The Protective Effects of Metformin and Vitamin C and Their Co-Administration in Bleomycin-Induced Pulmonary Fibrosis in Mice

Bleomycin, an antibacterial antibiotic, is used in chemotherapy and is effective against various forms of human carcinomas. However, its use is limited due to its tendency to cause pulmonary fibrosis. Oxidative stress and excessive expression of TGF beta occur in pulmonary fibrosis, leading to cellular death, inflammation, and additional damage to lung tissue. Metformin has the ability to reduce oxidative stress and lower the level of TGF beta by activating AMPK. Additionally, ascorbic acid possesses potent antioxidant characteristics. Consequently, we decided to investigate the effects of these two medications on pulmonary fibrosis and compare with methyl prednisolone. Thirty-six adult mice were categorized into 6 distinct groups: Control, bleomycin (bleo), bleo + methyl prednisolone, bleo + metformin, bleo + ascorbic acid, bleo + metformin + ascorbic acid. Pulmonary fibrosis was induced by the administration of bleomycin in all groups, except for the control group. Subsequently, medications were administered for a duration of 14 days. Ultimately, the mice were sacrificed and lung tissues were obtained for biochemical and histological examination. As shown by biochemical and histological analysis, all treatment groups showed a decrease in oxidative stress factors, inflammation, and lung tissue fibrosis; however, the effects of administering metformin and ascorbic acid together were noticeable. Our study found that administering metformin and ascorbic acid over a period of 14 days, either alone or in combination, may contribute to the repair of pulmonary fibrosis. However, our data indicate that the combined therapy of these drugs provided a better result.

Therapeutic errors involving diabetes medications reported to United States poison centers

BACKGROUND

To investigate the characteristics and trends of therapeutic errors that occur outside of healthcare facilities involving diabetes medications reported to US poison centers.

METHODS

National Poison Data System data from 2000 to 2021 were retrospectively analyzed.

RESULTS

There were 157,623 exposure cases of non-healthcare facility-related therapeutic errors associated with diabetes medications as the primary substance reported to US poison centers from 2000 to 2021. The rate of all therapeutic errors involving diabetes medications increased by 279.8% from 2000 to 2011, followed by a slower 15.0% increase to 2021. Half (50.1%) of the exposure cases were treated/evaluated and released and 44.1% did not receive treatment in a healthcare facility; however, 9.9% experienced a serious medical outcome, including 17 fatalities, and 1.0% were admitted to a critical care unit and 2.2% to a non-critical care unit. Insulin had the highest rates of therapeutic errors and serious medical outcomes, while sulfonylureas and insulin had the highest medical hospital admission rates. Metformin accounted for 59% (n = 10) of fatalities.

CONCLUSIONS

Although most cases of therapeutic errors involving diabetes medications had no or minimal clinical consequences, an important minority were associated with a serious medical outcome or medical hospital admission. Increased efforts to prevent therapeutic errors involving diabetes medications are warranted.

Uncovering the Hidden Burden of Pharmaceutical Poisoning in High-Income and Low-Middle-Income Countries: A Scoping Review

Pharmaceutical poisoning is a significant global public health concern, causing approximately 190,000 deaths annually. This scoping review aims to comprehensively map the available literature on pharmaceutical poisoning and compare patterns between high-income countries (HICs) and low-middle-income countries (LMICs). A systematic search was performed across the following databases: Embase, PubMed, Cochrane Database of Systematic Reviews, Cochrane Central Register of Controlled Trials, and CINAHL. Studies included were from 1 January 2011 to 31 December 2020, in English, with full text available. Seventy-nine articles were included in the study; 21 were from LMICs and 58 were from HICs. Toxic exposure was largely intentional (77%) in LMICs and accidental (68%) in HICs. Drugs acting on the nervous system were responsible for 95% of toxicities worldwide with analgesics accounting for the largest subtherapeutic group in both LMICs (40%) and HICs (58%). Notable statistics were that HICs accounted for 99% of opioid overdoses, and LMICs accounted for 19% of anti-epileptic-induced toxicities. Overall, the medical outcomes due to poisonings were generally worse in LMICs. The review provides possible interventions to target specific geographic locations, based on the trends identified, to reduce the burden worldwide. Many gaps within the literature were recognised, calling for more robust analytical research.

