Drug Research Articles

Detecting univariate, bivariate, and overall effects of drug mixtures using Bayesian kernel machine regression

ABSTRACT Background: Innovative analytic approaches to drug studies are needed to understand better the co-use of opioids with non-opioids among people using illicit drugs. One approach is the Bayesian kernel machine regression (BKMR), widely applied in environmental epidemiology to study exposure mixtures but has received far less attention in substance use research. Objective: To describe the utility of the BKMR approach to study the effects of drug substance mixtures on health outcomes. Methods: We simulated data for 200 individuals. Using the Vale and Maurelli method, we simulated multivariate non-normal drug exposure data: xylazine (mean = 300 ng/mL, SD = 100 ng/mL), fentanyl (mean = 200 ng/mL, SD = 71 ng/mL), benzodiazepine (mean = 300 ng/mL, SD = 55 ng/mL), and nitazene (mean = 200 ng/mL, SD = 141 ng/mL) concentrations. We performed 10,000 MCMC sampling iterations with three Markov chains. Model diagnostics included trace plots, r-hat values, and effective sample sizes. We also provided visual relationships of the univariate and bivariate exposure-response and the overall mixture effect. Results: Higher levels of fentanyl and nitazene concentrations were associated with higher levels of the simulated health outcome, controlling for age. Trace plots, r-hat values, and effective sample size statistics demonstrated BKMR stability across multiple Markov chains. Conclusions: Our understanding of drug mixtures tends to be limited to studies of single-drug models. BKMR offers an innovative way to discern which substances pose a greater health risk than other substances and can be applied to assess univariate, bivariate, and cumulative drug effects on health outcomes.

Guyparkeria halophila: Novel cell platform for the efficient valorization of carbon dioxide and thiosulfate into ectoine

Utilizing carbon dioxide (CO2) for valuable chemical production is key to a circular economy. Current processes are costly due to limited microorganism use, low-value products, and the need for affordable energy. This study addresses these challenges by using industrial contaminants like thiosulfate (S2O32−) for CO2 conversion into ectoines. Ectoines, are important ingredients as pharmaceuticals and cosmetics. Here, six microbial genomes were identified as potential candidates to valorize CO2 and S2O32− into ectoine. After laboratory validation at 3 % NaCl, the fastest-growing strain, Guyparkeria halophila, was optimized at 6 %, 9 %, and 15 % NaCl, showing the highest specific ectoine contents (mgEct gbiomass−1) at 15 %. Batch bioreactors, combining optimal conditions, achieved maximum specific ectoine contents of 47 %. These results not only constitute the highest ectoine content so far reported by autotrophs and most of heterotrophs, but also the first proof of a novel valorization platform for CO2 and S2O32−, focused on pharmaceuticals production.

W single-atom mediated singlet oxygen formation on carbon nitride for powerful photocatalytic abatement of organic pollutants

Active single-atom W anchoring on graphitic carbon nitride (g-C3N4) with interplanar four-coordination with N was achieved via a microwave hydrothermal process followed by thermal polymerization. The optimal monoatomic W incorporated g-C3N4 (W-CN) catalyst exhibited a high photocatalytic activity toward a wide range of pharmaceutical and personal care products (PPCPs). The anchoring of W single atom narrowed the energy-band, negatively shifted the conduction band of g-C3N4, and promoted the charge transfer and photogenerated carrier separation. Moreover, W single-atom intercalation obviously favored the formation of singlet oxygen (1O2), as validated by the active species capture test and density functional theory (DFT) calculation, which played an important role in the photocatalytic degradation of PPCPs. The treatment of actual wastewater, toxicity assessment and hemp-pea-growth experiments further showed that the W-CN photocatalyst could effectively degrade the organic pollutants and reduce their poisonousness in the water environment. This work offers a facile route to obtain reactive g-C3N4 with single-metal active sites and some insights into its activity enhancement for the abatement of organic pollutant-containing wastewater.

