Hematopoietic stem cells (HSC) lie at the center of the hematopoiesis process, as they bear capacity to self-renew and generate all hematopoietic lineages, hence, all mature blood cells. The ability of HSCs to recognize systemic infection or inflammation or other forms of peripheral stress, such as blood loss, is essential for demand-adapted hematopoiesis. Also of critical importance for HSC function, specific metabolic cues (e.g., associated with changes in energy or O2 levels) can regulate HSC function and fate decisions. In this regard, the metabolic adaptation of HSCs facilitates their switching between different states, namely quiescence, self-renewal, proliferation and differentiation. Specific metabolic alterations in hematopoietic stem and progenitor cells (HSPCs) have been linked with the induction of trained myelopoiesis in the bone marrow as well as with HSPC dysfunction in aging and clonal hematopoiesis of indeterminate potential (CHIP). Thus, HSPC function is regulated by both immunologic/inflammatory and metabolic cues. The immunometabolic control of HSPCs and of hematopoiesis is discussed in this review along with the translational implications thereof, that is, how metabolic pathways can be therapeutically manipulated to prevent or reverse HSPC dysfunction or to enhance or attenuate trained myelopoiesis according to the needs of the host.Hyperkalemia is an electrolyte abnormality with potentially life-threatening consequences. Despite various guidelines, no universally accepted consensus exists on best practices for hyperkalemia monitoring, with variations in precise potassium (K+) concentration thresholds or for the management of acute or chronic hyperkalemia. Based on the available evidence, this review identifies several critical issues and unmet needs with regard to the management of hyperkalemia. Real-world studies are needed for a better understanding of the prevalence of hyperkalemia outside the clinical trial setting. There is a need to improve effective management of hyperkalemia, including classification and K+ monitoring, when to reinitiate previously discontinued renin-angiotensin-aldosterone system inhibitor (RAASi) therapy, and when to use oral K+-binding agents. Monitoring serum K+ should be individualized; however, increased frequency of monitoring should be considered for patients with chronic kidney disease, diabetes, heart failure, or a history of hyperkalemia and for those receiving RAASi therapy. Recent clinical studies suggest that the newer K+ binders (patiromer sorbitex calcium and sodium zirconium cyclosilicate) may facilitate optimization of RAASi therapy. Enhancing the knowledge of primary care physicians and internists with respect to the safety profiles of these newer K+ binders may increase confidence in managing patients with hyperkalemia. Lastly, the availability of newer K+-binding agents requires further study to establish whether stringent dietary K+ restrictions are needed in patients receiving K+-binder therapy. Individualized monitoring of serum K+ among patients with an increased risk of hyperkalemia and the use of newer K+-binding agents may allow for optimization of RAASi therapy and more effective management of hyperkalemia.
  Hepatocellular carcinoma has a high recurrence rate even after curative surgery, and hepatocellular carcinoma risk-predictive biomarkers will enable identification of patients who most need close monitoring and cancer-preventive intervention. Hepatocellular carcinoma has 2 different recurrence patterns-a multicentric recurrence and an intrahepatic metastasis. We have reported that the molecular gene signature from the gene expression of adjacent liver can be used to predict multicentric recurrence of hepatocellular carcinoma, but the signature to predict recurrence from intrahepatic metastasis has not been established. We aimed to identify the recurrence from intrahepatic metastasis gene signature from the gene expression of tumor to predict recurrence from intrahepatic metastasis.
 
  The intrahepatic metastasis-risk signature was created based on the exhaustive analysis using a microarray transcriptome database of hepatocellular carcinoma. The intrahepatic metastasis-risk signature was measured in a cohort n was independently associated with higher early hepatocellular carcinoma recurrence (hazard ratio = 3.7, P = .03) in multivariable modeling adjusted by tumor size, tumor number, and microvascular invasion. Gene set enrichment analysis demonstrates that the gene sets associated with "cell cycle" or "histone modulation" are highly enriched in the high intrahepatic metastasis gene signature group CONCLUSION The intrahepatic metastasis gene signature predicts early recurrence and is associated with malignant potential related to the promoted cell cycle.Automated performance metrics objectively measure surgeon performance during a robot-assisted radical prostatectomy. Machine learning has demonstrated that automated performance metrics, especially during the vesico-urethral anastomosis of the robot-assisted radical prostatectomy, are predictive of long-term outcomes such as continence recovery time.  https://www.selleckchem.com/products/cp2-so4.html  This study focuses on automated performance metrics during the vesico-urethral anastomosis, specifically on stitch versus sub-stitch levels, to distinguish surgeon experience. During the vesico-urethral anastomosis, automated performance metrics, recorded by a systems data recorder (Intuitive Surgical, Sunnyvale, CA, USA), were reported for each overall stitch (Ctotal) and its individual components needle handling/targeting (C1), needle driving (C2), and suture cinching (C3) (Fig 1, A). These metrics were organized into three datasets (GlobalSet [whole stitch], RowSet [independent sub-stitches], and ColumnSet [associated sub-stitches] (Fig 1, B) and applied to three machine learning models (AdaBoost, gradient boosting, and random forest) to solve two classifications tasks experts (≥100 cases) versus novices ( less then 100 cases) and ordinary experts (≥100 and less then 2,000 cases) versus super experts (≥2,000 cases). Classification accuracy was determined using analysis of variance. Input features were evaluated through a Jaccard index. From 68 vesico-urethral anastomoses, we analyzed 1,570 stitches broken down into 4,708 sub-stitches. For both classification tasks, ColumnSet best distinguished experts (n = 8) versus novices (n = 9) and ordinary experts (n = 5) versus super experts (n = 3) at an accuracy of 0.774 and 0.844, respectively. Feature ranking highlighted Endowrist articulation and needle handling/targeting as most important in classification. Surgeon performance measured by automated performance metrics on a granular sub-stitch level more accurately distinguishes expertise when compared with summary automated performance metrics over whole stitches.