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Background Determining the postmortem interval (PMI) accurately remains a significant challenge in forensic sciences, especially for intervals greater than 5 years (late PMI). Traditional methods often fail due to the extensive degradation of soft tissues, necessitating reliance on bone material examinations. The precision in estimating PMIs diminishes with time, particularly for intervals between 1 and 5 years, dropping to about 50% accuracy. This study aims to address this issue by identifying key protein biomarkers through proteomics and machine learning, ultimately enhancing the accuracy of PMI estimation for intervals exceeding 15 years. Methods Proteomic analysis was conducted using LC–MS/MS on skeletal remains, specifically focusing on the tibia and ribs. Protein identification was performed using two strategies: a tryptic-specific search and a semitryptic search, the latter being particularly beneficial in cases of natural protein degradation. The Random Forest algorithm was used to model protein abundance data, enabling the prediction of PMI. A thorough screening process, combining importance scores and SHAP values, was employed to identify the most informative proteins for model’s training and accuracy. Results A minimal set of three biomarkers—K1C13, PGS1, and CO3A1—was identified, significantly improving the prediction accuracy between PMIs of 15 and 20 years. The model, based on protein abundance data from semitryptic peptides in tibia samples, achieved sustained 100% accuracy across 100 iterations. In contrast, non-supervised methods like PCA and MCA did not yield comparable results. Additionally, the use of semitryptic peptides outperformed tryptic peptides, particularly in tibia proteomes, suggesting their potential reliability in late PMI prediction. Conclusions Despite limitations such as sample size and PMI range, this study demonstrates the feasibility of combining proteomics and machine learning for accurate late PMI predictions. Future research should focus on broader PMI ranges and various bone types to further refine and standardize forensic proteomic methodologies for PMI estimation.

Mesenchymal Stem/Stromal Cells (MSC) are promising therapeutic tools for inflammatory diseases due to their potent immunoregulatory capacities. Their suppressive activity mainly depends on inflammatory cues that have been recently associated with changes in MSC bioenergetic status towards a glycolytic metabolism. However, the molecular mechanisms behind this metabolic reprogramming and its impact on MSC therapeutic properties have not been investigated. Human and murine-derived MSC were metabolically reprogramed using pro-inflammatory cytokines, an inhibitor of ATP synthase (oligomycin), or 2-deoxy-D-glucose (2DG). The immunosuppressive activity of these cells was tested using co-culture experiments with pro-inflammatory T cells and with the Delayed-Type Hypersensitivity (DTH) and the Graph versus Host Disease (GVHD) murine models. We found that the oligomycin-mediated pro-glycolytic switch of MSC significantly enhanced their immunosuppressive properties . Conversely, glycolysis inhibition using 2DG significantly reduced MSC immunoregulatory effects. Moreover, , MSC glycolytic reprogramming significantly increased their therapeutic benefit in the DTH and GVHD mouse models. Finally, we demonstrated that the MSC glycolytic switch effect partly depends on the activation of the AMPK signaling pathway. Altogether, our findings show that AMPK-dependent glycolytic reprogramming of MSC using an ATP synthase inhibitor contributes to their immunosuppressive and therapeutic functions, and suggest that pro-glycolytic drugs might be used to improve MSC-based therapy.

Glucokinase (GK), the hexokinase involved in glucose sensing in pancreatic β cells, is also expressed in hypothalamic tanycytes, which cover the ventricular walls of the basal hypothalamus and are implicated in an indirect control of neuronal activity by glucose. Previously, we demonstrated that GK was preferentially localized in tanycyte nuclei in euglycemic rats, which has been reported in hepatocytes and is suggestive of the presence of the GK regulatory protein, GKRP. In the present study, GK intracellular localization in hypothalamic and hepatic tissues of the same rats under several glycemic conditions was compared using confocal microscopy and Western blot analysis. In the hypothalamus, increased GK nuclear localization was observed in hyperglycemic conditions; however, it was primarily localized in the cytoplasm in hepatic tissue under the same conditions. Both GK and GKRP were next cloned from primary cultures of tanycytes. Expression of GK by Escherichia coli revealed a functional cooperative protein with a S0.5 of 10 mM. GKRP, expressed in Saccharomyces cerevisiae, inhibited GK activity in vitro with a Ki 0.2 µM. We also demonstrated increased nuclear reactivity of both GK and GKRP in response to high glucose concentrations in tanycyte cultures. These data were confirmed using Western blot analysis of nuclear extracts. Results indicate that GK undergoes short-term regulation by nuclear compartmentalization. Thus, in tanycytes, GK can act as a molecular switch to arrest cellular responses to increased glucose.