NEW MATHEMATICAL MODEL FROM DYNAMIC DISSOLUTION RATE TESTS

In vitro dissolution experiments are becoming increasingly complex attempting to replicate in vivo behavior. The objective of these new methods is to explore the behavior of low-solubility drugs. This is more relevant for drugs classified in subclasses 2a, 2b and 2c of the BCS, considering their pH-dependent solubility and dissolution characteristics.A novel mathematical approach using a modified double Weibull equation is proposed to model the dissolution and precipitation kinetics observed in two-stage and transfer dissolution experiments (dumping test). This approach demonstrates a high level of accuracy in fitting experimental data for two drugs BCS class 2a and two BCS class 2b, providing valuable insights into their dissolution behavior under different conditions.The results highlight the versatility of the proposed model in capturing complex dissolution phenomena, including rapid dissolution, precipitation, and redissolution. The ease of implementation in Excel, using the Solver tool, makes it a practical and accessible tool for pharmaceutical researchers.Overall, the study contributes to the development of more effective in vitro dissolution testing methods, facilitating the formulation and optimization of pharmaceutical products and potentially aiding in the establishment of in vitro-in vivo correlations (IVIVC).

Recent approaches of regulatory perspectives for the registration of drugs in southern states in India

Recent approaches in regulatory perspectives for the registration of drugs in Southern states of India have been characterized by a focus on enhancing efficiency, transparency, and safety in the drug approval process. This involves the adoption of stringent regulatory frameworks aligned with international standards to ensure the quality, efficacy, and safety of pharmaceutical products. One significant development is the implementation of electronic submission systems for the registration of drugs, streamlining the application process and reducing administrative burdens for regulators and pharmaceutical companies. Additionally, there has been an emphasis on strengthening post-marketing surveillance mechanisms to monitor the safety and efficacy of drugs once they are in the market, enabling prompt identification and mitigation of potential risks or adverse reactions. Collaborative efforts between regulatory authorities, industry stakeholders, and healthcare professionals have also been encouraged to facilitate knowledge exchange and capacity building in regulatory compliance and pharmacovigilance. Overall, these recent approaches reflect a concerted effort to uphold public health standards and ensure access to safe and effective medications for the population of Southern states in India.

Evolutionary game model and simulation analysis of multi-stakeholder behaviour for promoting Braille labelling on pharmaceutical packaging in China’s legal framework

This study investigates the factors influencing the lack of Braille-embossed labels in Chinese pharmaceutical product packaging. To this end, this study constructs an evolutionary game model of multi-participant behaviour in drug production regulation and conducts simulation based on the perspective of stakeholders in order to study the evolutionary steady state of regulatory authorities, pharmaceutical companies, and visually impaired individuals and the influence of each parameter variable on the choice of strategic behaviour of the participants. The simulation results show that in the legal framework of China, administrative penalties, the production subsidy rate, and the Basic Medical Insurance (BMI) reimbursement rate are the main drivers of the system’s evolution towards a steady state. Reduced costs of drugs with Braille-embossed packaging is a prerequisite for the visually impaired to purchase such drugs, and the key for the whole system to converge to a stable state therefore lies in the BMI reimbursement rate for drugs with Braille-embossed packaging. This paper suggests that the Chinese government should improve the law enforcement mechanism for the regulation of the production of drugs with Braille-embossed packaging; increase the type and intensity of administrative penalties as well as production subsidies, tax incentives, and compliance incentives for pharmaceutical companies; and provide higher BMI reimbursement rates for the visually impaired. The results of the study provide valuable insights to improve accessibility to drugs and build a barrier-free environment for ensuring medication safety in China and other developing countries.

