Nanofabrication synthesis and its role in antibacterial, anti-inflammatory, and anticoagulant activities of AgNPs synthesized by Mangifera indica bark extract.

The bio-based nanoparticles synthesis and assessment of their potential biomedical applications related research is rapidly emerging. The ability of an aqueous ethanolic bark extract of Mangifera indica to synthesize silver nanoparticles (AgNPs) as well as its antibacterial, anti-inflammatory, and anticancer activities were investigated in this study. Interestingly, the bark extract effectively synthesized the AgNPs, including an absorbance peak at 412 nm and sizes ranging from 56 to 89 nm. The Fourier Transform Infrared spectroscopy (FTIR) analysis confirmed that the presence of most essential functional groups belongs to the most bioactive compounds. Synthesized AgNPs showed fine antibacterial activity against the Urinary Tract Infection (UTI) causing bacterial pathogens such as Escherichia coli, Enterococcus faecalis, Klebsiella pneumoniae, Proteus mirabilis, and Staphylococcus saprophyticus at 50 µg mL-1 concentrations. The minimum inhibitory concentration (MIC) and minimum bactericidal concentration (MBC) of AgNPs against these pathogens were found as 12.5 ± 0.8 & 13 ± 0.6, 13.6 ± 0.5 & 14 ± 0.7, 11.5 ± 0.3 & 11.5 ± 0.4, 13 ± 0.8 & 13 ± 0.7, and 11.8 ± 0.4 & 12 ± 0.8 µg mL-1 respectively. Interestingly, this AgNPs also possesses outstanding anti-inflammatory and anticancer activities as studied against the egg albumin denaturation (85%) inhibition and MCF 7 (Michigan Cancer Foundation-7: breast cancer cells) cell line (cytotoxicity: 80.1%) at 50 µg mL-1 concentration. Similarly at 50 µg mL-1 concentration showed 75% of DPPH radical scavenging potential. These activities were dose dependent, and the findings suggest that the M. indica bark aqueous ethanolic extract synthesized AgNPs can be used as antibacterial, anti-inflammatory, and anticancer agents after in-vivo testing.

Bayesian Analysis of Dynamic Cumulative Residual Entropy for Lindley Distribution.

Dynamic cumulative residual (DCR) entropy is a valuable randomness metric that may be used in survival analysis. The Bayesian estimator of the DCR Rényi entropy (DCRRéE) for the Lindley distribution using the gamma prior is discussed in this article. Using a number of selective loss functions, the Bayesian estimator and the Bayesian credible interval are calculated. In order to compare the theoretical results, a Monte Carlo simulation experiment is proposed. Generally, we note that for a small true value of the DCRRéE, the Bayesian estimates under the linear exponential loss function are favorable compared to the others based on this simulation study. Furthermore, for large true values of the DCRRéE, the Bayesian estimate under the precautionary loss function is more suitable than the others. The Bayesian estimates of the DCRRéE work well when increasing the sample size. Real-world data is evaluated for further clarification, allowing the theoretical results to be validated.

E-Bayesian Estimation of Chen Distribution Based on Type-I Censoring Scheme.

In this paper, E-Bayesian estimation of the scale parameter, reliability and hazard rate functions of Chen distribution are considered when a sample is obtained from a type-I censoring scheme. The E-Bayesian estimators are obtained based on the balanced squared error loss function and using the gamma distribution as a conjugate prior for the unknown scale parameter. Also, the E-Bayesian estimators are derived using three different distributions for the hyper-parameters. Some properties of E-Bayesian estimators based on balanced squared error loss function are discussed. A simulation study is performed to compare the efficiencies of different estimators in terms of minimum mean squared errors. Finally, a real data set is analyzed to illustrate the applicability of the proposed estimators.

Clinical characteristics and outcome of Tuberculosis lymphadenitis in a tertiary center from Saudi Arabia.