Glucokinase (GK), the hexokinase involved in glucosensing in pancreatic β-cells, is also expressed in arcuate nucleus (AN) neurons and hypothalamic tanycytes, the cells that surround the basal third ventricle (3V). Several lines of evidence suggest that tanycytes may be involved in the regulation of energy homeostasis. Tanycytes have extended cell processes that contact the feeding-regulating neurons in the AN, particularly, agouti-related protein (AgRP), neuropeptide Y (NPY), cocaine- and amphetamine-regulated transcript (CART) and proopiomelanocortin (POMC) neurons. In this study, we developed an adenovirus expressing GK shRNA to inhibit GK expression in vivo . When injected into the 3V of rats, this adenovirus preferentially transduced tanycytes. qRT-PCR and Western blot assays confirmed GK mRNA and protein levels were lower in GK knockdown animals compared to the controls. In response to an intracerebroventricular glucose injection, the mRNA levels of anorexigenic POMC and CART and orexigenic AgRP and NPY neuropeptides were altered in GK knockdown animals. Similarly, food intake, meal duration, frequency of eating events and the cumulative eating time were increased, whereas the intervals between meals were decreased in GK knockdown rats, suggesting a decrease in satiety. Thus, GK expression in the ventricular cells appears to play an important role in feeding behavior.

Background The metabolic reprogramming of mesenchymal stem/stromal cells (MSC) favoring glycolysis has recently emerged as a new approach to improve their immunotherapeutic abilities. This strategy is associated with greater lactate release, and interestingly, recent studies have proposed lactate as a functional suppressive molecule, changing the old paradigm of lactate as a waste product. Therefore, we evaluated the role of lactate as an alternative mediator of MSC immunosuppressive properties and its contribution to the enhanced immunoregulatory activity of glycolytic MSCs. Materials and methods Murine CD4 + T cells from C57BL/6 male mice were differentiated into proinflammatory Th1 or Th17 cells and cultured with either L-lactate, MSCs pretreated or not with the glycolytic inductor, oligomycin, and MSCs pretreated or not with a chemical inhibitor of lactate dehydrogenase A (LDHA), galloflavin or LDH siRNA to prevent lactate production. Additionally, we validated our results using human umbilical cord-derived MSCs (UC-MSCs) in a murine model of delayed type 1 hypersensitivity (DTH). Results Our results showed that 50 mM of exogenous L-lactate inhibited the proliferation rate and phenotype of CD4 + T cell-derived Th1 or Th17 by 40% and 60%, respectively. Moreover, the suppressive activity of both glycolytic and basal MSCs was impaired when LDH activity was reduced. Likewise, in the DTH inflammation model, lactate production was required for MSC anti-inflammatory activity. This lactate dependent-immunosuppressive mechanism was confirmed in UC-MSCs through the inhibition of LDH, which significantly decreased their capacity to control proliferation of activated CD4 + and CD8 + human T cells by 30%. Conclusion These findings identify a new MSC immunosuppressive pathway that is independent of the classical suppressive mechanism and demonstrated that the enhanced suppressive and therapeutic abilities of glycolytic MSCs depend at least in part on lactate production.

An amendment to this paper has been published and can be accessed via a link at the top of the paper.An amendment to this paper has been published and can be accessed via a link at the top of the paper.

