Multicriteria decision making attributes and estimation of physicochemical properties of kidney cancer drugs via topological descriptors.

Based on topological descriptors, QSPR analysis is an incredibly helpful statistical method for examining many physical and chemical properties of compounds without demanding costly and time-consuming laboratory tests. Firstly, we discuss and provide research on kidney cancer drugs using topological indices and done partition of the edges of kidney cancer drugs which are based on the degree. Secondly, we examine the attributes of nineteen drugs casodex, eligard, mitoxanrone, rubraca, and zoladex, etc and among others, using linear QSPR model. The study in the article not only demonstrates a good correlation between TIs and physical characteristics with the QSPR model being the most suitable for predicting complexity, enthalpy, molar refractivity, and other factors and a best-fit model is attained in this study. This theoretical approach might benefit chemists and professionals in the pharmaceutical industry to forecast the characteristics of kidney cancer therapies. This leads towards new opportunities to paved the way for drug discovery and the formation of efficient and suitable treatment options in therapeutic targeting. We also employed multicriteria decision making techniques like COPRAS and PROMETHEE-II for ranking of said disease treatment drugs and physicochemical characteristics.

Evaluation of Factors Contributing to Diagnosis of Crohn's Disease in the Face of Increasing Trend in Pakistan.

Background: Crohn's disease (CD) is characterized by granulomatous inflammation of the digestive tract. Diagnosing CD involves assessing clinical symptoms, radiological and endoscopic findings, and histopathological evidence. Although previously considered a disease in developed countries, CD is increasing in developing nations, but challenges exist in diagnosing CD promptly. This study aims to report diagnostic parameters for early and correct CD diagnosis in Pakistan. Methodology: A retrospective analysis from June 2016 to August 2023 of 22 CD patients was done, by data from medical records, questionnaires completed at diagnosis, and telephonic interviews. Baseline demographic and clinical characteristics were assessed, and patients were categorized using the Montreal classification. Results: CD was diagnosed in 22 patients, with a 1:1 male-to-female ratio with a mean age of 33 years (range 15-55 years). Symptoms at presentation included abdominal pain (95.5%), watery diarrhea (86.4%), fever (31.8%), rectal bleeding (54.5%), and weight loss (81.8%) with 68% having symptoms for over 12 months before diagnosis. Disease characteristics were diverse, with various patterns of involvement and histopathological findings. Conclusions: In resource-limited countries like Pakistan, the timely diagnosis of CD presents a significant healthcare challenge. Therefore, it is necessary to tackle these complex problems by enhancing diagnostic capabilities, raising medical awareness, and improving access to healthcare resources.

An estimation of physiochemical properties of bladder cancer drugs via degree-based chemical bonding topological descriptors.

The use of topological descriptors remains a significant approach due to numerous advances in the field of drug design. Descriptors provide numerical representations of a molecule's chemical characteristics when used with QSPR models. The QSPR analysis for bladder medications is the main focus of this study. Linear regression model is developed for the computed indices values, the physicochemical properties of the bladder medications are examined.Communicated by Ramaswamy H. Sarma.

Computation of Entropy Measures for Metal-Organic Frameworks.

Entropy is a thermodynamic function used in chemistry to determine the disorder and irregularities of molecules in a specific system or process. It does this by calculating the possible configurations for each molecule. It is applicable to numerous issues in biology, inorganic and organic chemistry, and other relevant fields. Metal-organic frameworks (MOFs) are a family of molecules that have piqued the curiosity of scientists in recent years. They are extensively researched due to their prospective applications and the increasing amount of information about them. Scientists are constantly discovering novel MOFs, which results in an increasing number of representations every year. Furthermore, new applications for MOFs continue to arise, illustrating the materials' adaptability. This article investigates the characterisation of the metal-organic framework of iron(III) tetra-p-tolyl porphyrin (FeTPyP) and CoBHT (CO) lattice. By constructing these structures with degree-based indices such as the K-Banhatti, redefined Zagreb, and the atom-bond sum connectivity indices, we also employ the information function to compute entropies.

Fundamental Aspects of Skin Cancer Drugs via Degree-Based Chemical Bonding Topological Descriptors.

