Search

Postoperative anesthetic complications are not uncommon. Nonetheless, surgical procedures induce specific anatomical and physiological changes. Managing patients undergoing surgery remains complex across various phases of hospital care. Anesthesiologists frequently administer up to half a million doses during their careers, and morbidity and mortality linked to improper dosing and administration postoperatively are frequent concerns. The likelihood of errors is unpredictable and can lead to serious consequences. This review aims to assist anesthesiologists in adopting updated clinical skills to better assess and manage anesthetic complications. The purpose of this literature review is to explore and discuss the main categories of adverse complications and mortality rates following anesthetic administration. The information was gathered through electronic searches, including Google Scholar and PubMed. Anesthesiologists need to classify complications systematically to outline anesthetic risks clearly. Doing so will improve outcomes for vulnerable patients and enable faster, more accurate interventions.

Turmeric (Curcuma longa L.) has antiestrogenic effects that may interfere with the activity of the hypothalamic-pituitary axis, thereby disrupting estrogen production and influencing both uterine weight and diameter. This experiment investigated how turmeric extract affects uterine parameters in female white rats (Rattus norvegicus, Sprague Dawley strain). A total of 28 rats were assigned to four groups: the control group (C) received only H₂O. In contrast, treatment groups T1, T2, and T4 were administered turmeric extract at doses of 250 mg/Kg BW, 500 mg/Kg BW, and 1000 mg/Kg BW, respectively, each combined with 1 ml of H₂O. The treatments were given once daily for five days. Results indicated that turmeric extract at 250 mg/Kg BW and 1000 mg/Kg BW led to a significant reduction in both uterine weight and diameter. In summary, turmeric extract reduced uterine size and weight, indicating its antiestrogenic capability in female rats.

Gout disease is recognized as one of the most prevalent forms of arthritis worldwide, with an estimated prevalence of around 2.5%. Its pathophysiology is primarily linked to elevated levels of uric acid in the bloodstream, which may result from either increased production or reduced renal excretion. Clinically, it often presents as a swollen, erythematous, and painful joint. The definitive method for diagnosis remains synovial aspiration from the affected joint. While acute attacks typically require management in the emergency department, long-term care is generally provided in primary care settings.This literature review aims to explore gout disease in terms of its clinical features, diagnostic approaches, and treatment strategies, particularly focusing on the role of primary care. A comprehensive search was conducted on the PubMed database using the MeSH term “gout disease” to identify pertinent studies. Gout disease continues to be among the most widespread rheumatologic conditions globally, and unhealthy lifestyle choices, including alcohol intake and high consumption of red and white meats, may exacerbate its incidence. Diagnosis may involve blood tests, radiographic imaging, and joint aspiration. Maintenance therapy, often administered in primary care, includes the use of Allopurinol or other urate-lowering agents.

This study assesses the risk of severe hyperlactatemia (defined as lactate levels > 4 mmol/L) in patients undergoing chronic low-dose aspirin (ASP) therapy and estimates the strength of this association. A case-crossover design was employed, in which individuals who experienced the outcome also served as their own controls, with exposure evaluated during a pre-event period. Additionally, a person-day–based analytical approach was used to compare exposure between case and control periods. Among the case group (ASP exposed/unexposed), the distribution was 127/578, while in the control group it was 547/3,968, yielding an odds ratio (OR) of 1.6 (95% CI: 1.29–1.97; z = 4.31; P < 0.0001). The findings suggest a modest association between low-dose aspirin use (100 mg/day) and elevated lactate levels, particularly in its primary indication for secondary prevention of vascular ischemic events. Although the observed risk is relatively low (OR = 1.6), clinical monitoring is advisable, especially when aspirin is co-administered with agents sharing similar toxicological profiles. Incorporating lactate level assessment into therapeutic management plans may enhance patient safety and therapeutic outcomes.

