Miracle-dz/UniFAR-FTUs huggingface dataset:
Acute respiratory distress syndrome and acute lung injury in a trauma population with and without long bone fractures

Introduction: Trauma is the leading cause of death in persons under the age of 45. Recovery in patients who survive initial trauma are frequently complicated by sequelae of injury that increases susceptibility to infection and inflammation. Uncontrolled inflammation can advance into life-threatening organ failure, including acute respiratory distress syndrome (ARDS). Similarities exist between biomarkers established in the etiology of acute respiratory distress syndrome and those identified in the acute inflammatory and healing phase of bone fractures. This study investigates the impact of long bone fractures on the development of acute respiratory distress syndrome where it is hypothesized that patients with long bone fractures would have different biomarker profiles and increased development of lung injury compared to patients without long bone fractures. Methods: This is a retrospective data analysis of patients from an observational data repository from three trauma centers. Trauma patients with and without long bone fractures were matched and analyzed for the presence of known biomarkers of acute respiratory distress syndrome and for the development of acute respiratory distress syndrome. Results: There were no differences in overall acute respiratory distress syndrome development or hospital outcomes, however long bone fracture patients had a 2.35-fold higher hazard ratio of acute respiratory distress syndrome in the first 10 hospital days. There was a statistically significant increase in the levels of IL-6 in patients with long bone fractures (p = .0007). Structural equations modeling demonstrated that IL-6 was positively influenced by long bone fractures and IL-8. Conclusion: The presence of long bone fractures did not result in differences in the overall development of acute respiratory distress syndrome or hospital outcomes, though was found to have an increased hazard ratio for acute respiratory distress syndrome development in the first 10 days. Further research is needed to better characterize the relationship between varying cytokine profiles and the development of acute respiratory distress syndrome in a trauma population.
Prognostic significance of preoperative C-reactive protein to albumin ratio in non-small cell lung cancer patients: A meta-analysis

Objective We aimed to assess whether C-reactive protein to albumin ratio (CAR) is associated with the clinicopathology and prognosis of patients with non-small cell lung cancer (NSCLC) after surgery. Methods Several literature databases were searched for eligible studies in English and Chinese published before September 1, 2022, according to the inclusion and exclusion criteria. The pooled odds ratios (ORs) with 95% confidence interval (CI) were calculated to assess the association of CAR in lung cancer with clinicopathological characteristics including age, sex, smoking status, lymph node metastasis, and American Association of Cancer (AJCC) stage. The pooled hazard ratios (HRs) with 95% CI were calculated to assess the association of CAR with prognosis in lung cancer. Publication bias was assessed using Egger's test. Results Overall, 9 studies involving 3,359 NSCLC patients were included in this meta-analysis. The CAR was observed to be higher in males, smokers, and patients with lymph node metastasis and correlated with advanced AJCC stage but not with age. Moreover, a high CAR correlated with poor survival. No publication bias was observed in this meta-analysis. Conclusions CAR was observed to be a significant biomarker for prognosis and associated with clinicopathological characteristics in patients with NSCLC after surgery.
A novel risk model based on the correlation between the expression of basement membrane genes and immune infiltration to predict the invasiveness of pituitary adenomas