Due to significant advancements being made in the field of drug design, the use of topological descriptors remains the primary approach. When combined with QSPR models, descriptors illustrate a molecule's chemical properties numerically. Numbers relating to chemical composition topological indices are structures that link chemical composition to physical characteristics. This research concentrates on the analysis of curvilinear regression models and degree-based topological descriptors for thirteen skin cancer drugs. The physicochemical characteristics of the skin cancer drugs are examined while regression models are built for computed index values. An analysis is performed for several significant results based on the acquired data.

A Paradigmatic Approach to Find the Valency-Based K-Banhatti and Redefined Zagreb Entropy for Niobium Oxide and a Metal-Organic Framework.

Entropy is a thermodynamic function in chemistry that reflects the randomness and disorder of molecules in a particular system or process based on the number of alternative configurations accessible to them. Distance-based entropy is used to solve a variety of difficulties in biology, chemical graph theory, organic and inorganic chemistry, and other fields. In this article, the characterization of the crystal structure of niobium oxide and a metal-organic framework is investigated. We also use the information function to compute entropies by building these structures with degree-based indices including the K-Banhatti indices, the first redefined Zagreb index, the second redefined Zagreb index, the third redefined Zagreb index, and the atom-bond sum connectivity index.

Rationale and performances of a data-driven method for computing the duration of pharmacological prescriptions using secondary data sources.

The assessment of the duration of pharmacological prescriptions is an important phase in pharmacoepidemiologic studies aiming to investigate persistence, effectiveness or safety of treatments. The Sessa Empirical Estimator (SEE) is a new data-driven method which uses k-means algorithm for computing the duration of pharmacological prescriptions in secondary data sources when this information is missing or incomplete. The SEE was used to compute durations of exposure to pharmacological treatments where simulated and real-world data were used to assess its properties comparing the exposure status extrapolated with the method with the "true" exposure status available in the simulated and real-world data. Finally, the SEE was also compared to a Researcher-Defined Duration (RDD) method. When using simulated data, the SEE showed accuracy of 96% and sensitivity of 96%, while when using real-world data, the method showed sensitivity ranging from 78.0 (nortriptyline) to 95.1% (propafenone). When compared to the RDD, the method had a lower median sensitivity of 2.29% (interquartile range 1.21-4.11%). The SEE showed good properties and may represent a promising tool to assess exposure status when information on treatment duration is not available.

Artificial Intelligence in Pharmacoepidemiology: A Systematic Review. Part 1-Overview of Knowledge Discovery Techniques in Artificial Intelligence.

AIM: To perform a systematic review on the application of artificial intelligence (AI) based knowledge discovery techniques in pharmacoepidemiology. STUDY ELIGIBILITY CRITERIA: Clinical trials, meta-analyses, narrative/systematic review, and observational studies using (or mentioning articles using) artificial intelligence techniques were eligible. Articles without a full text available in the English language were excluded. DATA SOURCES: Articles recorded from 1950/01/01 to 2019/05/06 in Ovid MEDLINE were screened. PARTICIPANTS: Studies including humans (real or simulated) exposed to a drug. RESULTS: In total, 72 original articles and 5 reviews were identified via Ovid MEDLINE. Twenty different knowledge discovery methods were identified, mainly from the area of machine learning (66/72; 91.7%). Classification/regression (44/72; 61.1%), classification/regression + model optimization (13/72; 18.0%), and classification/regression + features selection (12/72; 16.7%) were the three most frequent tasks in reviewed literature that machine learning methods has been applied to solve. The top three used techniques were artificial neural networks, random forest, and support vector machines models. CONCLUSIONS: The use of knowledge discovery techniques of artificial intelligence techniques has increased exponentially over the years covering numerous sub-topics of pharmacoepidemiology. SYSTEMATIC REVIEW REGISTRATION: Systematic review registration number in PROSPERO: CRD42019136552.

Artificial Intelligence in Pharmacoepidemiology: A Systematic Review. Part 2-Comparison of the Performance of Artificial Intelligence and Traditional Pharmacoepidemiological Techniques.