Screening and Simultaneous Determination of 80 Veterinary Drug Residues in Food by HPLC-MS/MS

Screening and Simultaneous Determination of 80 Veterinary Drug Residues in Food by HPLC-MS/MS

A Comparative Metagenomic Analysis of Specified Microorganisms in Groundwater for Non-Sterilized Pharmaceutical Products

In pharmaceutical manufacturing, ensuring product safety involves the detection and identification of microorganisms with human pathogenic potential, including Burkholderia cepacia complex (BCC), Escherichia coli, Pseudomonas aeruginosa, Salmonella enterica, Staphylococcus aureus, Clostridium sporogenes, Candida albicans, and Mycoplasma spp., some of which may be missed or not identified by traditional culture-dependent methods. In this study, we employed a metagenomic approach to detect these taxa, avoiding the limitations of conventional cultivation methods. We assessed the groundwater microbiome’s taxonomic and functional features from samples collected at two locations in the spring and summer. All datasets comprised 436–557 genera with Proteobacteria, Bacteroidota, Firmicutes, Actinobacteria, and Cyanobacteria accounting for > 95% of microbial DNA sequences. The aforementioned species constituted less than 18.3% of relative abundance. Escherichia and Salmonella were mainly detected in Hot Springs, relative to Jefferson, while Clostridium and Pseudomonas were mainly found in Jefferson relative to Hot Springs. Multidrug resistance efflux pumps and BlaR1 family regulatory sensor-transducer disambiguation dominated in Hot Springs and in Jefferson. These initial results provide insight into the detection of specified microorganisms and could constitute a framework for the establishment of comprehensive metagenomic analysis for the microbiological evaluation of pharmaceutical-grade water and other non-sterile pharmaceutical products, ensuring public safety.

Alternative therapies recently applied in controlling farmed fish diseases: mechanisms, challenges, and prospects

AbstractThe aquaculture industry is suffering from significant financial setbacks due to an increasing frequency of disease outbreaks, posing a threat to the sector’s sustainability. Various bacterial, viral, parasitic, and fungal pathogens have led to massive mortalities in farmed fish worldwide. Throughout the years, the management of fish diseases has predominantly centered around the utilization of conventional antibiotics and chemicals. Nevertheless, their indiscriminate use has given rise to serious implications, including an increase in resistant pathogens, disruptions in the metabolic processes of fish, degradation of the aquatic environment, the presence of drug residues in aquatic products, and a potential threat to human health. Various effective bio-based and immunoprophylaxis alternative therapies have been developed to overcome these impediments. Recent alternative therapeutic approaches to fish diseases encompass a range of strategies, including phytotherapeutics, nanotherapeutics, probiotics, prebiotics, synbiotics, phage therapy, vaccination, quorum quenching, antimicrobial peptides, biosurfactants, bacteriocins, stem cells, and diagnostic-based therapy. Advancements in biotechnology have significantly enhanced the efficacy of these therapies. However, additional research is essential to refine the utilization of these therapeutic approaches. Critical concerns, such as efficacy, cost, risks, availability, and adverse effects on fish and the ecosystem, need to be addressed to establish guidelines for their sustainable application in aquaculture. This review will increase aquaculturists’ awareness of recent therapies used in fish farming, their mechanisms, challenges, and impacts while promoting the sustainability of commercial aquaculture.

Quantitative 31P NMR Spectroscopy Platform Method for the Assay of Oligonucleotides as Pure Drug Substances and in Drug Product Formulations Using the Internal Standard Method.

One of the most widely used techniques for the quantification of small interfering ribonucleic acid (siRNA) is the ultraviolet (UV) spectroscopy method. However, due to uncertainties in the extinction coefficient affecting the accuracy of the method and a sample preparation including several dilution steps, the purpose of this study was to explore the possibility of determining the content of siRNA by a platform method using quantitative 31P nuclear magnetic resonance (31P-qNMR) and the internal standard method. In this paper, acquisition time, selection of a suitable internal certified reference material, signal selection used for quantification, relaxation delay, and precision are discussed. In addition, the robustness of the method and the ability to apply this platform method to both drug substance (DS) and drug product samples is also discussed. Quantifications of siRNA determined by the 31P-qNMR platform method were on average 98.5%w/w when adjusting for the sodium and water contents. The data confirmed the applicability of 31P-qNMR in siRNA content determinations. The quantifications were compared to quantifications determined by the traditional UV spectroscopy method by F- and t-tests. The statistical tests showed that the platform 31P-qNMR method provided more accurate results (mass balance close to 100% w/w) compared to the traditional UV spectroscopy method when analyzing DS samples.