Objective Invasive pituitary adenomas (IPAs) are common tumors of the nervous system tumors for which invasive growth can lead to difficult total resection and a high recurrence rate. The basement membrane (BM) is a special type of extracellular matrix and plays an important role in the invasion of pituitary adenomas (PAs). The aim of this study was to develop a risk model for predicting the invasiveness of PAs by analyzing the correlation between the expression of BM genes and immune infiltration. Methods Four datasets, featuring samples IPAs and non-invasive pituitary adenomas (NIPAs), were obtained from the Gene Expression Omnibus database (GEO). R software was then used to identify differentially expressed genes (DEGs) and analyze their functional enrichment. Protein-protein interaction (PPI) network was used to screen BM genes, which were analyzed for immune infiltration; this led to the generation of a risk model based on the correlation between the expression of BM genes and immunity. A calibration curve and receiver operating characteristic (ROC) curve were used to evaluate and validate the model. Subsequently, the differential expression levels of BM genes between IPA and NIPA samples collected in surgery were verified by Quantitative Polymerase Chain Reaction (qPCR) and the prediction model was further evaluated. Finally, based on our analysis, we recommend potential drug targets for the treatment of IPAs. Results The merged dataset identified 248 DEGs that were mainly enriching in signal transduction, the extracellular matrix and channel activity. The PPI network identified 11 BM genes from the DEGs: SPARCL1, GPC3, LAMA1, SDC4, GPC4, ADAMTS8, LAMA2, LAMC3, SMOC1, LUM and THBS2. Based on the complex correlation between these 11 genes and immune infiltration, a risk model was established to predict PAs invasiveness. Calibration curve and ROC curve analysis (area under the curve [AUC]: 0.7886194) confirmed the good predictive ability of the model. The consistency between the qPCR results and the bioinformatics results confirmed the reliability of data mining. Conclusion Using a variety of bioinformatics methods, we developed a novel risk model to predict the probability of PAs invasion based on the correlation between 11 BM genes and immune infiltration. These findings may facilitate closer surveillance and early diagnosis to prevent or treat IPAs in patients and improve the clinical awareness of patients at high risk of IPAs.
Deep learning radiomic analysis of DCE-MRI combined with clinical characteristics predicts pathological complete response to neoadjuvant chemotherapy in breast cancer

Objective The aim of this study was to develop and validate a deep learning-based radiomic (DLR) model combined with clinical characteristics for predicting pathological complete response (pCR) to neoadjuvant chemotherapy (NAC) in breast cancer. For early prediction of pCR, the DLR model was based on pre-treatment and early treatment dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) data. Materials and methods This retrospective study included 95 women (mean age, 48.1 years; range, 29–77 years) who underwent DCE-MRI before (pre-treatment) and after two or three cycles of NAC (early treatment) from 2018 to 2021. The patients in this study were randomly divided into a training cohort (n=67) and a validation cohort (n=28) at a ratio of 7:3. Deep learning and handcrafted features were extracted from pre- and early treatment DCE-MRI contoured lesions. These features contribute to the construction of radiomic signature RS1 and RS2 representing information from different periods. Mutual information and least absolute shrinkage and selection operator regression were used for feature selection. A combined model was then developed based on the DCE-MRI features and clinical characteristics. The performance of the models was assessed using the area under the receiver operating characteristic curve (AUC) and compared using the DeLong test. Results The overall pCR rate was 25.3% (24/95). One radiomic feature and three deep learning features in RS1, five radiomic features and 11 deep learning features in RS2, and five clinical characteristics remained in the feature selection. The performance of the DLR model combining pre- and early treatment information (AUC=0.900) was better than that of RS1 (AUC=0.644, P=0.068) and slightly higher that of RS2 (AUC=0.888, P=0.604) in the validation cohort. The combined model including pre- and early treatment information and clinical characteristics showed the best ability with an AUC of 0.925 in the validation cohort. Conclusion The combined model integrating pre-treatment, early treatment DCE-MRI data, and clinical characteristics showed good performance in predicting pCR to NAC in patients with breast cancer. Early treatment DCE-MRI and clinical characteristics may play an important role in evaluating the outcomes of NAC by predicting pCR.
Mild behavioral impairment linked to progression to Alzheimer’s disease and cortical thinning in amnestic mild cognitive impairment