Comparison of decision tree with common machine learning models for prediction of biguanide and sulfonylurea poisoning in the United States: an analysis of the National Poison Data System

BACKGROUND

Biguanides and sulfonylurea are two classes of anti-diabetic medications that have commonly been prescribed all around the world. Diagnosis of biguanide and sulfonylurea exposures is based on history taking and physical examination; thus, physicians might misdiagnose these two different clinical settings. We aimed to conduct a study to develop a model based on decision tree analysis to help physicians better diagnose these poisoning cases.

METHODS

The National Poison Data System was used for this six-year retrospective cohort study.The decision tree model, common machine learning models multi layers perceptron, stochastic gradient descent (SGD), Adaboosting classiefier, linear support vector machine and ensembling methods including bagging, voting and stacking methods were used. The confusion matrix, precision, recall, specificity, f1-score, and accuracy were reported to evaluate the model's performance.

RESULTS

Of 6183 participants, 3336 patients (54.0%) were identified as biguanides exposures, and the remaining were those with sulfonylureas exposures. The decision tree model showed that the most important clinical findings defining biguanide and sulfonylurea exposures were hypoglycemia, abdominal pain, acidosis, diaphoresis, tremor, vomiting, diarrhea, age, and reasons for exposure. The specificity, precision, recall, f1-score, and accuracy of all models were greater than 86%, 89%, 88%, and 88%, respectively. The lowest values belong to SGD model. The decision tree model has a sensitivity (recall) of 93.3%, specificity of 92.8%, precision of 93.4%, f1_score of 93.3%, and accuracy of 93.3%.

CONCLUSION

Our results indicated that machine learning methods including decision tree and ensembling methods provide a precise prediction model to diagnose biguanides and sulfonylureas exposure.

Metformin: Is it a drug for all reasons and diseases?

Metformin was first used to treat type 2 diabetes in the late 1950s and in 2022 remains the first-choice drug used daily by approximately 150 million people. An accumulation of positive pre-clinical and clinical data has stimulated interest in re-purposing metformin to treat a variety of diseases including COVID-19. In polycystic ovary syndrome metformin improves insulin sensitivity. In type 1 diabetes metformin may help reduce the insulin dose. Meta-analysis and data from pre-clinical and clinical studies link metformin to a reduction in the incidence of cancer. Clinical trials, including MILES (Metformin In Longevity Study), and TAME (Targeting Aging with Metformin), have been designed to determine if metformin can offset aging and extend lifespan. Pre-clinical and clinical data suggest that metformin, via suppression of pro-inflammatory pathways, protection of mitochondria and vascular function, and direct actions on neuronal stem cells, may protect against neurodegenerative diseases. Metformin has also been studied for its anti-bacterial, -viral, -malaria efficacy. Collectively, these data raise the question: Is metformin a drug for all diseases? It remains unclear as to whether all of these putative beneficial effects are secondary to its actions as an anti-hyperglycemic and insulin-sensitizing drug, or result from other cellular actions, including inhibition of mTOR (mammalian target for rapamycin), or direct anti-viral actions. Clarification is also sought as to whether data from ex vivo studies based on the use of high concentrations of metformin can be translated into clinical benefits, or whether they reflect a 'Paracelsus' effect. The environmental impact of metformin, a drug with no known metabolites, is another emerging issue that has been linked to endocrine disruption in fish, and extensive use in T2D has also raised concerns over effects on human reproduction. The objectives for this review are to: 1) evaluate the putative mechanism(s) of action of metformin; 2) analyze the controversial evidence for metformin's effectiveness in the treatment of diseases other than type 2 diabetes; 3) assess the reproducibility of the data, and finally 4) reach an informed conclusion as to whether metformin is a drug for all diseases and reasons. We conclude that the primary clinical benefits of metformin result from its insulin-sensitizing and antihyperglycaemic effects that secondarily contribute to a reduced risk of a number of diseases and thereby enhancing healthspan. However, benefits like improving vascular endothelial function that are independent of effects on glucose homeostasis add to metformin's therapeutic actions.

Distinguishing characteristics of exposure to biguanide and sulfonylurea anti-diabetic medications in the United States

OBJECTIVES

Biguanides and sulfonylureas are anti-hyperglycemic drugs commonly used in the United States. Poisoning with these drugs may lead to serious consequences. The diagnosis of biguanide and sulfonylurea poisoning is based on history, clinical manifestations, and laboratory studies.

