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Spermatogenesis holds considerable promise for human-assisted reproduction and livestock breeding based on stem cells. It occurs in seminiferous tubules within the testis, which mainly comprise male germ cells and Sertoli cells. While the developmental progression of male germ cells and Sertoli cells has been widely reported in mice, much less is known in other large animal species, including dairy goats. In this study, we present the data of single cell RNA sequencing (scRNA-seq) for 25,373 cells from 45 (pre-puberty), 90 (puberty), and 180-day-old (post-puberty) dairy goat testes. We aimed to identify genes that are associated with key developmental events in male germ cells and Sertoli cells. We examined the development of spermatogenic cells and seminiferous tubules from 15, 30, 45, 60, 75, 90, 180, and 240-day-old buck goat testes. scRNA-seq clustering analysis of testicular cells from pre-puberty, puberty, and post-puberty goat testes revealed several cell types, including cell populations with characteristics of spermatogonia, early spermatocytes, spermatocytes, spermatids, Sertoli cells, Leydig cells, macrophages, and endothelial cells. We mapped the timeline for male germ cells development from spermatogonia to spermatids and identified gene signatures that define spermatogenic cell populations, such as AMH, SOHLH1, INHA, and ACTA2. Importantly, using immunofluorescence staining for different marker proteins (UCHL1, C-KIT, VASA, SOX9, AMH, and PCNA), we explored the proliferative activity and development of male germ cells and Sertoli cells. Moreover, we identified the expression patterns of potential key genes associated with the niche-related key pathways in male germ cells of dairy goats, including testosterone, retinoic acid, PDGF, FGF, and WNT pathways. In summary, our study systematically investigated the elaborate male germ cells and Sertoli cells developmental patterns in dairy goats that have so far remained largely unknown. This information represents a valuable resource for the establishment of goat male reproductive stem cells lines, induction of germ cell differentiation in vitro, and the exploration of sequential cell fate transition for spermatogenesis and testicular development at single-cell resolution.

BackgroundProlonged mechanical ventilation (PMV) has been proven as a risk factor for poor prognosis in patients with neurocritical illness. Spontaneous basal ganglia intracerebral hemorrhage (ICH) is one common subtype of hemorrhagic stroke and is associated with high morbidity and mortality. The systemic immune-inflammation index (SII) is used as a novel and valuable prognostic marker for various neoplastic diseases and other critical illnesses.ObjectiveThis study aimed to analyze the predictive value of preoperative SII for PMV in patients with spontaneous basal ganglia ICH who underwent surgical operations.MethodsThis retrospective study was conducted in patients with spontaneous basal ganglia ICH who underwent surgical operations between October 2014 and June 2021. SII was calculated using the following formula: SII = platelet count × neutrophil count/lymphocyte count. Multivariate logistic regression analysis and receiver operating characteristics curve (ROC) were used to evaluate the potential risk factors of PMV after spontaneous basal ganglia ICH.ResultsA total of 271 patients were enrolled. Of these, 112 patients (47.6%) presented with PMV. Multivariate logistic regression analysis showed that preoperative GCS (OR, 0.780; 95% CI, 0.688–0.883; P < 0.001), hematoma size (OR, 1.031; 95% CI, 1.016–1.047; P < 0.001), lactic acid (OR, 1.431; 95% CI, 1.015–2.017; P = 0.041) and SII (OR, 1.283; 95% CI, 1.049–1.568; P = 0.015) were significant risk factors for PMV. The area under the ROC curve (AUC) of SII was 0.662 (95% CI, 0.595–0.729, P < 0.001), with a cutoff value was 2,454.51.ConclusionPreoperative SII may predict PMV in patients with spontaneous basal ganglia ICH undergoing a surgical operation.

A wide range of phenolic compounds participate in oilseed growth, regulate oxidative stability of corresponding vegetable oil, and serve as important minor food components with health-promoting effects. Composition distribution of phenolic compounds varied in oilseeds. Isoflavones, sinapic acid derivatives, catechin and epicatechin, phenolic alcohols, chlorogenic acid, and lignans were the main phenolic compounds in soybean, rapeseed, peanut skin, olive, sunflower seed, sesame and flaxseed, respectively. Among which, the total isoflavones content in soybean seeds reached from 1,431 to 2,130 mg/100 g; the main phenolic compound in rapeseed was sinapine, representing 70–90%; chlorogenic acid as the predominant phenolic compound in sunflower kernels, represented around 77% of the total phenolic content. With the rapid development of analytical techniques, it is becoming possible for the comprehensive profiling of these phenolic compounds from oilseeds. This review aims to provide recently developments about the composition distribution of phenolic compounds in common oilseeds, advanced technologies for profiling of phenolic compounds by the metabolomics approaches based on mass spectrometry. As there is still limited research focused on the comprehensive extraction and determination of phenolics with different bound-forms, future efforts should take into account the non-targeted, pseudo-targeted, and spatial metabolomic profiling of phenolic compounds, and the construction of phenolic compound database for identifying and quantifying new types of phenolic compounds in oilseeds and their derived products.