Introduction: Tuberculosis is among the deadliest infectious diseases. Lymphadenitis is an inflammation of the lymph nodes which is the most common extrapulmonary manifestation of tuberculosis. Saudi Arabia is rated as a country with a low incidence of tuberculosis. The study's objective is to describe the clinical characteristics and outcome of TB lymphadenitis (TBL) at a large tertiary care center in Riyadh, Saudi Arabia. Method: All patients 18 years and older diagnosed with TB lymphadenitis between 2010 and 2021 at a single tertiary center in Riyadh, Saudi Arabia, were reviewed retrospectively for their clinical presentation, diagnostic yield, therapy, and outcome. Result: 107 patients were included in the final analysis. The distribution of males and females were nearly equal, at 50.5 % and 49.5 %, respectively. The average age was 45. During the ten-year period of our investigation, the number of confirmed TBL ranged from 19 (the highest in 2010) to as little as one patient in 2021. 72.8 percent of patients presented with TBL affecting the neck. The most commonly used diagnostic method was histopathological examination of the tissue sample, granulomatous inflammations were found in 89.2 % of cases of theses necrotizing granuloma. 10.7 % of our isolates had resistance. The average duration of anti-TB treatment was 6.8 months with a cure rate of 72.9 %. Conclusion: The majority of patients in this study had cervical lymphadenopathy, with histopathology being the mainstay of diagnosis. 90 % of TBL cultures were susceptible to first-line anti-TB therapy.

A New Useful Exponential Model with Applications to Quality Control and Actuarial Data.

The compounding approach is used to introduce a new family of distributions called exponentiated Bell G, analogy to exponentiated G Poisson. Several essential properties of the proposed family are obtained. The special model called exponentiated Bell exponential (EBellE) is presented along with properties. Furthermore, the risk theory related measures including value-at-risk and expected-shortfall are also computed for the special model. Group acceptance sampling plan is designed when a lifetime of a product or item follows an EBellE model taking median as a quality parameter. The parameters of the proposed model are estimated by considering maximum likelihood approach along with simulation analysis. The usefulness of the proposed model is illustrated by practical means which yield better fits as compared to several exponential related extended models.

A new extended gumbel distribution: Properties and application.

A robust generalisation of the Gumbel distribution is proposed in this article. This family of distributions is based on the T-X paradigm. From a list of special distributions that have evolved as a result of this family, three separate models are also mentioned in this article. A linear combination of generalised exponential distributions can be used to characterise the density of a new family, which is critical in assessing some of the family's properties. The statistical features of this family are determined, including exact formulations for the quantile function, ordinary and incomplete moments, generating function, and order statistics. The model parameters are estimated using the maximum likelihood method. Further, one of the unique models has been systematically studied. Along with conventional skewness measures, MacGillivray skewness is also used to quantify the skewness measure. The new probability distribution also enables us to determine certain critical risk indicators, both numerically and graphically. We use a simulated assessment of the suggested distribution, as well as apply three real-world data sets in modelling the proposed model, in order to ensure its authenticity and superiority.

Case Report: Dubin-Johnson Syndrome Presenting With Infantile Cholestasis: An Overlooked Diagnosis in an Extended Family.

Dubin-Johnson syndrome (DJS) is an often-missed diagnosis of neonatal cholestasis. We report two patients with DJS, who presented with neonatal cholestasis. The first patient underwent extensive investigations for infantile cholestasis with no definitive etiology reached; the diagnosis of DJS was missed until the age of 14 years old. The diagnosis was confirmed genetically with c.2273G > T, p.G758V mutation in exon 18 of the ABCC2 gene. The 2nd patient is a 7-day-old baby, the son of the 1st patient who gave birth to him at the age of 21 years old. He was diagnosed with DJS at the age of 2 weeks based on normal clinical and laboratory workup apart from direct hyperbilirubinemia. He had the same mutation as his mother in homozygous status. The husband was heterozygous for the same mutation. DJS is one of the often-missed differential diagnoses of neonatal cholestasis. It should be suspected in patients of infantile cholestasis, who have an, otherwise, normal physical examination, and laboratory investigations to avoid unnecessary lengthy, invasive, and expensive workups.

On a new generalized lindley distribution: Properties, estimation and applications.

In this study, an extension of the generalized Lindley distribution using the Marshall-Olkin method and its own sub-models is presented. This new model for modelling survival and lifetime data is flexible. Several statistical properties and characterizations of the subject distribution along with its reliability analysis are presented. Statistical inference for the new family such as the Maximum likelihood estimators and the asymptotic variance covariance matrix of the unknown parameters are discussed. A simulation study is considered to compare the efficiency of the different estimators based on mean square error criterion. Finally, a real data set is analyzed to show the flexibility of our proposed model compared with the fit attained by some other competitive distributions.