Occurrence of pharmaceuticals in Latin America: case study on hazard assessment and prioritization in Costa Rica.

Pharmaceuticals are considered as contaminants of emerging concern, and their occurrence in diverse environmental matrices has been described during the last 25years. Nonetheless, pharmaceutical occurrence has not been evenly described worldwide, and reports from some geographical areas such as most parts of Latin America are scarce. This work aims to address the situation of water pollution due to pharmaceuticals in Latin America by means of two main goals: i. First, reviewing the monitoring studies performed in Latin America on this topic (period 2009-2024), which were conducted in Brazil, Mexico, Colombia, Ecuador, Peru and Argentina, to highlight the most frequently detected compounds from each therapeutic group in the region. ii. Second, analyzing the case of Costa Rica through the hazard assessment and prioritization of pharmaceuticals based on the monitoring performed in this country (years 2011; 2018-2019). The monitoring in Costa Rica comprised a total of 163 sampling points: wastewater treatment plants (WWTPs) (14 urban WWTPs plus two landfill WWTPs; total samples n = 44 influents and n = 34 effluents), nine hospital effluents (n = 32), wastewater from livestock farms (six swine farms and seven dairy farms; n = 23 influents and n = 37 effluents), 64 continental surface water sampling points (n = 137), and 61 coastal seawater sampling points (n = 61). Risk assessment of detected concentrations by the hazard quotient (HQ) approach (period 2018-2019) revealed a total of 25 mediumorhigh-hazard compounds (out of 37 detected compounds). The prioritization approach (which included the Frequency of Appearance (FoA), the Frequency of PNEC exceedance (FoE), and the Extent of predicted no-effect concentration (PNEC) exceedance (EoE)), showed a critical list of nine pharmaceuticals: caffeine, diphenhydramine, acetaminophen, lovastatin, gemfibrozil, ciprofloxacin, ibuprofen, doxycycline and norfloxacin. These compounds should be taken into account as a first concern during the implementation of environmental policies related to pharmaceutical products in the region.

Quality by design approach to improve quality and decrease cost of in vitro transcription of mRNA using design of experiments.

In vitro transcription (IVT) reaction is an RNA polymerase-catalyzed production of messenger RNA (mRNA) from DNA template, and the unit operation with highest cost of goods in the mRNA drug substance production process. To decrease the cost of mRNA production, reagents should be optimally utilized. Due to the catalytic, multicomponent nature of the IVT reaction, optimization is a multi-factorial problem, ideally suited to design-of-experiment approach for optimization and identification of design space. We derived a data-driven model of the IVT reaction and explored factors that drive process yield (in g/L), including impact of nucleoside triphosphate (NTP) concentration and Mg:NTP ratio on reaction yield and how to optimize the main cost drivers RNA polymerase and DNA template, while minimizing dsRNA formation, a critical quality attribute in mRNA products. We report a methodological approach to derive an optimum reaction design, with which cost efficiency of the reaction was improved by 44%. We demonstrate the validity of the model on mRNA construct of different lengths. Finally, we maximized the yield of the IVT reaction to 24.9 ± 1.5 g/L in batch, thus doubling the highest ever reported IVT yield.

