Perioperative anaphylaxis is a rare, life-threatening, iatrogenic condition that predominantly arises following anesthesia. The unique context of this condition, characterized by the concurrent administration of multiple drugs, patient draping, and altered physiological states, presents significant diagnostic and therapeutic challenges, contributing to a higher mortality rate compared to anaphylaxis in other settings. This narrative review synthesizes the evidence to delineate the evolving etiology, pathophysiology, atypical clinical presentation, evidence-based immediate management, and strategic prevention of perioperative anaphylactic reactions. The primary causative agents include neuromuscular blocking agents, antibiotics, and latex, with emerging culprits such as chlorhexidine, dyes, and novel agents like remimazolam. Diagnosis is complicated by the paucity of cutaneous signs; thus, cardiovascular collapse combined with a low end-tidal carbon dioxide level has emerged as a useful supportive diagnostic indicator that requires integration with the clinical context. Immediate management prioritizes the prompt administration of epinephrine and aggressive fluid resuscitation. Subsequent allergological investigations, primarily via skin testing and serum tryptase/histamine measurement, are paramount for identifying the causative agent and preventing its recurrence. Prevention strategies emphasize meticulous history-taking, risk stratification, and the creation of latex-free environments. Future directions must focus on establishing global surveillance networks, exploring novel biomarkers and risk factors such as the circulating microbiome—a preliminary but promising area of research—and enhancing team preparedness through simulation training to improve patient safety outcomes.

Chronic stress-induced hypercortisolism causes diminished ovarian reserve (DOR), contributing to infertility and miscarriage. Androgen supplementation is an emerging therapeutic approach for DOR. The traditional Chinese herbal decoction modified Gengnianchun formula (MGNC) has shown clinical efficacy in treating DOR. This study aimed to compare the effectiveness of MGNC with that of androgens in a stress-induced DOR mouse model.

Sexually mature female C57 mice aged six weeks were randomly assigned to six groups (n = 10 per group, with 3 independent replicates per group), including the control, model, low-dose testosterone (LT), medium-dose testosterone (MT), high-dose testosterone (HT), and MGNC groups. This sample size and study design were determined based on preliminary experimental data. Chronic stress was induced in mice, except for the control group, by daily glucocorticoid injection, and the mice in the LT, MT, HT, and MGNC groups were treated at the same time with testosterone (low, medium, or high dose) or MGNC for six weeks. Body weight, estrous cycles, ovarian follicle counts, hormone profiles, including follicle-stimulating hormone (FSH), luteinizing hormone (LH), estradiol, and testosterone, and in vitro preantral follicle growth rates (via MGNC-enriched or androgen-treated serum) were assessed.

All groups presented stable body weights. MGNC ameliorated estrous cycle irregularities caused by stress, while testosterone exacerbated the abnormality. Moreover, MGNC outperformed LT in improving primordial/primary/antral follicle counts and corpus luteum formation, while MT and HT did not improve ovarian follicle reserve. LT was associated with the highest serum estradiol level, but none of the testosterone doses reduced FSH levels or the FSH/LH ratio, whereas MGNC lowered FSH and the FSH/LH ratio. Additionally, MGNC-enriched serum significantly enhanced the in vitro follicular growth rate in corticosterone-supplemented culture medium, and this effect was superior to that observed with testosterone-pretreated serum.

MGNC demonstrates superior efficacy over androgen therapy in treating chronic stress-induced DOR in mice, supporting further investigations into its clinical potential and mechanisms.

The history of screening for cervical cancer is rich with implementing cutting-edge ideas and technologies. From the very first “Pap smear” to the semi-automated and computerized systems of today, the way we screen for cervical cancer has changed dramatically in the past 75 years. With the advent of new techniques and more advanced machine learning algorithms, we sought to understand the current and future applications of artificial intelligence in clinical pathology around cervical cancer screening, diagnosis, and treatment.

A structured narrative review was conducted to examine the historical evolution and contemporary advances in cervical cancer screening, diagnosis, excisional management, and artificial intelligence applications. Peer-reviewed articles, consensus guidelines, and global policy documents published between January 1990 and March 2025 were identified through targeted searches of PubMed and review of reference lists from relevant publications. Search terms included combinations of “cervical cancer screening,” “Papanicolaou test,” “liquid-based cytology,” “HPV testing,” “colposcopy,” “loop electrosurgical excision procedure,” “digital pathology,” “deep learning,” and “artificial intelligence.” Emphasis was placed on multi-center validation studies, systematic reviews, regulatory and implementation guidance, and global health frameworks. Publications lacking methodological transparency or direct relevance to clinical or translational practice were excluded.

Through a review of the literature, we describe how innovations in conventional and liquid-based cytology, human papillomavirus testing, and organized screening programs established the current prevention framework. Building on this foundation, recent studies demonstrate promising performance of deep learning algorithms applied to conventionally prepared cervical cytology slides, with systems capable of binary normal versus abnormal classification as well as more granular diagnostic categorization. Artificial intelligence-assisted colposcopy and computer-vision approaches have also shown improved diagnostic concordance, workflow efficiency, and potential to expand screening capacity in resource-limited environments.