Background Mild behavioral impairment (MBI) is a neurobehavioral syndrome characterized by later life emergence of sustained neuropsychiatric symptoms, as an at-risk state for dementia. However, the associations between MBI and a risk of progression to Alzheimer’s disease (AD) and its neuroanatomical correlates in mild cognitive impairment (MCI) are still unclear. Method A total 1,184 older adults with amnestic MCI was followed for a mean of 3.1 ± 2.0 years. MBI was approximated using a transformation algorithm for the Neuropsychiatric Inventory at baseline. A two-step cluster analysis was used to identify subgroups of individuals with amnestic MCI based on profiles of 5 MBI domain symptoms (decreased motivation, affective dysregulation, impulse dyscontrol, social inappropriateness, abnormal perception/thought content). A Cox regression analysis was applied to investigate differences in the risk of progression to AD between subgroups. A subset of participants (n = 202) underwent 3D T1-weighted MRI scans at baseline and cortical thickness was compared between the subgroups of amnestic MCI patients. Result The cluster analysis classified the patients into 3 groups: (1) patients without any MBI domain symptoms (47.4%, asymptomatic group); (2) those with only affective dysregulation (29.4%, affective dysregulation group); (3) those with multiple MBI domain symptoms, particularly affective dysregulation, decreased motivation and impulse dyscontrol (23.2%, complex group). Compared to the asymptomatic group, the complex group was associated with a higher risk of progression to AD (hazard ratio = 2.541 [1.904–3.392], p < 0.001), but the affective dysregulation group was not (1.214 [0.883–1.670], p = 0.232). In cortical thickness analysis, the complex group revealed cortical thinning bilaterally in the inferior parietal, lateral occipital, lateral superior temporal, and frontopolar regions compared with the affective dysregulation group. Conclusion The multiple co-occuring MBI domains in individuals with amnestic MCI are associated with a higher risk of progression to AD and cortical thinning in temporal, parietal and frontal areas. These results suggest that evaluation of MBI could be useful for risk stratification for AD and appropriate intervention in MCI individuals.
Growth performance, survivability and profitability of improved smallholder chicken genetics in Nigeria: A COVID-19 intervention study

The impact of COVID-19 pandemic on smallholder farming households (SFH) includes increased poverty, and loss of livelihoods. Provision of livestock to SFH is a helpful intervention to mitigate this impact. This study provided a total of 150 smallholder poultry farmers, randomly selected from three states (Kebbi, Nasarawa, and Imo) in Nigeria, with ten 5-week-old chickens (mixed sexes) each, of either FUNAAB Alpha or Noiler chicken genetics. The improved, dual-purpose chickens were evaluated for growth performance (GP), survivability and profitability. The birds were managed under semi-scavenging production system. Body weight, mortality, and cost of production (COP) were recorded every 4 weeks until 21 weeks of age. Profitability was a function of the COP, and the selling price for live-birds (cocks). Body weight of Noiler (1,927 g) birds was not significantly (p > .05) higher than FUNAAB Alpha (1,792 g) at 21 weeks. Agroecology and genetics had significant (p < .05) effects on GP and survivability. Survivability of FUNAAB Alpha was higher (p < .05) than Noiler, with Nasarawa (81%–96%), having the highest (p < .0001) survival rate compared to Imo (62%–81%), and Kebbi (58%–75%). At 21 weeks, the number of cocks and hens differed significantly (p < .05) within the states (Imo: 2.4 ± .2 and 5.4 ± .3; Kebbi: 2.6 ± .2 and 5.5 ± .3; and Nasarawa: 2.9 ± .2 and 5.8 ± .3). Nasarawa (NGN 7,808; USD 19) ranked best for profitability, followed by Kebbi (NGN 6,545; USD 16) and Imo (NGN 5,875; USD 14). Overall, this study demonstrates that provision of improved chickens to vulnerable SFH in Nigeria holds great potential for economic growth, and resilience during emergencies, such as the COVID-19 pandemic.
Differences in dietary patterns related to metabolic health by gut microbial enterotypes of Korean adults