Optimization and evaluation of modified release solid dosage forms using artificial neural network

This study aims to optimize and evaluate drug release kinetics of Modified-Release (MR) solid dosage form of Quetiapine Fumarate MR tablets by using the Artificial Neural Networks (ANNs). In training the neural network, the drug contents of Quetiapine Fumarate MR tablet such as Sodium Citrate, Eudragit® L100 55, Eudragit® L30 D55, Lactose Monohydrate, Dicalcium Phosphate (DCP), and Glyceryl Behenate were used as variable input data and Drug Substance Quetiapine Fumarate, Triethyl Citrate, and Magnesium Stearate were used as constant input data for the formulation of the tablet. The in-vitro dissolution profiles of Quetiapine Fumarate MR tablets at ten different time points were used as a target data. Several layers together build the neural network by connecting the input data with the output data via weights, these weights show importance of input nodes. The training process optimises the weights of the drug product excipients to achieve the desired drug release through the simulation process in MATLAB software. The percentage drug release of predicted formulation matched with the manufactured formulation using the similarity factor (f2), which evaluates network efficiency. The ANNs have enormous potential for rapidly optimizing pharmaceutical formulations with desirable performance characteristics.

Synthesis and Characterization of Chiral Impurities of Rivaroxaban, Used as an Anticoagulant Drug

The mechanism of action of the anticoagulant medication rivaroxaban is the direct and specific suppression of activated coagulation factor X (FXa). With just (S)-Rivaroxaban exhibiting pharmacological activity and (R)-Rivaroxaban being an impurity that must be controlled, this molecule only has one chiral centre. The present work describes the synthesis of chiral impurities of each intermediate, including KSM and its drug substance, and their characterization by spectral data (IR, MS, 1H-NMR, and 13C-NMR). The chiral impurity of drug substance is challenging during the process development of Rivaroxaban, and it is challenging for all pharmaceutical industries as well. The opposite isomeric impurity is introduced form its key starting material and it follows the same reaction mechanism path up to final drug substance. This work will help quality control (QC) to identify and control these impurities in the Rivaroxban drug substance as per ICH guideline.

Implementation of green-assessed nanotechnology and quality by design approach for development of optical sensor for determination of tobramycin in ophthalmic formulations and spiked human plasma

A fast eco-friendly colorimetric method was developed for the determination of Tobramycin in drug substance, ophthalmic formulations, and spiked human plasma using silver nanoparticles optical sensor. Even though tobramycin is non-UV–visible absorbing, the developed method is based on measuring the absorbance quenching of silver nanoparticles resulting from the interaction with tobramycin. Different factors affecting the absorbance intensity were studied as; silver nanoparticle concentration, pH, buffer type, and reaction time using quality by design approach. Validation of the proposed method was performed according to ICH guidelines and was found to be accurate, precise, and sensitive. The linearity range of tobramycin was 0.35–4.0 μg/mL. The optical sensor was successfully applied for the determination of Tobramycin in ophthalmic formulations and spiked human plasma without pre-treatment. Additionally, the binding between Tobramycin and PVP- capped silver nanoparticles was studied using molecular docking software. The method was assessed and compared to colorimetric reported methods for the green character using Green Analytical Procedure Index (GAPI) and Analytical GREEnness calculator (AGREE) tools and found to be greener.

Implementation of temporal moments to elucidate the reactive transport of metformin and erythromycin in the saturated porous media.

This study investigates the fate and transport dynamics of metformin (MTN) and erythromycin (ETM), both classified as pharmaceutical and personal care products (PPCPs), in a saturated sandy soil column using temporal moment analysis (TMA). The key flow and transport parameters, including Darcy velocity, longitudinal dispersivity, adsorption, and degradation coefficients, were analyzed. The results reveal that MTN, a highly mobile contaminant, is eliminatedfrom the column in approximately 40days, while ETM shows significant adsorption due to its hydrophobic and adsorptive nature. Darcy velocity significantly affects PPCP transport; a one-order magnitude change alters contaminant mass recovery at the column outlet by 88% for MTN and 39-fold for ETM. Longitudinal dispersivity has minimal impacton the transport of PPCPs. However adsorption primarily governs the fateofPPCPs with high adsorption coefficients (Kd), and degradation rates control the fateoflow-sorbing PPCPs. A one-order magnitude change in Kd results in a 55% change in the zeroth temporal moment (ZTM) of MTN and a 30-fold change in the case of ETM. Additionally, a one-order magnitude change in the degradation coefficient leads to a 60% variation in MTN's ZTM and a 5% variation in ETM's ZTM. Thus, TMA is a valuable tool for understanding PPCP dynamics in subsurface environments, providing critical insights for managing their increasing concentrations.