Ensemble of Deep Learning Based Clinical Decision Support System for Chronic Kidney Disease Diagnosis in Medical Internet of Things Environment.

Recently, Internet of Things (IoT) and cloud computing environments become commonly employed in several healthcare applications by the integration of monitoring things such as sensors and medical gadgets for observing remote patients. For availing of improved healthcare services, the huge count of data generated by IoT gadgets from the medicinal field can be investigated in the CC environment rather than relying on limited processing and storage resources. At the same time, earlier identification of chronic kidney disease (CKD) becomes essential to reduce the mortality rate significantly. This study develops an ensemble of deep learning based clinical decision support systems (EDL-CDSS) for CKD diagnosis in the IoT environment. The goal of the EDL-CDSS technique is to detect and classify different stages of CKD using the medical data collected by IoT devices and benchmark repositories. In addition, the EDL-CDSS technique involves the design of Adaptive Synthetic (ADASYN) technique for outlier detection process. Moreover, an ensemble of three models, namely, deep belief network (DBN), kernel extreme learning machine (KELM), and convolutional neural network with gated recurrent unit (CNN-GRU), are performed. Finally, quasi-oppositional butterfly optimization algorithm (QOBOA) is used for the hyperparameter tuning of the DBN and CNN-GRU models. A wide range of simulations was carried out and the outcomes are studied in terms of distinct measures. A brief outcomes analysis highlighted the supremacy of the EDL-CDSS technique on exiting approaches.

Modelling of Paclitaxel Conjugated with Carbon Nanotubes as an Antitumor Agent for Cancer Therapy.

Functionalized carbon nanoparticles (CNPs) show great promise for various drug delivery applications. These CNPs have distinct physical and chemical properties, such as low solubility, very high conductivity, and drug loading capability, and are thus important nanodevices for cancer therapy. Cancer is a highly challenging disease, because its therapy involves distinguishing diseased cells from healthy ones. This study aimed to determine the ability of CNPs conjugated with a chemotherapeutic agent to inhibit cancer cell growth. We developed two models to determine the effectiveness of paclitaxel (PTX) as an antitumor agent bonded to single-walled carbon nanotubes (SWCNTs) varying in radius (r). The models were used to mathematically evaluate the energy arising from the PTX-SWCNT interaction. The first model divided the PTX molecule into 15 subcomponents: 4 imidazole rings, 1 group of atoms forming a cylindrical nanotube, 6 methyl groups (small spheres represented as individual CH3 molecules), 3 carboxyl groups (medium-sized spheres represented as individual CO2 molecules), and 1 large sphere. In the second model, PTX was modeled as a spherical cage with a spheroidal structure. Next, we determined the minimum interaction energy between each subcomponent and an SWCNT of radius r, and then summed the interactions to determine the total energy (E). The numerical results indicated that SWCNTs can be loaded with PTX. We also determined the critical nanotube r required for acceptance of the PTX molecule. We believe that the findings of this research will encourage the development of new nanodevices capable of delivering larger amounts of drugs, genes, and proteins.

On estimation procedures of stress-strength reliability for Weibull distribution with application.

For the first time, ten frequentist estimation methods are considered on stress-strength reliability R = P(Y < X) when X and Y are two independent Weibull distributions with the same shape parameter. The start point to estimate the parameter R is the maximum likelihood method. Other than the maximum likelihood method, a nine frequentist estimation methods are used to estimate R, namely: least square, weighted least square, percentile, maximum product of spacing, minimum spacing absolute distance, minimum spacing absolute-log distance, method of Cramér-von Mises, Anderson-Darling and Right-tail Anderson-Darling. We also consider two parametric bootstrap confidence intervals of R. We compare the efficiency of the different proposed estimators by conducting an extensive Mont Carlo simulation study. The performance and the finite sample properties of the different estimators are compared in terms of relative biases and relative mean squared errors. The Mont Carlo simulation study revels that the percentile and maximum product of spacing methods are highly competitive with the other methods for small and large sample sizes. To show the applicability and the importance of the proposed estimators, we analyze one real data set.

Risk factors for hospital admission among COVID-19 patients with diabetes. A study from Saudi Arabia.

