2024年 · 第07卷第04期

Observational studies have reported conflicting results on the association between leukocyte telomere length (LTL) and polycystic ovary syndrome (PCOS).This study evaluated the causal effect of LTL on PCOS in European and East Asian populations using Mendelian randomization (MR).

Genetic variants for LTL were selected as instrumental variables from the largest genome-wide association study (GWAS) from the UK Biobank (472,174 participants) and a Singapore Chinese GWAS (23,096 participants). Two-sample MR analysis was performed in a PCOS cohort of European ancestry (4,138 cases and 20,129 controls),with replication analyses in a Northern European PCOS cohort (3,609 cases and 229,788 controls) and in a Chinese PCOS cohort (4,386 cases and 8,017 controls). A reverse MR study was conducted to test the effect of PCOS on LTL.

In the PCOS cohort of European ancestry, genetically determined longer LTL was associated with lower risk of PCOS according to the inverse-variance weighted method [odds ratio (OR), 0.709; 95% confidence interval (CI), 0.506 to 0.992; P = 0.045]. The results remained directionally consistent in sensitivity analyses using different MR methods. In the Northern European replication cohort, a significant association was also found between genetically determined longer LTL and lower risk of PCOS via the MR-Egger method (OR, 0.580; 95% CI, 0.351 to 0.961; P = 0.038). Similar associations were observed in the East Asian replication cohort but did not reach nominal significance. In the reverse MR analysis, no evidence of associations was observed between genetically determined PCOS and LTL in either Europeans or East Asians.

This study revealed evidence supporting a causal role of LTL in the etiology of PCOS.

Artificial intelligence (AI) and machine learning (ML) are revolutionizing the pharmaceutical industry, particularly in drug development and delivery. These technologies enable precision medicine by analyzing extensive datasets to optimize formulations and predict patient responses. AI-driven models enhance nanoparticle-based drug carriers, improving their stability, bioavailability, and targeting accuracy. ML also facilitates real-time monitoring and adaptive control of drug release, ensuring better therapeutic outcomes. This review explores the integration of AI and ML in drug delivery, highlighting their potential to accelerate development, reduce costs, and advance personalized medicine.

Immunotherapy has emerged as a promising approach for treating head and neck neoplasms, with the potential to improve patient outcomes and revolutionize cancer treatment. This review discusses the current evidence supporting the use of immunotherapy for head and neck cancer and outlines future research directions. Immunotherapy uses mostly immune checkpoint inhibitors, such as those that work on the PD-1/PD-L1 axis and CTLA-4, to improve the ability of the immune system to fight cancer cells. PD-1/PD-L1 inhibitors, such as pembrolizumab and nivolumab, have been shown to work in clinical trials, which is why they have been approved for some people with head and neck cancer. These treatments reactivate the immune response against tumors, resulting in tumor reduction and improved survival rates. CTLA-4 inhibition has shown promise in enhancing the immune system’s ability to combat head and neck cancer cells, although its efficacy has been more pronounced in melanoma treatment. Ongoing research focuses on improving immunotherapy efficacy, identifying biomarkers to predict patient responses, and developing personalized treatment strategies. Clinical trials have documented marked increases in survival rates and decreases in tumor size, highlighting the effectiveness of targeted approaches. As scientific advancements progress, personalized immunotherapy strategies may soon become accessible, enabling the customization of treatment plans for individual patients with head and neck cancer.

Diabetes mellitus remains a global health care challenge, promoting the search for innovative treatments. In vivo and in vitro studies have shown the potential benefits of ultrasound (US) on enhancing insulin release and reactivating pancreatic β cell function. As a novel, nonpharmacological, noninvasive, and cost-effective approach, US therapy holds promise for stimulating pancreatic function and improving insulin secretion. This review explores recent findings on US therapy, focusing on low-intensity US (LIUS) and its effects on varying treatment parameters. Despite promising results, conflicting evidence highlights the need for further investigation through large-scale clinical trials to establish the therapeutic potential of US therapy and to optimize treatment regimens for effective diabetes management. The biological response to LIUS is complex and involves multiple cell types and pathways. The mechanisms triggering these effects require further exploration. A future engineering challenge lies in designing an experimental setup to control the US-induced mechanical phenomena, enabling the evaluation of biological effects with respect to parameters such as intensity, frequency, or duty cycle.