Predicting pharmaceutical prices. Advances based on purchase-level data and machine learning

BackgroundIncreased costs in the health sector have put considerable strain on the public budgets allocated to pharmaceutical purchases. Faced with such pressures amplified by financial crises and pandemics, national purchasing authorities are presented with a puzzle: how to procure pharmaceuticals of the highest quality for the lowest price. The literature explored a range of impactful factors using data on producer and reference prices, but largely foregone the use of data on individual purchases by diverse public buyers.MethodsLeveraging the availability of open data in public procurement from official government portals, the article examines the relationship between unit prices and a host of predictors that account for policies that can be amended nationally or locally. The study uses traditional linear regression (OLS) and a machine learning model, random forest, to identify the best models for predicting pharmaceutical unit prices. To explore the association between a wide variety of predictors and unit prices, the study relies on more than 200,000 purchases in more than 800 standardized pharmaceutical product categories from 10 countries and territories.ResultsThe results show significant price variation of standardized products between and within countries. Although both models present substantial potential for predicting unit prices, the random forest model, which can incorporate non-linear relationships, leads to higher explained variance (R2 = 0.85) and lower prediction error (RMSE = 0.81).ConclusionsThe results demonstrate the potential of i) tapping into large quantities of purchase-level data in the health care sector and ii) using machine learning models for explaining and predicting pharmaceutical prices. The explanatory models identify data-driven policy interventions for decision-makers seeking to improve value for money.

Headspace Gas Chromatography Mass Spectrometry Method for Determination of class-I Residual Solvents in Several Drug Substances: Method Evaluation by Quality by Design Statistical Tool.

Class-I residual solvents such as 1,1-dichloroethene, 1,1,1-trichloroethane, carbon tetrachloride, benzene, 1,2-dichloroethane are toxic, environmental hazard and carcinogenic to human. Headspace gas chromatography mass spectrometer is the sophisticated instrument for quantification of residual solvents at lower limits. An exact, sensitive, reliable and fast method was developed to determine the 1,1-dichloroethene, 1,1,1-trichloroethane, carbon tetrachloride, benzene and 1,2-dichloroethane present in different drug substances by using a headspace gas chromatography mass spectrometer. Helium is used as a carrier gas in this method. N-Methyl-2-pyrrolidone is used as a diluent and the stationary phase is a DB-624 (60 m x 0.25 mm x 1.4 μm film thickness) column with a flow rate of 1.5 mL/min. The concentration of limit of detection for 1,1-dichloroethene, 1,1,1-trichloroethane, carbon tetrachloride, benzene and 1,2-dichloroethane were achieved with 0.24 ppm, 5 ppm, 0.12 ppm, 0.06 ppm, 0.15 ppm. Subsequently, the concentrations of limit of quantification for aforementioned impurities were achieved with 0.8 ppm, 15 ppm, 0.4 ppm, 0.2 ppm, 0.5 ppm. The linearity was covered in the range of LOQ to 120% with respect to the specification level. The current method's system suitability, precision, linearity, accuracy parameters were performed in accordance to the USP < 1225> and ICH Q2(R2) and results were lies within the acceptance criteria. No research studies are reported for the determination of class-I residual solvents in lincomycin hydrochloride, dapagliflozin, vonoprazan fumarate, telmisartan drug substances. The proposed study is to develop the common method for quantification of class-I residual solvents for drug substances. QbD concept is utilized in the method performance verification.