Producing enantiomerically pure drugs from racemic mixtures has become a critical objective in modern pharmaceutical science. Racemic compounds typically consist of two or more enantiomers, of which only one may be therapeutically active, while the others may be biologically inactive or even toxic, including teratogenic effects. Hence, the ability to effectively isolate the desired enantiomer is essential for ensuring both the safety and effectiveness of pharmaceutical treatments. This mini-review provides an overview of recent innovations in chiral stationary phases (CSPs) employed for the resolution of racemic drugs and mixtures. It covers various classes of CSPs, including those based on Pirkle-type selectors, polysaccharides, polypeptides, inclusion complexes, ligand-exchange mechanisms, macrocyclic antibiotics, and other novel materials. The performance of these phases in a range of separation techniques—such as high-performance liquid chromatography (HPLC), gas chromatography (GC), capillary electrophoresis (CE), supercritical fluid chromatography (SFC), and simulated moving bed (SMB) chromatography—is examined. Emphasis is also placed on the types of molecular interactions between CSPs and target analytes that drive effective enantioseparation.

Doxorubicin, an anthracycline, is a potent anti-cancer drug; however, its clinical use is hindered by its acute and chronic side effects, particularly cardiotoxicity. This study aimed to assess the protective effects of quercetin on doxorubicin-induced cardiotoxicity. Wistar rats were divided into five groups: a control group receiving saline (1 mL/kg), a quercetin control group receiving DMSO (1 mL/kg), a quercetin group receiving quercetin (20 mg/kg), a doxorubicin group receiving doxorubicin (25 mg/kg) intraperitoneally for three days, and a pretreatment group receiving quercetin (20 mg/kg) for 14 days before being treated with doxorubicin (25 mg/kg). After 14 days, the rats’ weights were recorded, and heart tissue samples were collected for histopathological examination. The results revealed significant weight loss in the doxorubicin-treated animals (P < 0.05), while quercetin pretreatment prevented the weight loss. Pathological analysis showed that quercetin protected the heart tissue from doxorubicin-induced damage. Overall, this study suggests that quercetin pretreatment effectively prevents doxorubicin-induced cardiotoxicity, likely due to its antioxidant properties.

Snakebite envenoming remains a significant global health challenge, with high mortality and morbidity rates persisting despite decades of medical attention. Each year, millions are affected by venomous snakebites, often resulting in death or severe disability. Snake venoms exhibit diverse bioactivities, including hemorrhagic, inflammatory, cytotoxic, cardiotoxic, and neurotoxic effects, mainly due to complex mixtures of toxin-rich proteins. Although considerable research has been undertaken, the majority of venom components remain uncharacterized. Recent advancements in proteomics and bioinformatics have enabled more detailed exploration of venom profiles, facilitating the identification and functional prediction of novel toxins. Computational approaches now enable modeling of toxin-target interactions, aiding understanding of venom mechanisms. This review also explores the emerging role of medicinal plants in snakebite treatment, alongside conventional antivenoms. Emphasis is placed on the urgent need to improve access to safe, affordable, and effective antivenoms in low-income tropical regions and to promote their appropriate clinical use.

Sildenafil citrate (SC), known for its role as a phosphodiesterase type-5 (PDE5) inhibitor, enhances the activity of cyclic guanosine monophosphate (cGMP). This study explores SC’s influence on blood sugar regulation and blood-related parameters in rats with diabetes induced by streptozotocin (STZ). 50 male Wistar rats were randomly assigned to four experimental groups: (i) a control group (n = 10), (ii) a control group receiving SC (n = 10), (iii) a diabetic group (n = 15), and (iv) a diabetic group treated with SC (n = 15). Diabetes was triggered using a single intraperitoneal dose of STZ (50 mg/kg), followed by oral administration of SC at 20 mg/kg daily for six weeks. Blood analyses were conducted to evaluate fasting glucose, insulin, HbA1c, liver enzymes (AST, ALT), renal markers (urea, creatinine), and coagulation profiles (PT, aPTT, fibrinogen, protein C, protein S). Diabetic rats showed significant increases in glucose, HbA1c, AST, ALT, urea, creatinine, and fibrinogen levels, along with reductions in insulin, aPTT, protein C, and protein S compared with non-diabetic controls. PT remained unaffected. SC did not significantly alter any parameters in non-diabetic rats, but in diabetic ones, it restored most measurements toward normal levels (P < 0.05). These findings indicate that SC may support better glycemic control and improve microvascular function, offering potential therapeutic value in mitigating diabetes-related complications.