Cefuroxime axetil, a second-generation cephalosporin antibiotic, has long been utilized to treat various bacterial infections. However, recent advances in nanotechnology have provided new directions for enhancing its effectiveness through the development of innovative nanoformulations. Cefuroxime axetil, which is classified as a β-lactam agent, has a broad spectrum of activity against both gram-positive and gram-negative microorganisms. This drug exists in polymorphous crystalline and amorphous forms, the latter of which exhibits superior bioavailability. This review explores the pharmacokinetic and various pharmacodynamic properties and mechanisms of action of cefuroxime axetil. Moreover, the challenges posed by the drug’s poor aqueous solubility and bioavailability, and the potential of nanoformulations to address these limitations and enhance the therapeutic efficacy of this agent, are discussed.

Recent advancements in artificial intelligence (AI) have significantly impacted the diagnosis and treatment of kidney diseases, offering novel approaches for precise quantitative assessments of nephropathology. The collaboration between computer engineers, renal specialists, and nephropathologists has led to the development of AI- assisted technology, presenting promising avenues for renal pathology diagnoses, disease prediction, treatment effectiveness assessment, and outcome prediction. This review provides a comprehensive overview of AI applications in renal pathology, focusing on computer vision algorithms for kidney structure segmentation, specific pathological changes, diagnosis, treatment, and prognosis prediction based on images along with the role of machine learning (ML) and deep learning (DL) in addressing global public health issues related to various nephrological conditions. Despite the transformative potential, the review acknowledges challenges such as data privacy, interpretability of AI models, the imperative need for trust in AI-driven recommendations for broad applicability, external validation, and improved clinical decision-making. Overall, the ongoing integration of AI technologies in nephrology paves the newer way for more precise diagnostics, personalized treatments, and improved patient care outcome.

Receptor for advanced glycosylation end products (RAGE) is an essential cell surface receptor that detects advanced glycation end products (AGEs) to mediate important inflammatory and immune processes. Inflammation can cause insulin resistance, in which the excess glucose in the blood that cannot be stored as fat induces hyperglycemia. Under these conditions, AGEs, high mobility group box 1, S100s, and other inflammatory factors induce the production of tumor necrosis factor-α, interleukin-1β, interleukin (interleukin-6), and other proinflammatory cytokines, inducing chronic inflammation. Herein, we reviewed the relationships between diabetes and RAGE-related inflammation and the associated signaling pathways. Recent progress in targeted therapy against RAGE has also been discussed. Since RAGE is involved in the progression of diabetes, it might be a promising therapeutic target for the prevention and management of this disorder and related complications.

Researchers and practitioners are increasingly interested in the application of artificial intelligence (AI) to drive advancements in the pharmaceutical sector and elevate it to the required level. The pharmaceutical sector is significantly impacted by drug research and discovery, which also has an impact on several human health problems. AI has been a key instrument in the analysis of a large volume of high-dimensional data in recent years because of progress in experimental techniques and computer hardware. Due to the exponential increase in the volume of biomedical data, it is beneficial to integrate AI in all phases of pharmacological research and development. AI’s capacity to find novel treatments more quickly and cheaply has enabled big data in biomedicine to drive a revolution in drug research and development. The use of AI in the pharmaceutical sector has developed over the past several years and is predicted to become more widespread. AI can improve drug development processes and formulations while saving time and money. This study aims to help determine the extent to which using AI in pharmaceuticals enhances health care results and patient-specific treatment. In addition to this in-depth examination, this study highlights the potential of AI, related issues, and its future application in the pharmaceutical industry.

The eye is one of the most delicate organs in the body, and glaucoma is considered to be a major cause of blindness. The unique and distinct architecture and physiology of the human eye continue to pose a major challenge for pharmacologists and researchers seeking to provide effective medication delivery. Despite the number of established invasive and noninvasive eye treatments, such as implants, eye drops, and injections, these still lead to several serious side effects that can result from either low bioavailability or adverse ocular effects. Novel eye disease treatments can be developed with the help of nanoscience and nanotechnology. Many active compounds have been engineered to react with nanocarriers to engage with ocular tissues precisely and overcome ocular difficulties. Future research into novel drug delivery systems and targeted treatments is expected to increase because the approach of reducing intraocular pressure (IOP) cannot contain the progress of glaucoma in the general population of patients. This review focuses on the potential benefits of green chemistry and nanotechnology in ophthalmology, particularly in the treatment and diagnosis of glaucoma. Green synthesis has attracted significant interest as a dependable, environmentally friendly, and sustainable method for producing a range of nanomaterials, such as metal/oxide nanoparticles, hybrid materials, and bioinspired materials.