There has been much work done in the past several years surrounding the implementation of deep learning algorithms in regard to cervical cancer screening. The work in this field shows promise in enhancing diagnostic accuracy, streamlining diagnostic workflow, and decreasing turnaround times from specimen collection to rendering a diagnosis. However, there are still many technical, legal, and ethical questions that must be answered prior to widespread adoption of these algorithms for patient care.

The huge burden of type 1 diabetes mellitus (T1DM) has been a source of concern globally since the Industrial Revolution in the 18th–19th centuries. To this end, studies have shown that certain environmental changes that accompanied the Revolution may have increased the risk and burden of the disease in genetically predisposed individuals. However, documented studies that synthesize these environmental triggers are scarce. As a result, the current study was conceived to synthesize the environmental triggers of T1DM to boost public awareness. Relevant information was retrieved from reputable academic databases; namely, Scopus, PubMed, SpringerLink, and Embase. The results showed that chemical exposure, viral infection, gut microbiome disruption, vitamin and mineral deficiencies, inadequate or exclusive breastfeeding, as well as early exposure to infant feeding formulas could increase the risk and burden of T1DM in genetically predisposed individuals. As a consequence, these triggers could compromise the expression of certain genes involved in insulin synthesis and immune function, such as the human leukocyte antigen (HLA), insulin (INS), cytotoxic T lymphocyte-associated antigen 4 (CTLA-4), and protein tyrosine phosphatase non-receptor type 22 (PTPN22) genes. This would result in a dysfunctional immune system in which immune cells, such as T-cells and B-cells and molecules, such as cytokines would attack self-tissues, thus causing autoimmunity of the pancreatic beta cells. Environmental triggers could also induce the T1DM pathophysiology by modifying the epigenome of the mentioned genes. Furthermore, some epigenetic changes could be reversed, which would infer that treatment procedures that would include the pathophysiology of the environmental triggers could be more effective.

Emerging evidence suggests that RNA-binding motif (RBM) proteins are involved in hepatocarcinogenesis and act either as oncogenes or tumor suppressors. The objective of this study was to investigate the role of RBM34, an RBM protein, in hepatocellular carcinoma (HCC).

We first examined the expression of RBM34 across cancers. The correlation of RBM34 with clinicopathological features and the prognostic value of RBM34 for HCC was then investigated. Functional enrichment analysis of RBM34-related differentially expressed genes (DEGs) was performed to explore its biological function. RNA sequencing (RNA-seq) was applied to identify downstream genes and pathways affected upon RBM34 knockout. The correlation of RBM34 with immune characteristics was also analyzed. The oncogenic function of RBM34 was examined in in vitro and in vivo experiments.

RBM34 was highly expressed in hepatocellular carcinoma and correlated with poor clinicopathological features and prognosis. RBM34 was positively associated with tumor immune cell infiltration, biomarkers of immune cells, and immune checkpoint expression. A positive correlation was also observed between RBM34, T cell exhaustion, and regulatory T cell marker genes. Knockout of RBM34 significantly inhibited cell proliferation, migration, and xenograft tumor growth, and sensitized HCC cells to sorafenib treatment. RBM34 inhibition reduced FGFR2 expression and affected PI3K-AKT pathway activation in HCC cells.

Our study suggests that RBM34 may serve as a new prognostic marker and therapeutic target of HCC.

Naringenin is an anti-inflammatory flavonoid that has been studied in chronic liver disease. The mechanism specific to its antifibrosis activity needs further investigation This study was to focused on the cyclic guanosine monophosphate-adenosine monophosphate synthase (cGAS) pathway in hepatic stellate cells and clarified the antifibrosis mechanism of naringenin.

The relationship between the cGAS-stimulator of interferon genes (STING) pathway and liver fibrosis was analyzed using the Gene Expression Omnibus database. Histopathology, immunohistochemistry, fluorescence staining, Western blotting and polymerase chain reaction were performed to assess gene and protein expression levels associated with the cGAS pathway in clinical liver tissue samples and mouse livers. Molecular docking was performed to evaluate the relationship between naringenin and cGAS, and western blotting was performed to study the expression of inflammatory factors downstream of cGAS in vitro.

Clinical database analyses showed that the cGAS-STING pathway is involved in the occurrence of chronic liver disease. Naringenin ameliorated liver injury and liver fibrosis, decreased collagen deposition and cGAS expression, and inhibited inflammation in carbon tetrachloride (CCl4)-treated mice. Molecular docking found that cGAS may be a direct target of naringenin. Consistent with the in vivo results, we verified the inhibitory effect of naringenin on activated hepatic stellate cells (HSCs). By using the cGAS-specific agonist double-stranded (ds)DNA, we showed that naringenin attenuated the activation of cGAS and its inflammatory factors affected by dsDNA. We verified that naringenin inhibited the cGAS-STING pathway, thereby reducing the secretion of inflammatory factors by HSCs to ameliorate liver fibrosis.

Interrupting the cGAS-STING pathway helped reverse the fibrosis process. Naringenin has potential as an antihepatic fibrosis drug.