A Retrospective Case Series Analysis of Pregnancy and COVID-19 Based on a Global Postmarketing COVID-19 Surveillance Program

PurposeTo document and characterize COVID-19 cases involving pregnancy in the context of exposure to pharmaceutical products. MethodsThis retrospective case series analysis leveraged the Pfizer safety database containing worldwide adverse event data related to use of Pfizer products between October 1, 2019 and November 3, 2022. Selected Medical Dictionary for Drug Regulatory Activities (Version 25.0) Preferred Terms and subsequent clinical review were used to identify COVID-19 cases involving female patients who received Pfizer products during pregnancy and infants with intrauterine exposure to Pfizer products. FindingsAs of November 3, 2022, 504 pregnancy cases (426 maternal; 78 infants) were identified. Most maternal cases reported COVID-19 during the third trimester, and (when known) 52% of cases involved presentation or progression of severe COVID-19 with associated complications requiring hospitalization, and often intensive management (eg, mechanical ventilation, oxygen support) and emergent delivery. Twenty-three maternal cases were fatal; patients developed severe COVID-19 disease involving multisystem deterioration (eg, cardiopulmonary injury/decompensation, coagulopathies, septic/hemorrhagic shock) and frequently required risk-benefit decisions regarding maintaining/prolonging pregnancies to improve fetal viability while attempting to improve or stabilize maternal conditions or electing to either terminate pregnancies or induce emergent deliveries. Approximately 40% of maternal cases reported medical history involving at least one underlying condition (eg, diabetes, respiratory disorders, renal/hepatic disease, cardiac disease, obesity, autoimmune conditions) considered potentially associated with susceptibility to infection/adverse outcome of infection, or twin/triplet pregnancy, which may further complicate COVID-19 disease. Most cases with known fetal outcomes reported normal newborns including preterm/low birth weight infants, which occurred in many cases involving emergent preterm delivery due to deteriorating maternal conditions. The remaining smaller proportion of cases involved abnormal newborn/perinatal/postperinatal complications (eg, premature births, respiratory distress, alveolar damage, meconium aspiration with hypoxic-ischemic encephalopathy), intrauterine/neonatal death (due to multiple concurrent complications such as neonatal sepsis, hypoxemia/acute respiratory distress, potential cardiac damage, mucormycosis) and congenital anomaly (eg, intrauterine growth restriction in association with contracting COVID-19). Among infants tested within our dataset, 28 cases involved reference to infants who tested positive for COVID-19 infection at birth or shortly thereafter, with vertical transmission suspected only in 2 infants. ImplicationThis large retrospective case series provides additional perspectives regarding potential impact of COVID-19 on pregnancy outcomes, and its characterization of this case volume may contribute to the current information landscape related to COVID-19 in pregnancy. Further studies may be warranted to confirm the generalizability of our findings to the general pregnant patient population infected with COVID-19.

The hidden cost of chronic pain: A narrative review of the environmental impact of outpatient spine and musculoskeletal care.

Health care is a major driver of greenhouse gas emissions and is closely intertwined with industrial processes responsible for air, water, and soil pollution. Chronic pain - particularly as it relates to spine and musculoskeletal diagnoses - comprises a significant portion of health care utilization and affects millions of people worldwide. Despite the prevalence of chronic spine and musculoskeletal pain, there has been limited discussion of the environmental impacts of outpatient clinics and interventional processes as they relate to these conditions. This narrative review explores the environmental impact related to diagnostics, pharmacologics, and common nonoperative interventional procedures utilized in the management of patients with chronic musculoskeletal and spine pain. Topics explored include energy utilization, production and disposal of pharmaceuticals, and waste production from interventional procedures. This study aims to educate providers involved in spine and musculoskeletal disease management regarding the possible environmental consequences of their practices. The article also focuses on modifying approaches to patient care to those that are more sustainable as well as highlighting areas in need of further investigation.