Diet has a profound impact on the progression of metabolic syndrome (MetS) into various diseases. The gut microbiota could modulate the effect of diet on metabolic health. We examined whether dietary patterns related to MetS differed according to gut microbial enterotypes among 348 Korean adults aged 18–60 years recruited between 2018∼2021 in a cross-sectional study. The enterotype of each participant was identified based on 16S rRNA gut microbiota data. The main dietary pattern predicting MetS (MetS-DP) of each enterotype was derived using reduced-rank regression (RRR) models. In the RRR models, 27 food group intakes assessed by a semi-quantitative food frequency questionnaire and MetS prediction markers including triglyceride to high-density lipoprotein cholesterol (TG/HDL) ratio and homeostatic model assessment for insulin resistance (HOMA-IR) were used as predictor and response variables, respectively. The MetS-DP extracted in Bacteroides enterotype (B-type) was characterized by high consumption of refined white rice and low consumption of eggs, vegetables, and mushrooms. The MetS-DP derived among Prevotella enterotype (P-type) was characterized by a high intake of sugary food and low intakes of bread, fermented legumes, and fermented vegetables. The MetS-DP of B-type was positively associated with metabolic unhealthy status (ORT3 vs. T1 = 3.5; 95% CI = 1.5–8.2), comparing the highest tertile to the lowest tertile. Although it was not significantly associated with overall metabolic unhealthy status, the MetS-DP of P-type was positively associated with hyperglycemia risk (ORT3 vs. T1 = 6.2; 95% CI = 1.6–24.3). These results suggest that MetS-DP may differ according to the gut microbial enterotype of each individual. If such associations are found to be causal, personalized nutrition guidelines based on the enterotypes could be recommended to prevent MetS.
Virome and metagenomic analysis reveal the distinct distribution of microbiota in human fetal gut during gestation

Studies have shown that fetal immune cell activation may result from potential exposure to microbes, although the presence of microbes in fetus has been a controversial topic. Here, we combined metagenomic and virome techniques to investigate the presence of bacteria and viruses in fetal tissues (small intestine, cecum, and rectum). We found that the fetal gut is not a sterile environment and has a low abundance but metabolically rich microbiome. Specifically, Proteobacteria and Actinobacteria were the dominant bacteria phyla of fetal gut. In total, 700 species viruses were detected, and Human betaherpesvirus 5 was the most abundant eukaryotic viruses. Especially, we first identified Methanobrevibacter smithii in fetal gut. Through the comparison with adults’ gut microbiota we found that Firmicutes and Bacteroidetes gradually became the main force of gut microbiota during the process of growth and development. Interestingly, 6 antibiotic resistance genes were shared by the fetus and adults. Our results indicate the presence of microbes in the fetal gut and demonstrate the diversity of bacteria, archaea and viruses, which provide support for the studies related to early fetal immunity. This study further explores the specific composition of viruses in the fetal gut and the similarities between fetal and adults’ gut microbiota, which is valuable for understanding human fetal immunity development during gestation.
The association of periodontal diseases and Sjogren’s syndrome: A systematic review and meta-analysis

Background The relationship between periodontal diseases and Sjogren’s syndrome were found inconsistent in current studies. Our objective is to clarify the relationship between periodontal diseases and Sjogren’s syndrome. Methods A systematic review was performed and reported according to Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA). Electronic databases (EMBASE, PubMed, Web of Science, and Cochrane Library, from inceptions until 24 November 2021) were searched. The Newcastle-Ottawa Scale (NOS) and Agency for Healthcare Research and Quality (AHRQ) were applied to evaluate the quality of studies. Quality assessment of the certainty of evidence was performed based on the Grading of Recommendations Assessment, Development, and Evaluation (GRADE) guidelines. When the output is the ratio, Odds ratio (OR) of periodontal diseases with Sjogren’s syndrome were calculated. When the output is the mean, weighted mean difference (WMD) of periodontal diseases with Sjogren’s syndrome was calculated. We conducted meta-analysis and estimated the pool sensitivity. Begg’s test was used to test the possibility of publication bias. We also carried out meta-regression to clarify the source of heterogeneity (I2 > 50%). Finally, we performed a trial sequential analysis (TSA) to identify the false positive or false negative outcomes that might occur during repeated updates. Results 21 studies were included in this systematic review, with a total of 11435 subjects. Meta-analysis of 5 studies showed that there is a positive correlation between periodontitis and Sjogren’s syndrome (OR = 2.12, 95% CI = 1.43–3.17; 5 studies, 6927 participants; low certainty of evidence). Meta-analysis of 16 studies showed that the periodontal condition of patients with Sjogren’s syndrome was worse compared with the control group, and the scores of clinical periodontal parameters were relatively high. Conclusion Sjogren’s syndrome patients seem to be more likely to be diagnosed with periodontal diseases. However, our results should be interpreted with caution considering the high heterogeneity. Systematic review registration [https://www.crd. york.ac. uk/prospero/], identifier [CRD42021261322].
Foundational concepts in person-machine teaming