Aim: To summarize the evidence on the performance of artificial intelligence vs. traditional pharmacoepidemiological techniques. Methods: Ovid MEDLINE (01/1950 to 05/2019) was searched to identify observational studies, meta-analyses, and clinical trials using artificial intelligence techniques having a drug as the exposure or the outcome of the study. Only studies with an available full text in the English language were evaluated. Results: In all, 72 original articles and five reviews were identified via Ovid MEDLINE of which 19 (26.4%) compared the performance of artificial intelligence techniques with traditional pharmacoepidemiological methods. In total, 44 comparisons have been performed in articles that aimed at 1) predicting the needed dosage given the patient's characteristics (31.8%), 2) predicting the clinical response following a pharmacological treatment (29.5%), 3) predicting the occurrence/severity of adverse drug reactions (20.5%), 4) predicting the propensity score (9.1%), 5) identifying subpopulation more at risk of drug inefficacy (4.5%), 6) predicting drug consumption (2.3%), and 7) predicting drug-induced lengths of stay in hospital (2.3%). In 22 out of 44 (50.0%) comparisons, artificial intelligence performed better than traditional pharmacoepidemiological techniques. Random forest (seven out of 11 comparisons; 63.6%) and artificial neural network (six out of 10 comparisons; 60.0%) were the techniques that in most of the comparisons outperformed traditional pharmacoepidemiological methods. Conclusion: Only a small fraction of articles compared the performance of artificial intelligence techniques with traditional pharmacoepidemiological methods and not all artificial intelligence techniques have been compared in a Pharmacoepidemiological setting. However, in 50% of comparisons, artificial intelligence performed better than pharmacoepidemiological techniques.

A microscopically calcified Meckel's diverticulum: a histopathological perspective of a case of both gastric and pancreatic mucosae.

A 2-year-old child presented with a 1-week history of abdominal pain and non-bilious vomiting. Upon examination, the abdomen was distended and faecal aspirate was noted from a nasogastric tube. Ultrasound scan and a failed air enema demonstrated intestinal intussusception warranting a surgical intervention. The intraoperative findings were of an ileocolic intussusception that was reduced and a Meckel's diverticulum (MD) was noted as a lead point necessitating resection with an end-to-end anastomosis. Histopathological analysis revealed a heterotopic MD containing both gastric and pancreatic mucosae with dystrophic calcification. MD is a pathologically diverse condition. The commonest histopathological picture in MD is that of an ectopic gastric mucosa at the terminal ileum. The histopathological analysis of this case demonstrated the interesting heterotopic results with calcification, which the histopathologist needs to be aware of when interpreting a surgical specimen with a clinical picture consistent with intussusception.

Congenital anomalies are commonly associated with exomphalos minor.

AIM: The objective of this study is to define the incidence of chromosomal and congenital anomalies in neonates with exomphalos major and minor. BACKGROUND: Incidence of major congenital anomalies varies from 35% to 81% in exomphalos. It is unclear whether these malformations are more common with exomphalos major. MATERIAL AND METHODS: The case notes of 82 antenatal diagnoses of exomphalos, made between January 1998 and December 2004, were retrospectively reviewed. Exomphalos major was defined as a defect 5 cm or greater and exomphalos minor a defect less than 5 cm in diameter. RESULTS: There were 72 live births, 6 still births, and 4 terminations of pregnancy. There was no statistical significance between exomphalos major and minor regarding mode of delivery, gestational age at birth, birth weight, major cardiac anomalies (21% vs 23%), and renal and external genitalia abnormalities (11% vs 18%). Chromosomal anomalies, syndromes, and dysmorphism were common in exomphalos minor 17 (39%, P = .0001). Congenital malformations of the gastrointestinal tract (14% vs 27%), central nervous system (0 vs 21%), and Wilms' tumor (0 vs 5%) occurred commonly in exomphalos minor. Limb abnormalities (25% vs 5%), ectopia cordis (11% vs 0), and bladder exstrophy (7% vs 0) occurred predominantly in exomphalos major. Mean follow-up was 34 months. Three neonates with exomphalos major died. Overall mortality was 4%. CONCLUSIONS: Chromosomal anomalies and syndromes occur more commonly in exomphalos minor. Exomphalos minor and major seem to have a predilection for associated anomalies of specific organ systems. This predisposition may help in counseling parents, planning investigations, and organization of multidisciplinary management strategy.