After intensive research on the gut-brain axis, intestinal dysbiosis is considered to be one of the important pathways of cognitive decline. Microbiota transplantation has long been thought to reverse the behavioral changes in the brain caused by colony dysregulation, but in our study, microbiota transplantation seemed to improve only behavioral brain function, and there was no reasonable explanation for the high level of hippocampal neuron apoptosis that remained. Butyric acid is one of the short-chain fatty acids of intestinal metabolites and is mainly used as an edible flavoring. It is commonly used in butter, cheese and fruit flavorings, and is a natural product of bacterial fermentation of dietary fiber and resistant starch in the colon, acting similarly to the small-molecule HDAC inhibitor TSA. The effect of butyric acid on HDAC levels in hippocampal neurons in the brain remains unclear. Therefore, this study used rats with low bacterial abundance, conditional knockout mice, microbiota transplantation, 16S rDNA amplicon sequencing, and behavioral assays to demonstrate the regulatory mechanism of short-chain fatty acids on the acetylation of hippocampal histones. The results showed that disturbance of short-chain fatty acid metabolism led to high HDAC4 expression in the hippocampus and regulated H4K8ac, H4K12ac, and H4K16ac to promote increased neuronal apoptosis. However, microbiota transplantation did not change the pattern of low butyric acid expression, resulting in maintained high HDAC4 expression in hippocampal neurons with continued neuronal apoptosis. Overall, our study shows that low levels of butyric acid in vivo can promote HDAC4 expression through the gut-brain axis pathway, leading to hippocampal neuronal apoptosis, and demonstrates that butyric acid has great potential value for neuroprotection in the brain. In this regard, we suggest that patients with chronic dysbiosis should pay attention to changes in the levels of SCFAs in their bodies, and if deficiencies occur, they should be promptly supplemented through diet and other means to avoid affecting brain health.

Cervical cancer (CESC) presents significant clinical challenges due to its complex tumor microenvironment (TME) and varied treatment responses. This study identified undifferentiated M0 macrophages as high-risk immune cells critically involved in CESC progression. Co-culture experiments further demonstrated that M0 macrophages significantly promoted HeLa cell proliferation, migration, and invasion, underscoring their pivotal role in modulating tumor cell behavior within the TME. A nine-gene prognostic model constructed from immune gene signatures highlighted CXCL8 as a key regulator of M0 macrophage behavior. Functional experiments demonstrated that CXCL8 knockdown in M0 macrophages inhibited their proliferation, shifted polarization toward an M1-dominant phenotype, and reduced tumor-promoting M2 polarization. Co-culture experiments with CXCL8-deficient M0 macrophages further revealed a suppression of HeLa cell proliferation, migration, and invasion. These findings position M0 macrophages as central regulators within the TME and suggest that targeting pathways like CXCL8 could provide novel therapeutic strategies for improving outcomes in CESC patients.

Rationale: Primordial follicles are limited in number and cannot be regenerated, dormant primordial follicles cannot be reversed once they enter a growth state. Therefore, the length of the female reproductive lifespan depends on the orderly progression and selective activation of primordial follicles, the mechanism of which remains unclear.

Background: Congenital unilateral renal agenesis (URA) is a kind of rare birth defect during fetal development with varies clinical phenotypes. The pathogenesis and the relationship between gene and phenotype are still unclear. Methods: Ten URA fetuses were followed up after birth using postnatal renal ultrasound examination to confirm the diagnosis with nine children were URA and one was Renal Ectopy (RE). Trio- WES, CNV- seq were performed with the 10 children and their close relatives. Results: There were 3 heterozygous variants of CHD7, PROKR2 and NRIP1 genes were identified in 3 children, respectively. CHD7 (c.2663T>C, p.M888T) is classified as likely pathogenic (LP), PROKR2 (c.685G>C, p.G229R) and NRIP1 (c.2705T>G, p.F902C) are classified as variants of uncertain significance (VUS). CHD7 (c.2663T>C, p.M888T) and PROKR2 (c.685G>C, p.G229R) as URA-related genes may be associated with idiopathic hypogonadotropic hypogonadism (IHH) or CHARGE syndrome (CS), and 3D-protein structure prediction revealed that the two variants may affect the stability in the CHD7 protein or PROKR2 protein, separately. The RE-related gene NRIP1 (c.2705T>G, p.F902C) may be causative of congenital anomalies of the kidneys and urinary tract (CAKUT). Conclusions: Identification of these variants can in exploring the etiology of URA or RE and improve the level of genetic counseling. Impacts: We performed trio-whole-exome sequencing (trio- WES) and copy number variation sequencing (CNV- seq) in 10 children, including 9 children with Unilateral Renal Agenesis and 1 with Renal Ectopy after birth. The possible pathogenic genes of URA can be screened using prenatal and postnatal diagnosis of URA fetuses and gene detection after birth. Future studies evaluating this association may lead to a better understanding of URA and elucidate exploring the etiology of URA or RE and improve the level of genetic counseling.

Proline is an important amino acid that widely affects life activities. It plays an important role in the occurrence and development of diseases. It is of great significance to monitor the metabolism of the machine. With the great advantages of deep learning in feature extraction, near‐infrared analysis technology has great potential and has been widely used in various fields. This study explored the potential application of near‐infrared spectroscopy in the detection of serum proline. We collected blood samples from clinical sources, separated the serum, established a quantitative model, and determined the changes in proline. Four algorithms of SMLR, PLS, iPLS, and SA were used to model proline in serum. The root mean square errors of prediction were 0.00111, 0.00150, 0.000770, and 0.000449, and the correlation coefficients (Rp) were 0.84, 0.67, 0.91, and 0.97, respectively. The experimental results show that the model is relatively robust and has certain guiding significance for the clinical monitoring of proline. This method is expected to replace the current mainstream but time‐consuming HPLC, or it can be applied to rapid online monitoring at the bedside.