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Steatotic liver disease (SLD), formerly named fatty liver disease, has a prevalence estimated at 30–38% in adults. Detection of SLD is important, since prompt initiation of treatment can stop disease progression, lead to a reduction in adverse outcomes, and reduce the economic burden associated with the disease. We report the development of a novel Deep Learning (DL) method for the prediction of SLD, which consists of transforming the input variables from tabular data into images, with the goal of using the pattern recognition power of DL models to reach the best prediction performance. The dataset used in this study includes registries from 2,999 patients. The data of each patient, originally represented as a vector, is converted into an image replicating each variable in rows and columns. Our DL models reach better results compared to those of traditional ML models at various levels of sensitivity and specificity. A sensitivity of 0.9497, a specificity of 0.6417, and an AUCROC of 0.8662 were reached with one DL model. We also achieved significantly better results relative to those obtained with the Hepatic Steatosis Index (HSI). Our DL models reach higher AUCROC values compared to those of the traditional ML models, and also with respect to those obtained with HSI.

Background Early prediction of Gestational Diabetes Mellitus (GDM) risk is of particular importance as it may enable more efficacious interventions and reduce cumulative injury to mother and fetus. The aim of this study is to develop machine learning (ML) models, for the early prediction of GDM using widely available variables, facilitating early intervention, and making possible to apply the prediction models in places where there is no access to more complex examinations. Methods The dataset used in this study includes registries from 1,611 pregnancies. Twelve different ML models and their hyperparameters were optimized to achieve early and high prediction performance of GDM. A data augmentation method was used in training to improve prediction results. Three methods were used to select the most relevant variables for GDM prediction. After training, the models ranked with the highest Area under the Receiver Operating Characteristic Curve (AUCROC), were assessed on the validation set. Models with the best results were assessed in the test set as a measure of generalization performance. Results Our method allows identifying many possible models for various levels of sensitivity and specificity. Four models achieved a high sensitivity of 0.82, a specificity in the range 0.72–0.74, accuracy between 0.73–0.75, and AUCROC of 0.81. These models required between 7 and 12 input variables. Another possible choice could be a model with sensitivity of 0.89 that requires just 5 variables reaching an accuracy of 0.65, a specificity of 0.62, and AUCROC of 0.82. Conclusions The principal findings of our study are: Early prediction of GDM within early stages of pregnancy using regular examinations/exams; the development and optimization of twelve different ML models and their hyperparameters to achieve the highest prediction performance; a novel data augmentation method is proposed to allow reaching excellent GDM prediction results with various models.

Prediction of Large for Gestational Age (LGA) risk is important as it can enable earlier, more effective interventions, and avoid or mitigate cumulative injury to both mother and baby at the time of delivery. The goal of this research is to improve the prediction of LGA using machine learning (ML) models and variables that are widely available as that allow for broad intervention in late pregnancy and during delivery. An improved prediction of LGA was achieved using twelve ML models with hyperparameter optimization. Also, to improve the LGA prediction a data augmentation method was employed in the training set. Additionally, improvement in LGA prediction was obtained with four variable selection methods employed to identify the most significant variables. To rank the best models on the validation set after training, the Area under the Receiver Operating Characteristic Curve (AUROC) was used. Finally, to assess the generalization performance, the best models were evaluated on the test set. Our method enabled us to identify several models with high sensitivity and specificity. The best models included those that achieved a sensitivity of 0.84, a specificity of 0.84, an accuracy of 0.84, and AUCROC 0.83, requiring 14 variables. Another model reached an accuracy of 0.87, a sensitivity of 0.71, and a specificity of 0.90 with an AUCROC of 0.83 (14 variables). Additionally, a model with a sensitivity of 0.58, the same as that described by Hadlock et al. [1] for ultrasound, required 10 variables, and reached an accuracy of 0.91, a specificity of 0.97, and an AUCROC of 0.86. Both models included maternal BMI (body mass index), First Control, Maternal Weight, BMI Last Control, and EFW (estimated fetal weight) as the most important variables. The main contributions of our study include the prediction of LGA using data obtained from standard clinical evaluations during prenatal care, as well as the development ML models, tuning the hyperparameters to improve prediction results, thus achieving high levels of sensitivity and specificity. To achieve optimal LGA prediction outcomes across different models, a data augmentation approach is introduced, an important improvement in LGA prediction over using only Hadlock's ultrasound formula.

