PRIMARY OVARIAN PREGNANCY IN A PRIMIGRAVIDA: A CASE REPORT

Primary ovarian pregnancy is a rare form of ectopic pregnancy due to the implantation of the gestational sac in the ovary. It is reported to occur in 1 in 25000-40000 pregnancies following natural conceptions and accounts for 0.3- 3% of all ectopic pregnancies. The preoperative diagnosis is difficult and is mostly diagnosed following surgery or histo pathological examination. Here we present a case of ovarian pregnancy which was managed surgically and its review of the literature is discussed. KEYWORDS: Ectopic pregnancy; Extrauterine pregnancy; Primary ovarian pregnancy; Diagnosis; Management

PRIMARY OVARIAN PREGNANCY IN A PRIMIGRAVIDA: A CASE REPORT

Primary ovarian pregnancy is a rare form of ectopic pregnancy due to the implantation of the gestational sac in the ovary. It is reported to occur in 1 in 25000-40000 pregnancies following natural conceptions and accounts for 0.3- 3% of all ectopic pregnancies. The preoperative diagnosis is difficult and is mostly diagnosed following surgery or histo pathological examination. Here we present a case of ovarian pregnancy which was managed surgically and its review of the literature is discussed. KEYWORDS: Ectopic pregnancy; Extrauterine pregnancy; Primary ovarian pregnancy; Diagnosis; Management

Conjugation of Au Nanoparticles with Chlorambucil for Improved Anticancer Activity

Gold nanoparticles (AuNPs) of 30–40 nm in size has been prepared using A. hirsutus leaves extract as reducing agent for Au3+ ions under microwave irradiation from 60 to 360 s. These biocapped AuNPs were effectively conjugated with activated folic acid (FA, receptor) and chlorambucil (CHL, anticancer drug) molecules. The formation of AuNPs–FA–CHL was confirmed from different characterization techniques such as XRD, UV–Visible spectra, FT-IR and TEM images. The anticancer activity of these bioconjugated AuNPs was tested against human cancer cell lines (HeLa, RKO and A549) in comparison with normal epithelial cells (Vero). Unlike AuNPs and CHL alone, AuNPs–FA–CHL showed high toxicity towards human cancer cells by significantly decreasing the percentage viability of cells. Furthermore, the amount of drug released was found to be maximum at an ideal tumor environment pH 5.3. Keyword

Thermal Stability Analysis of PbO/ISO-UP Resin Composites

Composites of isophthalate-based unsaturated polyester (ISO-UP) resin with various concentrations of lead monoxide (PbO) filler were fabricated and investigated for degradation kinetics & thermal stability of the composites. The thermogravimetric data have been treated with Freeman-Caroll & Horowitz-Metzger methods, and results were discussed. The filler concentration effect on thermal stability & degradation kinetics of composites were also discussed. The neat sample was observed to exhibit one-stage degradation, while the filled composites underwent degradation at two stages. Further, with the increased filler content in the composite, the initial degradation temperature values (IDT) were found to decrease from 3370C for the neat polymer to 3040C for 50% filled composite, whereas the presence of filler slows down the degradation process. Among the two classical degradation kinetic theories used, the Freeman-Caroll method yields almost close activation energies from 18.295KJ/mol to 20.029KJ/mol, while the Horowitz-Metzger method yields activation energies from 17.919KJ/mol to 13.198KJ/mol.

Predicting cervical cancer biopsy results using demographic and epidemiological parameters: a custom stacked ensemble machine learning approach

The human papillomavirus (HPV) is responsible for most cervical cancer cases worldwide. This gynecological carcinoma causes many deaths, even though it can be treated by removing malignant tissues at a preliminary stage. In many developing countries, patients do not undertake medical examinations due to the lack of awareness, hospital resources and high testing costs. Hence, it is vital to design a computer aided diagnostic method which can screen cervical cancer patients. In this research, we predict the probability risk of contracting this deadly disease using a custom stacked ensemble machine learning approach. The technique combines the results of several machine learning algorithms on multiple levels to produce reliable predictions. In the beginning, a deep exploratory analysis is conducted using univariate and multivariate statistics. Later, the one-way ANOVA, mutual information and Pearson’s correlation techniques are utilized for feature selection. Since the data was imbalanced, the Borderline-SMOTE technique was used to balance the data. The final stacked machine learning model obtained an accuracy, precision, recall, F1-score, area under curve (AUC) and average precision of 98%, 97%, 99%, 98%, 100% and 100%, respectively. To make the model explainable and interpretable to clinicians, explainable artificial intelligence algorithms such as Shapley additive values (SHAP), local interpretable model agnostic explanation (LIME), random forest and ELI5 have been effectively utilized. The optimistic results indicate the potential of automated frameworks to assist doctors and medical professionals in diagnosing and screening potential cervical cancer patients

Poly(N-isopropyl acrylamide)-co-poly(sodium acrylate) hydrogel for the adsorption of cationic dyes from aqueous solution

