been used in Iran for postpartum women during a health system shock. Consequently, this study was conducted to determine the validity and reliability of the Persian version (PSAS-IR-RSF-C) during the COVID-19 pandemic. METHODS: This cross-sectional study was performed with 180 women who were between six weeks and six months after delivery, by random sampling method from December 2021 to June 2022. The validity of the PSAS-RSF-C in terms of face, content, was analyzed, and the construct validity was assessed using exploratory and confirmatory factor analyses. Internal consistency and test-retest reliability of the questionnaire were measured using (Cronbach's alpha, McDonald's ω) and intraclass correlation coefficient (ICC), respectively. RESULTS: The content validity index and content validity ratio of the Persian version of the PSAS-IR-RSF-C were 0.96 and 0.98, respectively. A three-factor structure was extracted during the exploratory factor analysis process, and model validity was confirmed by the values of fit indices. Cronbach's alpha coefficient, McDonald's ω and intra-cluster correlation coefficient (95% confidence interval) were 0.74, 0.92 (0.78 to 0.93) and 0.97 (0.93 to 0.98), respectively. CONCLUSIONS: For the specific assessment of postpartum anxiety among Iranian women during crises, the Persian version of the PSAS-IR-RSF-C is a valid and reliable tool.

The influence of vegetation structure on sleeping site selection by chimpanzees (Pan troglodytes troglodytes).

Sleep is an important aspect of great ape life; these animals build sleeping platforms every night. In a community of chimpanzees, each subgroup selects a sleeping site where each individual builds a sleeping platform, mostly on a tree. Previous studies have measured the heights of sleeping platforms and sleeping trees to test the predation avoidance and thermoregulation hypotheses of sleeping site selection. However, it remains unclear how components of vegetation structure (vertical and horizontal) together determine the selection of sleeping sites by chimpanzees. Using botanical inventories around sleeping sites in a tropical rainforest of Cameroon, we found that chimpanzees preferentially sleep in trees measuring 40-50 cm in diameter. Regarding height, on average, sleeping trees measured 26 m and sleeping platforms were built at 16 m. To build sleeping platforms, chimpanzees preferred four tree species, which represent less than 3% of tree species in the study area. We demonstrate that the variation in abundance of tree species and the vertical and horizontal structure of the vegetation drive chimpanzee sleeping site selection. It was previously thought that preference for vegetation types was the driver of sleeping site selection in chimpanzees. However, results from this study indicate that the importance of vegetation types in sleeping site selection depends on their botanical characteristics including the variation in tree size, the abundance of all trees, the abundance of sleeping trees, and the occurrence of preferred sleeping tree species, which predict sleeping site selection. The height and diameter of trees are considered by chimpanzees when selecting a particular tree for sleeping and when selecting a site with a specific vertical structure. In addition to tree height, the abundance of smaller neighboring trees may also play a role in the chimpanzee antipredation strategy. Our results demonstrate that chimpanzees consider several vegetation parameters to establish sleeping sites.

Local injection of adipose-derived mesenchymal stem cells in silk fibroin solution on the regeneration of lower esophageal sphincter in an animal model of GERD.

Presently, various tissue engineering methods using adult stem cells and biomaterials are being confirmed to regenerate vessels, cardiac muscle, bladder, and intestines. However, there are few studies about the repair of the lower esophageal sphincter (LES) may help alleviate the symptoms of gastroesophageal reflux disease (GERD). This study aims to determine whether Adipose-Derived Stem Cells (ADSCs) combined with regenerated silk fibroin (RSF) solution could regenerate the LES. In vitro, the ADSCs were isolated, identified, and then cultured with an established smooth muscular induction system. In vivo, in the experimental groups, CM-Dil labeled ADSCs or induced ADSCs mixed with RSF solution were injected into the LES of rats after the development of the animal model of GERD respectively. The results showed that ADSCs could be induced into smooth muscular-like cells with the expression of h-caldesmon, calponin, α-smooth muscle actin, and a smooth muscle-myosin heavy chain in vitro. In vivo, the thickness of LES in the experiment rats was much thicker than those in the controlled groups. This result indicated that ADSCs mixed with RSF solution might contribute to the regeneration of the LES, thus reducing the occurrence of GERD.

Artificial intelligence to predict outcomes of head and neck radiotherapy.