Patients undergoing metabolic surgery have factors ranging from anatomo-surgical, endocrine metabolic, eating patterns and physical activity, mental health and psychological factors. Some of the latter can explain the possible pathophysiological neuroendocrine, metabolic, and adaptive mechanisms that cause the high prevalence of weight regain in postbariatric patients. Even metabolic surgery has proven to be effective in reducing excess weight in patients with obesity; some of them regain weight after this intervention. In this vein, several studies have been conducted to search factors and mechanisms involved in weight regain, to stablish strategies to manage this complication by combining metabolic surgery with either lifestyle changes, behavioral therapies, pharmacotherapy, endoscopic interventions, or finally, surgical revision. The aim of this revision is to describe certain aspects and mechanisms behind weight regain after metabolic surgery, along with preventive and therapeutic strategies for this complication.

Antecedentes: Desde hace más de dos décadas se reportan observaciones que plantean que la obesidad se asocia a la presencia de infecciones. Sin embargo, los diferentes hallazgos han sido contradictorios y la dirección de la posible asociación no ha sido clarificada. En nuestro país no se encontraron trabajos al respecto que contribuyan a aclarar esta pregunta. Métodos: se realizó una serie de casos retrospectivo (N= 4840 con muestreo por conglomerados n= 100), evaluando Índice de Masa Corporal (IMC) e infección por Helicobacter pylori, Strepto- coccus β Hemolítico, Infección Urinaria (IVU) y Vaginosis. Se obtuvieron estadísticos descriptivos y se realizaron cruces de variables para obtener OR. Resultados: 85% sexo femenino, procedentes de la zona andina en un 77% que consultan en su mayoría para lipólisis laser, IMC: 32 (DS 5,4), la relación entre infección y obesidad fue: IVU OR 1,4 (IC 1,02-3,62), (p:0,042), VAGINITIS: OR 1,4 (IC: 1,09-3,019), (p:0,028), Helicobacter pylori: Pearson 0,25 (p:0,064) y Streptocccus: 0,56 (p:0.046). Conclusiones: La presencia de infección Urinaria y la vaginitis se asocian a la obesidad. Estos hallazgos confirman previos estudios, se discuten las implicaciones de los mismos. 

Tumours evade immune control by creating hostile microenvironments that perturb T cell metabolism and effector function 1 – 4 . However, it remains unclear how intra-tumoral T cells integrate and interpret metabolic stress signals. Here we report that ovarian cancer—an aggressive malignancy that is refractory to standard treatments and current immunotherapies 5 – 8 —induces endoplasmic reticulum stress and activates the IRE1α–XBP1 arm of the unfolded protein response 9 , 10 in T cells to control their mitochondrial respiration and anti-tumour function. In T cells isolated from specimens collected from patients with ovarian cancer, upregulation of XBP1 was associated with decreased infiltration of T cells into tumours and with reduced IFNG mRNA expression. Malignant ascites fluid obtained from patients with ovarian cancer inhibited glucose uptake and caused N -linked protein glycosylation defects in T cells, which triggered IRE1α–XBP1 activation that suppressed mitochondrial activity and IFNγ production. Mechanistically, induction of XBP1 regulated the abundance of glutamine carriers and thus limited the influx of glutamine that is necessary to sustain mitochondrial respiration in T cells under glucose-deprived conditions. Restoring N -linked protein glycosylation, abrogating IRE1α–XBP1 activation or enforcing expression of glutamine transporters enhanced mitochondrial respiration in human T cells exposed to ovarian cancer ascites. XBP1-deficient T cells in the metastatic ovarian cancer milieu exhibited global transcriptional reprogramming and improved effector capacity. Accordingly, mice that bear ovarian cancer and lack XBP1 selectively in T cells demonstrate superior anti-tumour immunity, delayed malignant progression and increased overall survival. Controlling endoplasmic reticulum stress or targeting IRE1α–XBP1 signalling may help to restore the metabolic fitness and anti-tumour capacity of T cells in cancer hosts. In human and mouse models of ovarian cancer, endoplasmic reticulum stress and the activation of the IRE1α–XBP1 pathway decreases the metabolic fitness of T cells and limits their anti-tumour functions.