The growing global population, particularly the increased density of urban areas in developed nations, has inevitably accelerated the transmission of various infectious diseases. Effective treatment of respiratory and gastrointestinal conditions often hinges on antibiotics. However, many viruses have developed resistance to specific antibiotic treatments. Currently, one of the most significant challenges in the pharmaceutical industry is developing and producing novel antibiotic classes. This article examines the properties of a new macrolide antibiotic, novomycin. Macrolides are widely prescribed antibiotics for both adults and children, functioning by disrupting protein synthesis within microbial cells. The study explored the acute and chronic toxicity of novomycin in laboratory animals, its potential allergic reactions on the skin, and its impact on pregnancy and fetal development. Furthermore, the antimicrobial properties of novomycin were investigated. Research into its antimicrobial efficacy revealed that, when administered 3 hours before infection in white mice, novomycin provided 63% protection against Bordetella infections, 44% against Salmonella infections, 56% against Pasteurella infections, and 80% against staphylococcal infections. The findings support the drug’s effectiveness and safety.

Parkinson’s disease stands as the second most common neurodegenerative disorder worldwide. This study aimed to assess the impact of DMSO in a rotenone-induced rat model of Parkinson’s disease. DMSO has become a popular agent in preclinical and clinical studies due to its ability to facilitate the transport of poorly soluble drugs across the blood-brain barrier. In this investigation, we explored how a three-week treatment with rotenone, combined with DMSO, influenced hippocampal neuronal activity and the properties of neuronal responses in rats. We specifically compared the toxic effects of rotenone on hippocampal CA1 and CA3 neurons in the presence of DMSO. Our results showed that rotenone induced substantial morphological changes in hippocampal cells. Following DMSO treatment, however, there was a significant restoration of pyramidal cells and Nissl bodies within the CA1 and CA3 regions. DMSO also effectively suppressed both outward and inward currents. Additionally, we recorded spontaneous and evoked spike activity in the hippocampus of rats treated with DMSO (1 ml/kg, administered intraperitoneally for 3 weeks). While rotenone elevated TP and produced a moderate TD effect, DMSO also increased TP but produced a more pronounced TD effect. The analysis indicated inhibitory responses in the hippocampus following high-frequency stimulation (100 Hz for 1 second) of the ipsilateral entorhinal cortex.

Assessing erythrocyte acid resistance is a key part of understanding the effects of toxicants on the blood. The objective of this research is to examine both the isolated and combined effects of cadmium, lead, and zinc ions from contaminated drinking water on the acid resistance of erythrocytes in laboratory rats. This investigation was conducted in the Laboratory of Anatomy, Physiology, and Histology at Chechen State University in Grozny, Russia. The study used laboratory rats weighing 100-150 grams, bred in the university’s vivarium. Exposure to metals altered erythrograms, with a noticeable increase in the proportion of erythrocytes with lower resistance and a reduction in hemolysis time. The most considerable alterations were observed after prolonged exposure to Pb2+, Cd2+, Zn2+, and their mixture. After 30 days of exposure to these ions, the peak times for erythrograms were recorded as 0.5 minutes for Pb2+, 1.0 minutes for Zn2+, and 1.5 minutes for Cd2+. The percentage of erythrocytes undergoing hemolysis at these times was significantly higher, being three times more than the control for Pb2+ and Zn2+, and comparable to the control for Cd2+ (36%). Hemolysis times were notably shorter—2.5 minutes for Pb2+ and Zn2+, and 4.5 minutes for Cd2+. By the end of the 30 days, all rats in the heavy-metal exposure group had died. The findings indicate that prolonged exposure to heavy metals induces significant changes in the erythrocyte population and their acid resistance.