As we enter an age where the behavior and capabilities of artificial intelligence and autonomous system technologies become ever more sophisticated, cooperation, collaboration, and teaming between people and these machines is rising to the forefront of critical research areas. People engage socially with almost everything with which they interact. However, unlike animals, machines do not share the experiential aspects of sociality. Experiential robotics identifies the need to develop machines that not only learn from their own experience, but can learn from the experience of people in interactions, wherein these experiences are primarily social. In this paper, we argue, therefore, for the need to place experiential considerations in interaction, cooperation, and teaming as the basis of the design and engineering of person-machine teams. We first explore the importance of semantics in driving engineering approaches to robot development. Then, we examine differences in the usage of relevant terms like trust and ethics between engineering and social science approaches to lay out implications for the development of autonomous, experiential systems.
Organic persistent room temperature phosphorescence enabled by carbazole impurity

The molecular design of metal-free organic phosphors is essential for realizing persistent room-temperature phosphorescence (pRTP) despite its spin-forbidden nature. A series of halobenzonitrile–carbazoles has been prepared following a one-pot nucleophilic substitution protocol involving commercially available and laboratory-synthesized carbazoles. We demonstrate how halo- and cyano-substituents affect the molecular geometry in the crystal lattice, resulting in tilt and/or twist of the carbazole with respect to the phenyl moiety. Compounds obtained from the commercially available carbazole result in efficient pRTP of organic phosphors with a high quantum yield of up to 22% and a long excited state lifetime of up to 0.22 s. Compounds obtained from the laboratory-synthesized carbazole exhibit thermally activated delayed fluorescence with an excited state lifetime in the millisecond range. In-depth photophysical studies reveal that luminescence originates from the mixed locally excited state (3LE, nπ*)/charge transfer state.
Prediction of flash flood susceptibility using integrating analytic hierarchy process (AHP) and frequency ratio (FR) algorithms

The landscape of Pakistan is vulnerable to flood and periodically affected by floods of different magnitudes. The aim of this study was aimed to assess the flash flood susceptibility of district Jhelum, Punjab, Pakistan using geospatial model and Frequency Ratio and Analytical Hierarchy Process. Also, the study considered eight most influential flood-causing parameters are Digital Elevation Model, slop, distance from the river, drainage density, Land use/Land cover, geology, soil resistivity (soil consisting of different rocks and soil formation) and rainfall deviation. The rainfall data was collected from weather stations in the vicinity of the study area. Estimated weight was allotted to each flood-inducing factors with the help of AHP and FR. Through the use of the overlay analysis, each of the factors were brought together, and the value of drainage density was awarded the maximum possible score. According to the study several areas of the region based on the parameters have been classified in flood zones viz, very high risk, high risk, moderate risk, low risk, and very low risk. In the light of the results obtained, 4% of the study area that accounts for 86.25 km2 is at high risk of flood. The areas like Bagham, Sohawa, Domeli, Turkai, Jogi Tillas, Chang Wala, Dandot Khewra were located at the very high elevation. Whereas Potha, Samothi, Chaklana, Bagrian, Tilla Jogian, Nandna, Rawal high-risk zones and have been damaged badly in the flood history of the area. This study is the first of its kind conducted on the Jhelum District and provides guidelines for disaster management authorities and response agencies, infrastructure planners, watershed management, and climatologists.