Osteoblast differentiation is achieved by activating a transcriptional network in which Dlx5 , Runx2 and Osx/SP7 have fundamental roles. The tumour suppressor p53 exerts a repressive effect on bone development and remodelling through an unknown mechanism that inhibits the osteoblast differentiation programme. Here we report a physical and functional interaction between Osx and p53 gene products. Physical interaction was found between overexpressed proteins and involved a region adjacent to the OSX zinc fingers and the DNA-binding domain of p53. This interaction results in a p53-mediated repression of OSX transcriptional activity leading to a downregulation of the osteogenic programme. Moreover, we show that p53 is also able to repress key osteoblastic genes in Runx2 -deficient osteoblasts. The ability of p53 to suppress osteogenesis is independent of its DNA recognition ability but requires a native conformation of p53, as a conformational missense mutant failed to inhibit OSX. Our data further demonstrates that p53 inhibits OSX binding to their responsive Sp1/GC-rich sites in the promoters of their osteogenic target genes, such as IBSP or COL1A1 . Moreover, p53 interaction to OSX sequesters OSX from binding to DLX5. This competition blocks the ability of OSX to act as a cofactor of DLX5 to activate homeodomain-containing promoters. Altogether, our data support a model wherein p53 represses OSX–DNA binding and DLX5–OSX interaction, and thereby deregulates the osteogenic transcriptional network. This mechanism might have relevant roles in bone pathologies associated to osteosarcomas and ageing.

This paper provides the first estimates of intergenerational earnings mobility in Chile using administrative data linking parents’ and children’s earnings from the formal private sector. We calculate mobility measures across the earnings distribution, revealing high mobility in the bottom 80% and 65% of the parents’ and children’s distribution, respectively. However, we observe significant persistence in the upper tail of the earnings distribution. Additionally, we identify notable gender heterogeneities in these mobility patterns. Specifically, the intergenerational mobility gender gap shows a nonlinear relationship with respect to parental earnings. Furthermore, we find that differences in mobility between the upper tail of the earnings distribution and the rest of the population are more pronounced for daughters than for sons. These findings suggest that the dynamics of gender-based mobility at the upper tail of the earnings distribution differ from those observed in the rest of the population.

Bacterial meningitis is an infectious pathology that remains a public health challenge. The most frequent etiological agent is Streptococcus pneumoniae, which is also associated with higher rates of mortality and sequels. However, less is known about the clinical presentation of atypical non-pneumoniae streptococcal meningitis. Here, we studied a 23-year-old man with no medical background who presented with projectile vomiting, states of consciousness alteration, unilateral cranial nerve palsy, and meningeal signs. Neuroimaging showed tonsillar herniation, regions of empyema, right transverse and sigmoid sinuses thrombosis, and multiple arterial subcortical infarcts. Cerebrospinal fluid suggested bacterial infection; blood and abscess cultures were positive for Streptococcus constellatus. The patient received antibiotics with no clinical improvement. He deteriorated over the following days, the abolishment of brainstem reflexes was observed, and brain death was declared. Streptococcal meningitis produced by atypical species is a potential cause of lethal cerebrovascular complications, even in immunocompetent patients.

In this work, we proposed a set of techniques and respective methodologies to investigate the physicochemical properties of six models of ultra high molecular weight polyethylene (UHMWPE) acetabular liners commercially available. The correlation between chemical properties, sterilization techniques, and potential performance of material were proposed. Optical microscopy, density, Fourier transform infrared spectroscopy (FTIR), differential scanning calorimetry (DSC), and swell test were employed to evaluate the medical product and manufacturing process safety characteristics. Microscopy demonstrated that all samples went through a satisfactory consolidation process. FTIR revealed that only three models were not exposed to gamma irradiation, and consequently were not cross-linked. On the other hand, three other samples were exposed to gamma irradiation in which on presented an elevated oxidation index. This result was further investigated through the packaging evaluation where it was found that the sample with higher IOX was exposed to an oxygen atmosphere during sterilization and/or shelf life. Samples exposed to sterilization technique in atmosphere with oxygen were observed to have higher oxidation indexes, which are associated with material degradation in vivo. This work demonstrated the relevance of combining specific physicochemical techniques for process mapping and to characterize UHMWPE acetabular liners.