Digital twins promise to reshape pharmaceutical development and manufacturing by creating virtual replicas that mirror physical systems over time. Their value lies in linking process knowledge, real-time data, predictive modelling, and decision support into a single operational framework. In pharmaceutical contexts, this promise is especially relevant because product quality is tightly coupled to process history, material attributes, and patient-facing performance. The core problem is that digital twin development in pharma remains fragmented across manufacturing, drug delivery, regulatory modelling, and digital transformation literatures. Manufacturing studies often focus on process control, PAT, and continuous production, whereas drug delivery studies emphasise physiological prediction, formulation performance, and patient-specific behaviour. These areas share mechanistic foundations, but they are rarely treated as parts of a unified pharmaceutical digital twin ecosystem. This conceptual review analyses digital twin logic across pharmaceutical manufacturing and drug delivery systems. It focuses on how mechanistic models, hybrid modelling, and real-time data infrastructures can be combined to support quality, performance prediction, and regulatory decision-making. The central argument is that digital twins must become not only predictive but also explainable and governable. The synthesis defines the architecture of pharmaceutical digital twins, catalogues their manufacturing and drug delivery applications, and identifies unresolved challenges in model coupling, parameter identifiability, uncertainty handling, and regulatory credibility. It also maps technological, organisational, economic, and regulatory barriers that prevent promising models from becoming routine industrial tools. Five tables summarise the conceptual architecture, application domains, model architectures, integration problems, and implementation barriers. Realising the full potential of pharmaceutical digital twins will require mechanistic rigour, explainable analytics, high-quality data connectivity, and early alignment with regulatory expectations. The future of the field will depend less on isolated demonstrations and more on reusable validation strategies, transparent model governance, and cross-sector collaboration. Digital twins should therefore be understood as evolving regulatory-scientific infrastructures rather than as standalone computational artefacts.

Current pharmaceutical development often treats formulation design, process control, and patient-focused performance as sequential domains rather than mutually dependent components of one technology system. This separation can produce technically elegant formulations that are difficult to manufacture, tightly controlled processes that do not fully serve patient needs, or patient-friendly dosage forms that lack robust process translation. A systems perspective is therefore needed to connect product intent, manufacturing feasibility, and real-world usability from the earliest stages of development. The central problem is the absence of an integrated theory that explains how formulation decisions, process control strategies, and patient-centric targets should be co-optimised. Existing development pathways often allow these domains to interact only after critical decisions have already been made. This creates avoidable friction during scale-up, regulatory justification, and clinical implementation. The objective of this article is to propose a theory-driven systems framework for applied pharmaceutical technologies. The framework integrates formulation design logic, process control logic, and patient-centric performance into a unified conceptual model. It is intended to guide early decision-making, cross-functional communication, and translational planning. The resulting framework identifies three interacting pillars: formulation design as the material and biopharmaceutical architecture of the product, process control as the mechanism for assuring reproducible quality, and patient-centric performance as the translation of product attributes into acceptability, adherence, and therapeutic usability. Four tables capture the formulation parameters, process control strategies, patient-centric targets, and integrated framework components. Together, these elements define a systems logic for pharmaceutical technology development. The proposed framework provides a conceptual blueprint for developing pharmaceutical products that are simultaneously manufacturable, quality-assured, and optimised for patients. It supports earlier recognition of trade-offs, clearer integration of predictive models, and stronger alignment between development choices and clinical use. Its broader value lies in reframing pharmaceutical technology as a patient-anchored system rather than a sequence of isolated technical operations.

Personalized medicine has transformed how disease risk, diagnosis, and therapeutic selection are conceptualised, yet the pharmaceutical products used to deliver many therapies remain comparatively static. In many clinical settings, personalization still means selecting a drug more precisely while administering it through conventional dosage forms. This creates a widening gap between diagnostic sophistication and pharmaceutical responsiveness. The prevailing interpretation of personalized pharmaceutics is often reduced to customized dosing. Dose adjustment is important, but it cannot by itself address fluctuating physiology, variable adherence, lifestyle change, disease progression, or treatment-emergent toxicity. A patient’s therapeutic need is not a fixed parameter but a moving target. This conceptual review reframes personalized pharmaceutics as the design of adaptive therapeutic systems. Such systems are not merely individualized at the point of prescription or manufacture; they sense, respond, and evolve with the patient over time. The central argument is that pharmaceutical technology must move from static customization toward dynamic therapeutic adaptation. The analysis shows that customized dosing addresses only one layer of patient variability, whereas adaptive therapeutic systems combine patient-specific design, feedback control, responsive materials, digital monitoring, and translational governance. Four tables clarify definitions, identify unresolved limits of dose-only personalization, describe representative adaptive systems, and map translation challenges. Together, these elements support a broader conceptual vocabulary for personalized pharmaceutics. Realising the full potential of personalized medicine requires pharmaceutical systems that co-evolve with the patient. The future of personalized pharmaceutics therefore depends not only on tailoring what dose is given, but on designing systems that can adapt when, how, where, and why therapy is delivered. This shift has implications for formulation science, device engineering, clinical trials, regulation, reimbursement, and patient participation.