Head and neck radiotherapy induces important toxicity, and its efficacy and tolerance vary widely across patients. Advancements in radiotherapy delivery techniques, along with the increased quality and frequency of image guidance, offer a unique opportunity to individualize radiotherapy based on imaging biomarkers, with the aim of improving radiation efficacy while reducing its toxicity. Various artificial intelligence models integrating clinical data and radiomics have shown encouraging results for toxicity and cancer control outcomes prediction in head and neck cancer radiotherapy. Clinical implementation of these models could lead to individualized risk-based therapeutic decision making, but the reliability of the current studies is limited. Understanding, validating and expanding these models to larger multi-institutional data sets and testing them in the context of clinical trials is needed to ensure safe clinical implementation. This review summarizes the current state of the art of machine learning models for prediction of head and neck cancer radiotherapy outcomes.

Developing and Validating Models to Predict Progression to Proliferative Diabetic Retinopathy.

Purpose: To develop models for progression of nonproliferative diabetic retinopathy (NPDR) to proliferative diabetic retinopathy (PDR) and determine if incorporating updated information improves model performance. Design: Retrospective cohort study. Participants: Electronic health record (EHR) data from a tertiary academic center, University of California San Francisco (UCSF), and a safety-net hospital, Zuckerberg San Francisco General (ZSFG) Hospital were used to identify patients with a diagnosis of NPDR, age ≥ 18 years, a diagnosis of type 1 or 2 diabetes mellitus, ≥ 6 months of ophthalmology follow-up, and no prior diagnosis of PDR before the index date (date of first NPDR diagnosis in the EHR). Methods: Four survival models were developed: Cox proportional hazards, Cox with backward selection, Cox with LASSO regression and Random Survival Forest. For each model, three variable sets were compared to determine the impact of including updated clinical information: Static0 (data up to the index date), Static6m (data updated 6 months after the index date), and Dynamic (data in Static0 plus data change during the 6-month period). The UCSF data were split into 80% training and 20% testing (internal validation). The ZSFG data were used for external validation. Model performance was evaluated by the Harrell's concordance index (C-Index). Main Outcome Measures: Time to PDR. Results: The UCSF cohort included 1130 patients and 92 (8.1%) patients progressed to PDR. The ZSFG cohort included 687 patients and 30 (4.4%) patients progressed to PDR. All models performed similarly (C-indices ∼ 0.70) in internal validation. The random survival forest with Static6m set performed best in external validation (C-index 0.76). Insurance and age were selected or ranked as highly important by all models. Other key predictors were NPDR severity, diabetic neuropathy, number of strokes, mean Hemoglobin A1c, and number of hospital admissions. Conclusions: Our models for progression of NPDR to PDR achieved acceptable predictive performance and validated well in an external setting. Updating the baseline variables with new clinical information did not consistently improve the predictive performance. Financial Disclosures: Proprietary or commercial disclosure may be found after the references.

Improved Quantitative Analysis Method for Magnetic Particle Imaging Based on Deblurring and Region Scalable Fitting.

PURPOSE: Magnetic particle imaging (MPI) is a technique for imaging magnetic particle concentration distribution. It has the advantages of high sensitivity, no signal attenuation with depth, and no ionizing radiation. Although MPI has been widely used in the biomedical field, accurate image analysis has been challenging due to its anisotropic point spread function (PSF). The purpose of this study is to propose an MPI image restoring and segmentation method to facilitate a more precise quantitative evaluation of the magnetic particle imaging in vivo. PROCEDURES: We proposed a DeRSF method that combined deblurring and region scalable fitting (RSF) to determine the imaging tracer distribution. Then a uniform erosion and scaling criterion was established based on simulation experiments to correct the segmentation results, which was further validated on phantom imaging. Finally, we imaged the MPI tracer at gradient concentrations to establish the calibration curve between the MPI signal and iron mass for iron quantification in phantom and in vivo imaging. RESULTS: The phantom imaging experiments showed that our method achieved improved segmentation performance. The mean value of the dice coefficients for segmentation was up to 0.86, demonstrating that our method can accurately map and quantify the distribution of the tracer. Moreover, the iron quantification on both phantom and in vivo mouse imaging was realized with the minimal error of 5.50%, by our established calibration curve. CONCLUSIONS: Our proposed DeRSF method was successfully used for improved MPI quantitative analysis. More importantly, this method also showed accurate quantitative results on images with different shapes and tracer concentrations in both phantom and in vivo data, which laid the foundation for the biomedical study of MPI.