OBJECTIVES: To elucidate the risk factors for hospital admission among COVID-19 patients with type 2 diabetes mellitus (T2DM). METHODS: This retrospective study was conducted at the Prince Sultan Military Medical City, Riyadh, Saudi Arabia between May 2020 and July 2020. Out of 7,260 COVID-19 patients, 920 were identified as T2DM. After the exclusion process, 806 patients with T2DM were included in this analysis. Patients' data were extracted from electronic medical records. A logistic regression model was performed to estimate the risk factors of hospital admission. Results: Of the total of 806 COVID-19 patients with T2DM, 48% were admitted in the hospital, 52% were placed under home isolation. Older age between 70-79 years (OR [odd ratio] 2.56; p=0.017), ≥80 years (OR 6.48; p=0.001) were significantly more likely to be hospitalized compared to less than 40 years. Similarly, patients with higher HbA1c level of ≥9% compared to less than 7%; (OR 1.58; p=0.047); patients with comorbidities such as, hypertension (OR 1.43; p=0.048), cardiovascular disease (OR 1.56; p=0.033), cerebrovascular disease (OR 2.38; p=0.016), chronic pulmonary disease (OR 1.51; p=0.018), malignancy (OR 2.45; p=0.025), chronic kidney disease (CKD) IIIa, IIIb, IV (OR 2.37; p=0.008), CKD V (OR 5.07; p=0.007) were significantly more likely to be hospitalized. Likewise, insulin-treated (OR 1.46; p=0.03) were more likely to require hospital admission compared to non-insulin treated patients. CONCLUSION: Among COVID-19 patients with diabetes, higher age, high HbA1c level, and presence of other comorbidities were found to be significant risk factors for the hospital admission.

Trunk flexion during walking in people with knee osteoarthritis.

BACKGROUND: Over 50% of the body's mass is concentrated within the head, arms and trunk. Thus, small deviations in the orientation of the trunk, during normal walking, could influence the position of the centre of mass relative to the lower limb joint centres and impact on lower limb biomechanics. However, there are minimal data available on sagittal kinematics of the trunk in people with knee osteoarthritis (OA) during walking. RESEARCH QUESTION: Do people with knee OA have altered kinematic patterns of the trunk, pelvis or hip compared with healthy control participants during walking? METHODS: Statistical parametric mapping was used to compare sagittal and frontal plane kinematic patterns, during walking, between a healthy group and cohort of people with knee OA. RESULTS: Individuals with knee OA walked with a mean increase in trunk flexion of 2.6°. Although this difference was more pronounced during early stance, it was maintained across the whole of stance phase. There were no differences, between the groups, in sagittal plane pelvic or hip kinematics. There were also no differences in trunk, pelvic or hip kinematics in the frontal plane. SIGNIFICANCE: Most previous gait research investigating trunk motion in people with knee OA has focused on the frontal plane. However, our data suggest that an increase in sagittal trunk flexion may be a clinical hallmark of people with this disease. Altered trunk flexion could affect joint moments and muscle patterns and therefore our results motivate further research in this area.

Idiopathic hypoparathyroidism with extensive intracranial calcification in children: First report from Saudi Arabia.

RATIONALE: Pediatric idiopathic hypoparathyroidism with extensive intracranial calcifications outside the basal ganglia (BG) is extremely rare with less than 10 cases worldwide. PATIENT CONCERNS: An 11-year-old Saudi male child presented with tetany with otherwise normal neurological and other body system examination diagnoses severe hypocalcemia for differential diagnosis. INTERVENTIONS: Further investigations revealed hyperphosphatemia and undetectable serum intact parathyroid hormone. Brain computed tomography revealed BG and extensive brain calcifications. He has no dysmorphic features, vitiligo, mucocuataneous manifestations, or hair loss. He had normal hemoglobin, electroencephalogram, and skeletal survey, with negative autoantibodies to alpha and omega interferons and negative genetic testing for Glial Cell Missing 2 (GCM2) and calcium-sensing receptors (CaSRs) excluding known causes of hypoparathyroidism. OUTCOMES: This case presents a rare entity of idiopathic hypoparathyroidism with extensive intracranial calcification, not only in BG but also outside the extrapyramidal system with normal mentality, development, pubertal achievement, and neurological examination. To our knowledge, this is the first report from Saudi Arabia in pediatrics. LESSONS: Idiopathic hypoparathyroidism is a diagnosis of exclusion after ruling out all known causes of hypoparathyroidism. It is associated with BG calcifications, but extensive intracranial calcifications outside the BG are extremely rare.