Generative artificial intelligence is becoming increasingly relevant to pharmaceutical formulation because it can propose compositions, excipient combinations, processing conditions, and optimisation trajectories that may not be obvious through conventional experimental design. These capabilities create the possibility of faster development, broader exploration of formulation space, and more systematic use of prior knowledge. Yet the same models that expand formulation creativity often operate through complex latent representations that are difficult to interpret. This creates a trust problem for both scientific and regulatory decision-making. The central problem is that an AI-generated formulation is not only a predicted technical solution but also a claim about product performance, manufacturability, and quality. If the rationale behind that claim cannot be explained, formulation scientists may struggle to convert model outputs into mechanistic understanding. Regulators may likewise find it difficult to assess whether the proposed formulation is supported by transparent evidence. Opaque formulation design therefore risks becoming a translational bottleneck rather than an innovation accelerator. This perspective develops a conceptual framework for dual-purpose explainability in AI-generated pharmaceutical formulation design. The framework is designed to serve two decision contexts simultaneously. Scientific decision-makers require explanations that clarify formulation logic, reveal influential variables, and support hypothesis generation. Regulatory decision-makers require explanations that are auditable, reproducible, uncertainty-aware, and connected to product quality and safety evidence. The article first defines the conceptual gap between existing AI formulation capabilities and explainability expectations. It then describes the logic of AI-generated formulation, identifies distinct scientific and regulatory explanation requirements, and analyses transparency barriers. The proposed framework integrates global model explanations, local formulation-specific explanations, mechanistic interpretation, uncertainty communication, and regulatory evidence packaging. Three tables summarise the gap analysis, explainability requirements, and framework architecture. The article concludes that explainability must be treated as a design requirement rather than a post hoc add-on to pharmaceutical AI. AI-generated formulation designs will become useful only when their rationale can be interrogated, documented, challenged, and connected to established principles of product and process understanding. A dual-purpose explainability framework can help move the field from black-box prediction toward transparent, accountable, and scientifically meaningful formulation intelligence.

Pharmaceutical quality is often operationalized through stability testing, in which products are exposed to defined temperature and humidity conditions to support shelf-life assignment. This practice is indispensable, but it can create a narrow interpretation of quality when stability under controlled chamber conditions is treated as evidence of real-world performance. Products do not move through idealized chambers; they move through development uncertainty, manufacturing variability, distribution stress, and patient-level handling. This article argues that the dominant stability paradigm has encouraged a conceptual conflation between stability and robustness. Stability testing primarily asks whether a product remains within specification under predefined storage conditions for a defined period. Robustness, by contrast, asks whether the product–process–use system can continue to deliver acceptable quality when exposed to interacting stresses across the full lifecycle. The objective of this article is to develop an Integrated Robustness Theory for pharmaceutical products. The theory frames robustness as a system-level property spanning development, manufacturing, storage, and administration. It proposes that quality should be understood not only as shelf-life survival but also as resilient performance under realistic and combined stress conditions. The article critiques the limits of stability testing, defines robustness dimensions across lifecycle phases, and develops a systems-based framework for translating robustness into development strategy, manufacturing control, storage evaluation, and administration design. Three tables are used to map lifecycle robustness dimensions, storage stress gaps, and the integrated theory. The central conclusion is that pharmaceutical quality assurance must move beyond shelf-life thinking toward lifecycle robustness thinking.