In this study, we used radical polymerization to create poly (N-isopropyl acrylamide)-co-poly (sodium acrylate) [PNIPAM-co-PSA] hydrogels and analyzed the resulting products. N, N′-Methylenebisacrylamide was employed as a cross-linker, ammonium persulfate as an initiator, and N,N′-isopropyl acrylamide and sodium acrylamide as monomers. Structural analysis was measured by using FT-IR. Indeed, SEM analysis was used to characterize the morphological structure of the hydrogel. Studies on swelling were also done. The Taguchi approach was used to study and assess the adsorption studies of the hydrogels for the efficient removal of malachite green and methyl orange. For the optimization, a central composite surface methodology was applied. The effect of several parameters, including adsorbent dosage, pH, initial dye concentration, temperature, time, and mixing speed, was examined using the Taguchi technique, and the primary factors were chosen and examined using the central composite surface methodology. It was discovered that MG dye's (cationic) removal efficiency was higher than that of MO dye's (anionic). The results suggest that [PNIPAM-co-PSA] hydrogel can be used as an effective, alternative and promising adsorbent to be applied in the treatment of effluents containing the cationic dyes from wastewater. The synthesis of hydrogels provides a suitable recyclability platform for the adsorption of cationic dyes and allows for their recovery without the use of powerful reagents

A machine learning and explainable artificial intelligence approach for predicting the efficacy of hematopoietic stem cell transplant in pediatric patients

Cancer is a fatal disease that affects people of all ages, including children. It is one of the leading causes of death worldwide. According to World Health Organization, an estimated 400,000 children develop cancer yearly. Bone marrow transplantation (BMT) is a specialized treatment for patients suffering from certain types of cancer, such as myeloma, lymphoma, leukemia, and others. It usually includes extracting healthy cells from the donor’s bone marrow and replacing the existing ones in the patient’s body. However, the treatment can also cause complications such as graft-versus-host disease, organ damage, stem cell failure, new cancers, and infections. In this study, we use machine learning and explainable artificial intelligence (XAI) techniques to predict the survival rate of children undergoing Hematopoietic Stem Cell Transplants. Three feature selection techniques have been utilized for feature selection: Harris Hawks optimization, salp swarm optimization, and mutual information. The final custom stacked model delivered optimal results with accuracy, precision (89%), recall (88%), f1-score (88%), area under curve (AUC) (92%), and average precision (86%). In addition, XAI techniques such as Shapley additive values (SHAP), local interpretable model-agnostic explanations (LIME), ELI5, and QLattice have been used to make the models more precise, understandable, and interpretable. According to XAI, the most important features were relapse, donor age, recipient age, and platelet recovery time. The promising results point to the potential use of artificial intelligence in understanding the effectiveness of bone marrow transplants in children

Studies on flocculation of clay suspension by polyacrylamide

The flocculation of dilute pottery clay suspension using polyacrylamide (PAM) was investigated. Different molecular weight PAMs were synthesized by free-radical polymerization initiated with the persulfate-bisulfate redox pair. The synthesized polyelectrolytes (PAM1, PAM2 and PAM3, from low to high molecular weight) and a commercial one (C-492) were used for flocculation studies. The flocculating performance of polyelectrolytes was measured on 3% w/v pottery clay suspension using settling tests and turbidity measurements. PAM2 at pH 5.0 showed the maximum settling rate, which is nearly three times that of C-492, and it also showed a better turbidity reduction. Molecular weight is the key factor in influencing settling and turbidity reduction. In the present study, increasing molecular weight enhanced settling rate and turbidity reduction to a certain level beyond which there is a decrease, suggesting an optimum molecular weight for the given application. PAM2, a medium molecular weight polyelectrolyte $(2.0.10^5 g/mol)$ has shown better performance than PAM1 $(1.3.10^5 g/mol)$, PAM3 $(6.0.10^5 g/mol)$ and the commercial polyelectrolyte C-492 (molecular weight of order 106)

Multiple Explainable Approaches to Predict the Risk of Stroke Using Artificial Intelligence

Stroke occurs when a brain’s blood artery ruptures or the brain’s blood supply is interrupted. Due to rupture or obstruction, the brain’s tissues cannot receive enough blood and oxygen. Stroke is a common cause of mortality among older people. Hence, loss of life and severe brain damage can be avoided if stroke is recognized and diagnosed early. Healthcare professionals can discover solutions more quickly and accurately using artificial intelligence (AI) and machine learning (ML). As a result, we have shown how to predict stroke in patients using heterogeneous classifiers and explainable artificial intelligence (XAI). The multistack of ML models surpassed all other classifiers, with accuracy, recall, and precision of 96%, 96%, and 96%, respectively. Explainable artificial intelligence is a collection of frameworks and tools that aid in understanding and interpreting predictions provided by machine learning algorithms. Five diverse XAI methods, such as Shapley Additive Values (SHAP), ELI5, QLattice, Local Interpretable Model-agnostic Explanations (LIME) and Anchor, have been used to decipher the model predictions. This research aims to enable healthcare professionals to provide patients with more personalized and efficient care, while also providing a screening architecture with automated tools that can be used to revolutionize stroke prevention and treatment