Does NLRP1 Inflammasome Activation in Immune Cells in Kidney Transplantation Relate with Donor Organ Age?

Inflammation causes a wide range of health disorders. In this process, the formation of inflammasome complexes plays a key role. Although inflammasomes have been extensively studied during kidney disease, their role in kidney transplantation has not been fully elucidated. In this study, we evaluate the gene and protein expression of several components of the inflammasome pathway before and at several time points after kidney transplantation in a cohort of patients of different ages and receiving an organ from older or younger donors. Our findings indicate the activation of the NLRP1 inflammasome in several immune cell population, monocytes and CD4+ and CD8+ cells mainly, in renal transplant, and its level increases gradually in patients who receive an older organ, whereas it has the opposite effect on older patients who receive a younger organ. Despite treatment with immunosuppressants, inflammation persists in some patients. These results lead to the hypothesis that the donor's age is a critical factor in post-transplant inflammasome activation and that specific NLRP1 inflammasome inhibitors should be considered to increase the success of kidney transplantation long-term.

Does Donor Age Have Effects on Senescence Biomarkers in Kidney-Transplanted Patients?

Renal transplantation is an effective treatment for severe chronic kidney diseases. However, young patients often face a scarcity of kidneys from donors of similar age, resulting in the transplantation of older organs, which increase the risk of graft rejection and several complications compared with older individuals who receive kidneys from donors of similar age or younger. This article focuses on studying different senescence biomarkers in donors and patients who received kidneys from various age ranges complying with the STROBE requirements. We studied 61 patients subjected to renal transplant isolating blood samples 24 h before, and 24 h, 3 days, 7 days, 3 months, and 6 months after transplant. The patients were divided into three groups: older donor than the patient (Old Donor), younger donor than the patient (Young Donor), and similar age (Matched). We studied different senescence markers such as p16, p21, interleukin 6 (IL-6), and senescence-associated secretory phenotype (SASP) release. Young patients who receive older organs showed increased mRNA and protein expression of the senescence makers. Hence, increased SASP release was also observed in patients from older donor. In contrast, older patients who receive younger organs showed a slow but consistent improvement in their initial senescent phenotype. In addition, macrophage cell model treated with blood-derived serum from patients 6 months after the transplant showed a pro-senescence environment in macrophages proposed by the SASP from the patients. These results lead the hypothesis that senolytics could reduce the presence of senescent cells and mitigate the complications associated with the transplantation of older organs in young patients.

Regularizing transformers with deep probabilistic layers.

Language models (LM) have grown non-stop in the last decade, from sequence-to-sequence architectures to attention-based Transformers. However, regularization is not deeply studied in those structures. In this work, we use a Gaussian Mixture Variational Autoencoder (GMVAE) as a regularizer layer. We study its advantages regarding the depth where it is placed and prove its effectiveness in several scenarios. Experimental result demonstrates that the inclusion of deep generative models within Transformer-based architectures such as BERT, RoBERTa, or XLM-R can bring more versatile models, able to generalize better and achieve improved imputation score in tasks such as SST-2 and TREC or even impute missing/noisy words with richer text.

Benefits of Routine Screening for Renal Cell Carcinoma of Native Kidney in Renal Transplant Recipients.

BACKGROUND: Renal-cell carcinoma (RCC) is the most common solid organ cancer in kidney transplantation recipients (KTRs). BACKGROUND: Analyze the incidence, prognosis, and evolution of primitive kidney RCC in KTRs at our institution. MATERIAL AND METHODS: Observational descriptive retrospective study in which all KTRs from January 2000 to December 2022 were included. We performed an annual abdominal ultrasound in all KTRs. Demographic and clinical data were collected. The surgical approach, location, size, histologic type, and tumor grade were analyzed. We assessed the coexistence of risk factors. We reported the appearance of tumors in other locations, changes in immunosuppressants (IS) after the diagnosis, and survival and recurrence rates observed during follow-up. RESULTS: Eighteen RCCs of native kidneys were diagnosed with an incidence in our population of 1.08%. The majority were men (77.8%), with a mean age of 59.9 years. The pathologic analysis revealed 11 clear cell carcinomas, 6 papillary carcinomas, and 1 chromophobe cell carcinoma. The median tumor size was 2.7 cm. TNM stage was T1aN0M0 in 15 cases. Laparoscopy was performed to remove the tumor in most cases. All our patients underwent changes in IS therapy, with conversion to mammalian target of rapamycin inhibitors when possible and reduction of IS in all of them. After a mean follow-up of 78.6 months, survival was 100% without tumor recurrence. Seven of the patients were diagnosed with a new tumor in another location. CONCLUSION: In our experience, annual abdominal ultrasound in KTRs may be an option for the early detection of RCC in native kidneys.

Psychiatric Profiles of eHealth Users Evaluated Using Data Mining Techniques: Cohort Study.

BACKGROUND: New technologies are changing access to medical records and the relationship between physicians and patients. Professionals can now use e-mental health tools to provide prompt and personalized responses to patients with mental illness. However, there is a lack of knowledge about the digital phenotypes of patients who use e-mental health apps. OBJECTIVE: This study aimed to reveal the profiles of users of a mental health app through machine learning techniques. METHODS: We applied a nonparametric model, the Sparse Poisson Factorization Model, to discover latent features in the response patterns of 2254 psychiatric outpatients to a short self-assessment on general health. The assessment was completed through a mental health app after the first login. RESULTS: The results showed the following four different profiles of patients: (1) all patients had feelings of worthlessness, aggressiveness, and suicidal ideas; (2) one in four reported low energy and difficulties to cope with problems; (3) less than a quarter described depressive symptoms with extremely high scores in suicidal thoughts and aggressiveness; and (4) a small number, possibly with the most severe conditions, reported a combination of all these features. CONCLUSIONS: User profiles did not overlap with clinician-made diagnoses. Since each profile seems to be associated with a different level of severity, the profiles could be useful for the prediction of behavioral risks among users of e-mental health apps.