METHODS

This study is a six-year retrospective cohort analysis based on the National Poison Data System. Clinical effects of 6183 biguanide and sulfonylurea exposures were identified using binary logistic regression.

RESULTS

The mean age of patients with biguanide and sulfonylurea exposure was 39.27 ± 28.91 and 28.91 ± 30.41 years, respectively. Sulfonylurea exposure is most commonly seen via unintentional exposure, while biguanide exposure frequently occurs as a result of intentional ingestion. Minor and moderate outcomes commonly developed following biguanide and sulfonylurea exposure, respectively. Sulfonylurea exposure was less likely to develop clinical effects abdominal pain, metabolic acidosis, diarrhea, nausea, vomiting, and elevated creatinine than patients ingesting biguanides. However, sulfonylurea exposure was more likely to cause dizziness or vertigo, tremor, drowsiness or lethargy, agitation, diaphoresis, and hypoglycemia.

CONCLUSIONS

Our study is the first to use a wide range of national data to describe the clinical characteristics that differentiate the toxicologic exposure to biguanides and sulfonylureas. Sulfonylurea exposure is commonly seen via unintentional exposure, while metformin exposure is frequently seen via intentional exposure. Sulfonylurea toxicity is more likely to cause agitation, dizziness or vertigo, tremor, diaphoresis, and hypoglycemia, while metformin exposure induces abdominal pain, acidosis, diarrhea, nausea, vomiting, and elevated creatinine.

A flow injection fluorescence "turn-on" sensor for the determination of metformin hydrochloride based on the inner filter effect of nitrogen-doped carbon dots/gold nanoparticles double-probe

In this paper, an ultrasensitive and rapid "turn-on" fluorescence sensor, integrating flow-injection (FI) with nitrogen-doped carbon dots/gold nanoparticles (N-CDs/AuNPs) double-probe is established for the determination of metformin hydrochloride (MET) in biological fluids. The sensing strategy involves the weak inner filter effect between AuNPs and N-CDs due to aggregation products of MET with AuNPs. Unfortunately, the degree of AuNPs aggregation is difficult to control through manual assays, resulting in intolerable measurement error that limits further applications. However, the proposed method overcomes the above problem, and significantly lowers the consumption of expensive reagents (AuNPs: about 60 μL per test). Under optimal conditions, the fluorescence intensity at 400 nm excitation and 505 nm emission wavelengths display a linear correlation with MET concentration (5-100 μg L^-1) and the limit of detection is 2.32 μg L^-1 (3.3 S/k). The advantages of the presented method include high sensitivity, rapid speed (60 sample h^-1), good accuracy and precision (RSD ≤ 2.1%, n = 11) and low cost. Since MET is the first-line hypoglycemic agent in patients with type II diabetes, this method can preliminarily determine MET content in urine samples, giving satisfactory results.

Visual and spectrophotometric detection of metformin based on the host-guest molecular recognition of cucurbit[6]uril-modified silver nanoparticles

A simple, sensitive, and naked-eye assay of metformin (MET), based on the host-guest molecular recognition of cucurbit[6]uril (CB[6])-modified silver nanoparticles, has been developed for the first time. The molecular recognition between CB[6] and MET is initially demonstrated and the related recognition mechanism is further discussed. CB[6]-modified AgNPs were first synthesized and then characterized by UV-vis spectroscopy, Fourier transform infrared spectroscopy, and transmission electron microscopy. The solution behavior of CB[6] in the presence of AgNO₃ was also studied, and the correlative result revealed that AgNPs could combine with the carbonyl portals of CB[6]. On the basis of the molecular recognition of CB[6] and the surface plasmon resonance effect of AgNPs, CB[6]-modified AgNPs were used as visual probes to detect MET. In CB[6]-modified AgNP solution, the aggregation of CB[6]-modified AgNPs induced by MET triggered changes of color and the UV-vis absorption spectrum, which laid the foundation for the visual identification and spectrophotometric determination of MET. Under the optimized detection conditions, the UV-vis spectral assay had a good linear relationship in the range from 3 to 750 μM, and the limit of detection was 1 μM. According to the color changes, the minimum concentration recognized by the naked eye was about 75 μM. Furthermore, this assay has high selectivity for coexisting interferents and was also applied to MET detection in human urine samples. This strategy provides a novel and facile tool for highly selective and sensitive detection of MET. Graphical abstract.