Glucose homeostasis is performed by specialized cells types that detect and respond to changes in systemic glucose concentration. Hepatocytes, β-cells and hypothalamic tanycytes are part of the glucosensor cell types, which express several proteins involved in the glucose sensing mechanism such as GLUT2, Glucokinase (GK) and Glucokinase regulatory protein (GKRP). GK catalyzes the phosphorylation of glucose to glucose-6-phosphate (G-6P), and its activity and subcellular localization are regulated by GKRP. In liver, when glucose concentration is low, GKRP binds to GK holding it in the nucleus, while the rise in glucose concentration induces a rapid export of GK from the nucleus to the cytoplasm. In contrast, hypothalamic tanycytes display inverse compartmentalization dynamic in response to glucose: a rise in the glucose concentration drives nuclear compartmentalization of GK. The underlying mechanism responsible for differential GK subcellular localization in tanycytes has not been described yet. However, it has been suggested that relative expression between GK and GKRP might play a role. To study the effects of GKRP expression levels in the subcellular localization of GK, we used insulinoma 832/13 cells and hypothalamic tanycytes to overexpress the tanycytic sequences of . By immunocytochemistry and Western blot analysis, we observed that overexpression of GKRP, independently of the cellular context, turns GK localization to a liver-like fashion, as GK is mainly localized in the nucleus in response to low glucose. Evaluating the expression levels of GKRP in relation to GK through RT-qPCR, suggest that excess of GKRP might influence the pattern of GK subcellular localization. In this sense, we propose that the low expression of GKRP (in relation to GK) observed in tanycytes is responsible, at least in part, for the compartmentalization pattern observed in this cell type. Since GKRP behaves as a GK inhibitor, the regulation of GKRP expression levels or activity in tanycytes could be used as a therapeutic target to regulate the glucosensing activity of these cells and consequently to regulate feeding behavior.

Hypothalamic glucosensing, which involves the detection of glucose concentration changes by brain cells and subsequent release of orexigenic or anorexigenic neuropeptides, is a crucial process that regulates feeding behavior. Arcuate nucleus (AN) neurons are classically thought to be responsible for hypothalamic glucosensing through a direct sensing mechanism; however, recent data has shown a metabolic interaction between tanycytes and AN neurons through lactate that may also be contributing to this process. Monocarboxylate transporter 1 (MCT1) is the main isoform expressed by tanycytes, which could facilitate lactate release to hypothalamic AN neurons. We hypothesize that MCT1 inhibition could alter the metabolic coupling between tanycytes and AN neurons, altering feeding behavior. To test this, we inhibited MCT1 expression using adenovirus-mediated transfection of a shRNA into the third ventricle, transducing ependymal wall cells and tanycytes. Neuropeptide expression and feeding behavior were measured in MCT1-inhibited animals after intracerebroventricular glucose administration following a fasting period. Results showed a loss in glucose regulation of orexigenic neuropeptides and an abnormal expression of anorexigenic neuropeptides in response to fasting. This was accompanied by an increase in food intake and in body weight gain. Taken together, these results indicate that MCT1 expression in tanycytes plays a role in feeding behavior regulation.

In the adult brain, well-characterized neurogenic niches are located in the subventricular zone (SVZ) of the lateral ventricles and in the subgranular zone (SGZ) of the hippocampus. In both regions, neural precursor cells (NPCs) share markers of embryonic radial glia and astroglial cells, and clonal expansion of these cells leads to neurosphere formation. It has also been more recently demonstrated that neurogenesis occurs in the adult hypothalamus, a brain structure that integrates peripheral signals to control energy balance and dietary intake. The NPCs of this region, termed tanycytes, are ependymal-glial cells, which comprise the walls of the infundibular recess of the third ventricle and contact the median eminence. Thus, tanycytes are in a privileged position to detect hormonal, nutritional and mitogenic signals. Recent studies reveal that in response to nutritional signals, tanycytes are capable of differentiating into orexigenic or anorexigenic neurons, suggesting that these cells are crucial for control of feeding behavior. In this review, we discuss evidence, which suggests that hypothalamic neurogenesis may act as an additional adaptive mechanism in order to respond to changes in diet.

This paper focuses on electricity demand forecasting and its uncertainty representation using a hybrid machine learning (ML) model in the eastern control area of southeastern Mexico. In this case, different sources of uncertainty are integrated by applying the Bootstrap method, which adds the characteristics of stochastic noise, resulting in a hybrid probabilistic and ML model in the form of a time series. The proposed methodology addresses a function density probability, which is the generalized of extreme values obtained from the errors of the ML model; however, it is adaptable and independent and simulates the variability that may arise due to unforeseen events. Results indicate that for a five-day forecast using only demand data, the proposed model achieves a Mean Absolute Percentage Error (MAPE) of 4.358%; however, incorporating temperature increases the MAPE to 5.123% due to growing uncertainty. In contrast, a day-ahead forecast, including temperature, improves accuracy, reducing MAPE to 1.644%. The stochastic noise component enhances probabilistic modeling, yielding a MAPE of 3.042% with and 2.073% without temperature in five-day forecasts. Therefore, the proposed model proves useful for regions with high demand variability, such as southeastern Mexico, while maintaining accuracy over longer time horizons.